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mattsecrestmattsecrest
authoredJan 9, 2024
Merge pull request #1 from mattsecrest/v0.0.1
v0.0.1
·
v0.0.5v0.0.1
2 parents 6c0aa68 + b9f9847 commit cf4e756

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‎.gitignore

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.DS_Store

‎DESCRIPTION

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Package: survivalCCW
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Title: An R Package for Clone Censor Weighting (CCW) Survival Analyses
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Version: 0.0.1
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Authors@R:
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person("Matthew", "Secrest", , "secrmatt@gmail.com", role = c("aut", "cre"),
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comment = c(ORCID = "0000-0002-0939-4902"))
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Description: This is a work-in-progress package that conducts clone censor weight analyses in R. Please use at your own risk. Consider filing a bug report or reaching out to [Matt](mailto:secrmatt@gmail.com) for questions/comments/suggestions.
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License: CC BY 4.0
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Encoding: UTF-8
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Roxygen: list(markdown = TRUE)
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RoxygenNote: 7.2.3
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Suggests:
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knitr,
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rmarkdown,
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boot,
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testthat (>= 3.0.0)
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VignetteBuilder: knitr
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Depends:
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R (>= 4.1.0)
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LazyData: true
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Config/testthat/edition: 3
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Imports:
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checkmate,
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survival

‎LICENSE.md

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This work is licensed under the Creative Commons
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Attribution-NonCommercial 4.0 International.
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https://creativecommons.org/licenses/by-nc/4.0/

‎NAMESPACE

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# Generated by roxygen2: do not edit by hand
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export(cast_clones_to_long)
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export(create_clones)
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export(generate_ccw)

‎NEWS.md

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# survivalCCW 0.0.1
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* Initial functionality and CRAN submission

‎R/cast_clones_to_long.R

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#' Cast one-row-per-clone data to long format
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#'
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#' @param df A data.frame with one row per clone as returned by [create_clones()]
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#'
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#' @return A data.frame with one row per patient per time period per clone.
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#'
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#' @export
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#'
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#' @examples
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#'
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#' # Load the toy dataset
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#' data(toy_df)
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#'
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#' # Create clones
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#' clones <- create_clones(toy_df,
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#' id = "id",
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#' event = "death",
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#' time_to_event = "fup_obs",
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#' exposure = "surgery",
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#' time_to_exposure = "timetosurgery",
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#' ced_window = 365.25/2)
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#'
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#' clones_long <- cast_clones_to_long(clones)
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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cast_clones_to_long <- function(df) {
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# Check inputs
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checkmate::assert_class(df, "ccw_clones")
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if (!all(c("outcome", "fup_outcome", "censor", "fup_censor", "clone") %in% names(df))) {
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stop("The input data.frame is missing at least one of the required columns: outcome, fup_outcome, censor, fup_censor, clone. Did you remove this?")
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}
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if (!all(c("id", "event", "time_to_event", "exposure", "time_to_exposure", "ced_window") %in% names(attributes(df)))) {
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stop("The input data.frame is missing at least one attribute: id, event, time_to_event, exposure, time_to_exposure, ced_window. Did you remove these or try to make a custom data.frame?")
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}
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id <- attributes(df)$id
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event <- attributes(df)$event
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exposure <- attributes(df)$exposure
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time_to_event <- attributes(df)$time_to_event
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time_to_exposure <- attributes(df)$time_to_exposure
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ced_window <- attributes(df)$ced_window
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# Now convert to long
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event_times <- sort(unique(c(df[,time_to_event], df[,time_to_exposure], ced_window)))
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event_times_df <- data.frame(
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t_event = event_times,
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time_id = 1:NROW(event_times)
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)
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# Exposed
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## Outcome
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df_1 <- df[df[, "clone"] == 1L, ]
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df_1$t_start <- 0.
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df_1_long_outcome <- survival::survSplit(
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df_1,
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cut = event_times,
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end = "fup_outcome",
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start = "t_start",
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event = "outcome"
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)
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df_1_long_outcome <- df_1_long_outcome[order(df_1_long_outcome[, id], df_1_long_outcome[, "fup_outcome"]), ]
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## Censor
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df_1_long_censor <- survival::survSplit(
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df_1,
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cut = event_times,
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end = "fup_outcome", # Not a typo, we want to expand at the outcome time still
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start = "t_start",
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event = "censor"
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)
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df_1_long_censor <- df_1_long_censor[order(df_1_long_censor[, id], df_1_long_censor[, "fup_outcome"]), ]
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## Replace censoring variable in df_1_long_outcome
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df_1_long_outcome$censor <- df_1_long_censor$censor
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df_1_long_outcome$t_stop <- df_1_long_outcome$fup_outcome
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df_1_long <- merge(
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x = df_1_long_outcome,
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y = event_times_df,
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by.x = "t_start",
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by.y = "t_event",
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all.x = TRUE)
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df_1_long <- df_1_long[order(df_1_long[,id], df_1_long[, "fup_outcome"]), ]
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df_1_long$time_id[is.na(df_1_long$time_id)] <- 0
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rownames(df_1_long) <- NULL
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# Unexposed
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## Outcome
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df_0 <- df[df[, "clone"] == 0L, ]
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df_0$t_start <- 0.
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df_0_long_outcome <- survival::survSplit(
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df_0,
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cut = event_times,
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end = "fup_outcome",
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start = "t_start",
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event = "outcome"
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)
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df_0_long_outcome <- df_0_long_outcome[order(df_0_long_outcome[, id], df_0_long_outcome[, "fup_outcome"]), ]
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## Censor
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df_0_long_censor <- survival::survSplit(
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df_0,
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cut = event_times,
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end = "fup_outcome", # Not a typo, we want to expand at the outcome time still
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start = "t_start",
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event = "censor"
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)
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df_0_long_censor <- df_0_long_censor[order(df_0_long_censor[, id], df_0_long_censor[, "fup_outcome"]), ]
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## Replace censoring variable in df_1_long_outcome
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df_0_long_outcome$censor <- df_0_long_censor$censor
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df_0_long_outcome$t_stop <- df_0_long_outcome$fup_outcome
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df_0_long <- merge(
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x = df_0_long_outcome,
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y = event_times_df,
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by.x = "t_start",
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by.y = "t_event",
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all.x = TRUE)
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df_0_long <- df_0_long[order(df_0_long[,id], df_0_long[, "fup_outcome"]), ]
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df_0_long$time_id[is.na(df_0_long$time_id)] <- 0
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rownames(df_0_long) <- NULL
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# Combine
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df_long <- rbind(
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df_1_long,
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df_0_long
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)
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df_long <- merge(
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x = df_long,
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y = event_times_df,
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by = "time_id",
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all.x = TRUE
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)
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df_long <- df_long[order(df_long[,id], df_long[, "clone"], df_long[, "fup_outcome"]), ]
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# Add attributes and return
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class(df_long) <- c("ccw_clones_long", class(df_long))
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attributes(df_long)$id <- id
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attributes(df_long)$event <- event
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attributes(df_long)$time_to_event <- time_to_event
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attributes(df_long)$exposure <- exposure
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attributes(df_long)$time_to_exposure <- time_to_exposure
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attributes(df_long)$ced_window <- ced_window
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attributes(df_long)$event_times_df <- event_times_df
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# Remove rownames
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rownames(df_long) <- NULL
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return(df_long)
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}

‎R/create_clones.R

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#' Create clones for CCW analysis
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#'
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#' Pass a one-row-per-patient data.frame and get back a data.frame with one row per clone.
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#'
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#' @param df A data.frame with one row per patient.
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#' @param id The name of the column in `df` that contains the patient identifier.
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#' @param event The name of the column in `df` that contains the event of interest.
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#' @param time_to_event The name of the column in `df` that contains the time to event.
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#' @param exposure The name of the column in `df` that contains the exposure.
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#' @param time_to_exposure The name of the column in `df` that contains the time to exposure.
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#' @param ced_window The date at which the clinical eligibility window closes. Can be left empty, in which case the clinical eligibility window is assumed to be part of
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#' `exposure` and `time_to_exposure`
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#'
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#' @return A data.frame with one row per clone.
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#'
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#' @export
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#'
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#' @examples
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#'
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#' # Load the toy dataset
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#' data(toy_df)
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#'
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#' # Create clones
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#' clones <- create_clones(toy_df,
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#' id = "id",
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#' event = "death",
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#' time_to_event = "fup_obs",
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#' exposure = "surgery",
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#' time_to_exposure = "timetosurgery",
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#' ced_window = 365.25/2)
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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create_clones <- function(
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df,
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id,
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event,
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time_to_event,
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exposure,
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time_to_exposure,
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ced_window = NULL
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) {
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# Check inputs
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valid_inputs <- create_clones_check_inputs(df = df,
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id = id,
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event = event,
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time_to_event = time_to_event,
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exposure = exposure,
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time_to_exposure = time_to_exposure,
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ced_window = ced_window)
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if (!valid_inputs) stop("something went wrong")
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# Update exposure and time-to-exposure based on CED window
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n_pts_to_update <- sum(!is.na(df[, time_to_exposure]) & df[, time_to_exposure] > ced_window)
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if (n_pts_to_update > 0) {
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message(paste0("Updating ", n_pts_to_update, " patients' exposure and time-to-exposure based on CED window"))
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ced_window_na_type <- ifelse(is.integer(ced_window), NA_integer_, NA_real_)
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df[, exposure] <- ifelse(!is.na(df[, time_to_exposure]) & df[, time_to_exposure] > ced_window, 0L, df[, exposure])
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df[, time_to_exposure] <- ifelse(!is.na(df[, time_to_exposure]) & df[, time_to_exposure] > ced_window, ced_window_na_type, df[, time_to_exposure])
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}
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# Create clones
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df_0 <- df_1 <- df
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df_0$outcome <- df_1$outcome <- rep(NA_integer_, NROW(df))
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df_0$fup_outcome <- df_1$fup_outcome <- rep(NA_real_, NROW(df))
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df_0$censor <- df_1$censor <- rep(NA_integer_, NROW(df))
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df_0$fup_censor <- df_1$fup_censor <- rep(NA_real_, NROW(df))
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df_0$clone <- 0L
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df_1$clone <- 1L
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# OUTCOMES
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## EXPOSED
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### Truly exposed --> keep outcomes
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df_1[df_1[, exposure] == 1L, "outcome"] <- df_1[df_1[, exposure] == 1L, event]
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df_1[df_1[, exposure] == 1L, "fup_outcome"] <- df_1[df_1[, exposure] == 1L, time_to_event]
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### Truly not exposed, follow-up ends before CED --> keep outcomes
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, "outcome"] <- df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, event]
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, "fup_outcome"] <- df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, time_to_event]
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### Truly not exposed, follow-up ends after CED --> censor
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] > ced_window, "outcome"] <- 0L
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] > ced_window, "fup_outcome"] <- ced_window
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## UNEXPOSED
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### Truly unexposed --> keep outcomes
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df_0[df_0[, exposure] == 0L, "outcome"] <- df_0[df_0[, exposure] == 0L, event]
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df_0[df_0[, exposure] == 0L, "fup_outcome"] <- df_0[df_0[, exposure] == 0L, time_to_event]
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### Truly not exposed --> censor at exposure
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df_0[df_0[, exposure] == 1L, "outcome"] <- 0L
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df_0[df_0[, exposure] == 1L, "fup_outcome"] <- df_0[df_0[, exposure] == 1L, time_to_exposure]
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# CENSORING
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## EXPOSED
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### Truly exposed --> Do not censor. Risk of censoring ends at exposure date
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df_1[df_1[, exposure] == 1L, "censor"] <- 0L
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df_1[df_1[, exposure] == 1L, "fup_censor"] <- df_1[df_1[, exposure] == 1L, time_to_exposure]
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### Truly not exposed, true censorship before/on CED --> Do not censor. Risk of censoring ends at event date
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, "censor"] <- 0L
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, "fup_censor"] <- df_1[df_1[, exposure] == 0L & df_1[, time_to_event] <= ced_window, time_to_event]
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### Truly not exposed, true censorship on/after CED --> Censor at CED.
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] > ced_window, "censor"] <- 1L
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df_1[df_1[, exposure] == 0L & df_1[, time_to_event] > ced_window, "fup_censor"] <- ced_window
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## UNEXPOSED
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### Truly exposed --> Censored at time of exposure.
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df_0[df_0[, exposure] == 1L, "censor"] <- 1L
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df_0[df_0[, exposure] == 1L, "fup_censor"] <- df_0[df_0[, exposure] == 1L, time_to_exposure]
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### Truly not exposed, true censorship before/on CED --> Do not censor. Risk of censoring ends at event date
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df_0[df_0[, exposure] == 0L & df_0[, time_to_event] <= ced_window, "censor"] <- 0L
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df_0[df_0[, exposure] == 0L & df_0[, time_to_event] <= ced_window, "fup_censor"] <- df_0[df_0[, exposure] == 0L & df_0[, time_to_event] <= ced_window, time_to_event]
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### Truly not exposed, true censorship on/after CED --> Do not censor. Risk of censoring ends at CED.
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df_0[df_0[, exposure] == 0L & df_0[, time_to_event] > ced_window, "censor"] <- 0L
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df_0[df_0[, exposure] == 0L & df_0[, time_to_event] > ced_window, "fup_censor"] <- ced_window
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# Combine and return
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df_clones <- rbind(df_1, df_0)
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df_clones <- df_clones[order(df_clones[, id], df_clones[, "clone"]), ]
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# Add class
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class(df_clones) <- c("ccw_clones", class(df_clones))
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# Pass attributes
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attributes(df_clones)$id <- id
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attributes(df_clones)$event <- event
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attributes(df_clones)$time_to_event <- time_to_event
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attributes(df_clones)$exposure <- exposure
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attributes(df_clones)$time_to_exposure <- time_to_exposure
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attributes(df_clones)$ced_window <- ced_window
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# Remove rownames
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rownames(df_clones) <- NULL
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# Return
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return(df_clones)
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}

‎R/create_clones_check_inputs.R

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#' Check inputs to create clones
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#'
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#' @param df A data.frame with one row per patient.
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#' @param id The name of the column in `df` that contains the patient identifier.
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#' @param event The name of the column in `df` that contains the event of interest.
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#' @param time_to_event The name of the column in `df` that contains the time to event.
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#' @param exposure The name of the column in `df` that contains the exposure.
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#' @param time_to_exposure The name of the column in `df` that contains the time to exposure.
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#' @param ced_window The date at which the clinical eligibility window closes.
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#' `exposure` and `time_to_exposure`
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#'
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#' @return TRUE if inputs are valid else false
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create_clones_check_inputs <- function(
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df,
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id,
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event,
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time_to_event,
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exposure,
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time_to_exposure,
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ced_window
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) {
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inputs_good <- FALSE
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# Check all input types
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checkmate::assert_class(df, "data.frame")
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checkmate::assert_class(id, "character")
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checkmate::assert_class(event, "character")
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checkmate::assert_class(time_to_event, "character")
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checkmate::assert_class(exposure, "character")
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checkmate::assert_class(time_to_exposure, "character")
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checkmate::assert_class(ced_window, "numeric")
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# Check that all columns are in data
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checkmate::assert_subset(c(id, event, time_to_event, exposure, time_to_exposure), names(df))
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# Check that there are no missing data in the study columns (except time to exposure)
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cc_sum <- sum(stats::complete.cases(df[, c(id, event, time_to_event, exposure)]))
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if (cc_sum != NROW(df)) {
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stop("There are missing data in the study columns")
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}
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# Check time to exposure is missing just for when exposure is 0/F
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if (any(!is.na(df[df[, exposure] == 0L, time_to_exposure]))) {
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stop("Time to exposure should only be for patients who received the exposure at some time")
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}
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if (any(is.na(df[df[, exposure] == 1L, time_to_exposure]))) {
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stop("Time to exposure should be complete for patients who have exposure = 1")
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}
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# Check exposure and event are just 0/1 or T/F
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if (any(df[, exposure] != 0L & df[, exposure] != 1L)) {
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stop("Exposure should be 0/1 or T/F")
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}
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if (any(df[, event] != 0L & df[, event] != 1L)) {
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stop("Event should be 0/1 or T/F")
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}
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# Make sure the user did not pass the same column name twice
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if (NROW(unique(c(id, event, time_to_event, exposure, time_to_exposure))) != NROW(c(id, event, time_to_event, exposure, time_to_exposure))) {
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stop("You passed the same column name twice")
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}
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# Check that the respective columns are numeric
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checkmate::assert_numeric(df[, time_to_event])
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checkmate::assert_numeric(df[, time_to_exposure])
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checkmate::assert_true(class(df[,event]) %in% c("integer", "logical"))
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checkmate::assert_true(class(df[,exposure]) %in% c("integer", "logical"))
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checkmate::assert_true(class(df[,id]) %in% c("integer", "numeric", "character"))
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# Some protected names
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protected_names <- c("clone", "outcome", "fup_outcome", "censor", "fup_censor",
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"t_start", "t_stop", "time_id", "t_event", "weight_cox",
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"p_uncens", "hazard", "lp")
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for (name in protected_names) {
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if (name %in% names(df)) {
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stop("'", name, "' is a protected collumn name and will be used by the function. Please rename this column")
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}
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}
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# Check no outcomes are before exposure dates
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if (any(df[!is.na(df[,time_to_exposure]), time_to_event] < df[!is.na(df[,time_to_exposure]), time_to_exposure])) {
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stop("There are outcomes before exposure dates")
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}
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inputs_good <- TRUE
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return(inputs_good)
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}
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‎R/data.R

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#' Toy dataset from Maringe et al. (2020)
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#'
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#' A toy dataset provided in Maringe et al. (2020) to demonstrate the clone-censor weight approach.
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#'
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#' @format ## `toy_df`
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#' A data frame with 200 rows and 12 columns:
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#' \describe{
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#' \item{id}{patient identifier}
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#' \item{fup_obs}{observed follow-up time (time to death or 1 year if censored alive)}
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#' \item{death}{observed event of interest (all-cause death) 1: dead, 0:alive}
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#' \item{timetosurgery}{time to surgery (NA if no surgery)}
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#' \item{age}{age at diagnosis}
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#' \item{sex}{patient's sex}
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#' \item{perf}{performance status at diagnosis}
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#' \item{stage}{stage at diagnosis}
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#' \item{deprivation}{deprivation score}
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#' \item{charlson}{Charlson's comorbidity index}
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#' \item{emergency}{route to diagnosis}
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#' }
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"toy_df"
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#' Testing data for `create_clones`
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#'
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#' The clones dataset from Maringe et al. (2020) named `tab`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"tab"
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#' Testing data for `cast_clones_to_long`
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#'
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#' The long clones dataset from Maringe et al. (2020) named `data_final`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"data_final"
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#' Testing data for `cast_clones_to_long`
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#'
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#' The long clones dataset from Maringe et al. (2020) named `data_final`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"data_final"
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#' Testing data for `generate_ccw_calc_weights`, surgery recipients
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#'
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#' The long clones dataset with weights for surgery recipients from Maringe et al. (2020) named `data_long`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"data_long"
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#' Testing data for `generate_ccw_calc_weights`, no surgery recipients
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#'
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#' The long clones dataset with weights for no surgery recipients from Maringe et al. (2020) named `data_long_2`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"data_long_2"
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#' Testing data for `generate_ccw`
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#'
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#' The long clones dataset with weights for all recipients from Maringe et al. (2020) named `data_long_cox`
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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#' @source <https://doi.org/10.1093/ije/dyaa057>
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"data_long_cox"

‎R/generate_ccw.R

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#' Generate clone censor weights (CCW) on the long data.frame
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#'
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#' Currently, the only way to generate weights is via multivariable Cox, as described in Maringe et al. 2020
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#'
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#' @param df A data.frame with one row per clone per observation period as returned by [cast_clones_to_long()]
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#' @param predvars The variables that will be used to derive weights (subset of those in your data.frame originally). At least one covariate must be used.
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#'
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#' @return The same data.frame with weights added.
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#'
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#' @export
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#'
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#' @examples
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#'
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#' # Load the toy dataset
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#' data(toy_df)
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#'
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#' # Create clones
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#' clones <- create_clones(toy_df,
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#' id = "id",
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#' event = "death",
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#' time_to_event = "fup_obs",
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#' exposure = "surgery",
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#' time_to_exposure = "timetosurgery",
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#' ced_window = 365.25/2)
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#'
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#' clones_long <- cast_clones_to_long(clones)
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#' clones_long_w <- generate_ccw(clones_long, predvars = c("age"))
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#'
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#' @references Maringe, Camille, et al. "Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data." International journal of epidemiology 49.5 (2020): 1719-1729.
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generate_ccw <- function(df, predvars) {
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# Check inputs
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checkmate::assert_class(df, "ccw_clones_long")
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if (!all(c("outcome", "fup_outcome", "censor", "fup_censor", "clone", "t_start", "t_stop", "time_id", "t_event") %in% names(df))) {
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stop("The input data.frame is missing at least one of the required columns: outcome, fup_outcome, censor, fup_censor, clone, t_start, t_stop, time_id, t_event. Did you remove this?")
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}
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if (!all(c("id", "event", "time_to_event", "exposure", "time_to_exposure", "ced_window") %in% names(attributes(df)))) {
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stop("The input data.frame is missing at least one attribute: id, event, time_to_event, exposure, time_to_exposure, ced_window. Did you remove these or try to make a custom data.frame?")
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}
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id <- attributes(df)$id
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event <- attributes(df)$event
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exposure <- attributes(df)$exposure
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time_to_event <- attributes(df)$time_to_event
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time_to_exposure <- attributes(df)$time_to_exposure
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ced_window <- attributes(df)$ced_window
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event_times_df <- attributes(df)$event_times_df
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# Check predvars to make sure the columns are there
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if (!all(predvars %in% names(df))) {
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stop("At least one of these predvars columns is not on the data.frame: ", paste(predvars, collapse = ", "), ".")
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}
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# Make sure predvars is not NULL
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if (is.null(predvars)) {
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stop("predvars cannot be NULL. Please specify at least one variable to use for weights.")
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}
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# Make sure no predvars are character/factor
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if (any(sapply(df[, predvars], is.character) | sapply(df[, predvars], is.factor))) {
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stop("At least one of the predvars columns is character/factor. In this early version of `survivalCCW`, only numeric variables are considered. Please make dummy vars on your own! :)")
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}
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# Create weights
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df_1 <- generate_ccw_calc_weights(df[df$clone == 1L, ], event_times_df, predvars)
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df_0 <- generate_ccw_calc_weights(df[df$clone == 0L, ], event_times_df, predvars)
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# Combine
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df <- rbind(df_0, df_1)
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# Check that all clones have weights
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if (any(is.na(df$weight_cox))) {
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stop("At least one clone is missing a weight. Please file a bug fix.")
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}
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# Update class
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class(df) <- c("ccw_clones_long_weights", class(df))
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# Remove rownames
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rownames(df) <- NULL
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return(df)
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}

‎R/generate_ccw_calc_weights.R

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#' Calculate weights for each arm of a long data.frame
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#'
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#' @param df the data.frame for a single arm
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#' @param event_times_df the event times data.frame
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#' @param predvars the baseline variables for adjustment
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#'
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#' @return a data.frame with weight columns included
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generate_ccw_calc_weights <- function(df, event_times_df, predvars) {
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model_fmla <- stats::as.formula(
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paste0(
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"survival::Surv(t_start, t_stop, censor) ~ ",
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paste(predvars, collapse = " + ")
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)
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)
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cens_model <- survival::coxph(model_fmla, data = df, ties = "efron")
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#@TODO allow factors and carry forward through previous functions
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# ref_df <- setNames(data.frame(matrix(0, nrow = 1, ncol = length(predvars))), predvars)
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df$lp <- as.matrix(df[, predvars]) %*% stats::coef(cens_model)
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baseline_hazard <- data.frame(
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survival::basehaz(cens_model, centered = FALSE)
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)
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names(baseline_hazard) <- c("hazard", "t")
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dat_base_times <- unique(
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merge(
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x = baseline_hazard,
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y = event_times_df,
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by.x = "t",
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by.y = "t_event",
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all.x = TRUE
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)
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)
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df <- merge(
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x = df,
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y = dat_base_times,
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by = "time_id",
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all.x = TRUE
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)
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df <- df[order(df$id,df$fup_outcome),]
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df$hazard <- ifelse(is.na(df$hazard), 0, df$hazard)
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df$p_uncens <- exp(-(df$hazard) * exp(df$lp))
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df$weight_cox <- 1 / df$p_uncens
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df$weight_cox[df$time_id == 0] <- 1
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row.names(df) <- NULL
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return(df)
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}

‎README.md

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# survivalCCW
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This is a work-in-progress package that conducts clone censor weight analyses in R. Please use at your own risk. Consider filing a bug report or reaching out to [Matt](mailto:secrmatt@gmail.com) for questions/comments/suggestions.

‎cran-comments.md

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## R CMD check results
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0 errors | 0 warnings | 1 note
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* This is a new release.

‎data/data_final.rda

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‎data/data_long.rda

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‎data/data_long_2.rda

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‎data/data_long_cox.rda

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‎data/tab.rda

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‎man/cast_clones_to_long.Rd

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‎man/create_clones.Rd

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‎man/create_clones_check_inputs.Rd

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‎man/data_final.Rd

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‎man/data_long.Rd

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‎man/data_long_2.Rd

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‎man/data_long_cox.Rd

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‎man/generate_ccw.Rd

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‎man/generate_ccw_calc_weights.Rd

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‎man/tab.Rd

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‎man/toy_df.Rd

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‎survivalCCW.Rproj

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Version: 1.0
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RestoreWorkspace: No
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SaveWorkspace: No
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AlwaysSaveHistory: Default
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EnableCodeIndexing: Yes
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UseSpacesForTab: Yes
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NumSpacesForTab: 4
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Encoding: UTF-8
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RnwWeave: Sweave
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LaTeX: pdfLaTeX
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AutoAppendNewline: Yes
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StripTrailingWhitespace: Yes
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LineEndingConversion: Posix
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BuildType: Package
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PackageUseDevtools: Yes
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PackageInstallArgs: --no-multiarch --with-keep.source
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PackageRoxygenize: rd,collate,namespace

‎tests/testthat.R

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# This file is part of the standard setup for testthat.
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# It is recommended that you do not modify it.
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#
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# Where should you do additional test configuration?
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# Learn more about the roles of various files in:
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# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
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# * https://testthat.r-lib.org/articles/special-files.html
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9+
library(testthat)
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library(survivalCCW)
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12+
test_check("survivalCCW")

‎tests/testthat/test-cast_clones_to_long.R

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test_that("casting clones requires ccw_clones class", {
2+
3+
df <- data.frame(
4+
id = c(1, 2, 3, 4),
5+
event = c(0L, 1L, 0L, 1L),
6+
time_to_event = c(100, 200, 100, 200),
7+
exposure = c(0L, 1L, 0L, 1L),
8+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
9+
)
10+
11+
expect_error(
12+
cast_clones_to_long(df)
13+
)
14+
15+
ccw_df <- create_clones(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
16+
17+
attributes(ccw_df)$id <- NULL
18+
19+
expect_error(
20+
cast_clones_to_long(ccw_df)
21+
)
22+
23+
ccw_df$outcome <- NULL
24+
25+
expect_error(
26+
cast_clones_to_long(ccw_df)
27+
)
28+
})
29+
30+
test_that("long format was created correctly", {
31+
32+
df_long <- toy_df |>
33+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 182.62) |>
34+
cast_clones_to_long()
35+
36+
expect_true(TRUE)
37+
#@TODO more test cases
38+
})
39+
40+
41+
test_that("Compare results to Maringe", {
42+
43+
df <- toy_df |>
44+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 182.62) |>
45+
cast_clones_to_long()
46+
47+
df <- df[order(df$id, df$clone, df$time_id),]
48+
row.names(df) <- NULL
49+
50+
data_final <- data_final[order(data_final$id, data_final$clone, data_final$time_id),]
51+
row.names(data_final) <- NULL
52+
53+
for (col in names(df)) {
54+
expect_equal(
55+
df[[col]],
56+
data_final[[col]],
57+
tolerance = 1e-6
58+
)
59+
}
60+
61+
})

‎tests/testthat/test-check_inputs_create_clones.R

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test_that("input types correct", {
2+
3+
df <- data.frame(
4+
id = c(1, 2, 3, 4),
5+
event = c(0L, 1L, 0L, 1L),
6+
time_to_event = c(100, 200, 100, 200),
7+
exposure = c(0L, 1L, 0L, 1L),
8+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
9+
)
10+
11+
expect_error(
12+
create_clones_check_inputs(df, id = id, event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
13+
)
14+
15+
expect_error(
16+
create_clones_check_inputs(df, id = "id", event = df$event, time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
17+
)
18+
19+
expect_error(
20+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = 2.0, exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
21+
)
22+
23+
expect_error(
24+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = "ced_window")
25+
)
26+
27+
})
28+
29+
test_that("columns are in data", {
30+
31+
df <- data.frame(
32+
id = c(1, 2, 3, 4),
33+
event = c(0L, 1L, 0L, 1L),
34+
time_to_event = c(100, 200, 100, 200),
35+
exposure = c(0L, 1L, 0L, 1L),
36+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
37+
)
38+
39+
expect_error(
40+
create_clones_check_inputs(df, id = "hamburger", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
41+
)
42+
43+
expect_error(
44+
create_clones_check_inputs(df, id = "id", event = "sausage", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
45+
)
46+
47+
expect_error(
48+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "pumpkin", time_to_exposure = "time_to_exposure", ced_window = 200)
49+
)
50+
51+
})
52+
53+
test_that("no missing data exists in these columns (other than time to exposure)", {
54+
55+
df <- data.frame(
56+
id = c(1, 2, 3, 4),
57+
event = c(0L, 1L, NA_integer_, 1L),
58+
time_to_event = c(100, 200, 100, 200),
59+
exposure = c(0L, 1L, 0L, 1L),
60+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
61+
)
62+
63+
expect_error(
64+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
65+
)
66+
67+
})
68+
69+
test_that("the same column name is not passed >1 time", {
70+
71+
df <- data.frame(
72+
id = c(1, 2, 3, 4),
73+
event = c(0L, 1L, 0L, 1L),
74+
time_to_event = c(100, 200, 100, 200),
75+
exposure = c(0L, 1L, 0L, 1L),
76+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
77+
)
78+
79+
expect_error(
80+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
81+
)
82+
83+
})
84+
85+
test_that("the respective columns have the right classes", {
86+
87+
df <- data.frame(
88+
id = c(1, 2, 3, 4),
89+
event = c('ham', 'is', 'good', 'food'),
90+
time_to_event = c(100, 200, 100, 200),
91+
exposure = c(0L, 1L, 0L, 1L),
92+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
93+
)
94+
95+
expect_error(
96+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
97+
)
98+
99+
df <- data.frame(
100+
id = c(1, 2, 3, 4),
101+
event = c(1.0, 1.0, 0.0, 1.0),
102+
time_to_event = c(100, 200, 100, 200),
103+
exposure = c(0L, 1L, 0L, 1L),
104+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
105+
)
106+
107+
expect_error(
108+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
109+
)
110+
111+
df <- data.frame(
112+
id = c(1, 2, 3, 4),
113+
event = c(0L, 1L, 0L, 1L),
114+
time_to_event = c(100, 200, 100, 200),
115+
exposure = c(1.0, 1.0, 0.0, 1.0),
116+
time_to_exposure = c(2.2, 2.3, NA_real_, 3.3)
117+
)
118+
119+
expect_error(
120+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
121+
)
122+
123+
124+
df <- data.frame(
125+
id = c(1, 2, 3, 4),
126+
event = c(0L, 1L, 0L, 1L),
127+
time_to_event = c('ham', 'is', 'great', 'food'),
128+
exposure = c(0L, 1L, 0L, 1L),
129+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
130+
)
131+
132+
expect_error(
133+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
134+
)
135+
136+
df <- data.frame(
137+
id = c(1, 2, 3, 4),
138+
event = c(0L, 1L, 0L, 1L),
139+
time_to_event = c(100, 200, 100, 200),
140+
exposure = c(0L, 1L, 0L, 1L),
141+
time_to_exposure = factor(c('ham', 'is', 'great', 'food'))
142+
)
143+
144+
expect_error(
145+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
146+
)
147+
148+
})
149+
150+
test_that("for all pts with an exposure, time to exposure is complete", {
151+
152+
df <- data.frame(
153+
id = c(1, 2, 3, 4),
154+
event = c(0L, 1L, 0L, 1L),
155+
time_to_event = c(100, 200, 100, 200),
156+
exposure = c(T, T, F, T),
157+
time_to_exposure = c(1.1, NA_real_, NA_real_, 3.3)
158+
)
159+
160+
expect_error(
161+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
162+
)
163+
164+
df <- data.frame(
165+
id = c(1, 2, 3, 4),
166+
event = c(0L, 1L, 0L, 1L),
167+
time_to_event = c(100, 200, 100, 200),
168+
exposure = c(T, T, F, T),
169+
time_to_exposure = c(1.1, 3.3, 9.4, 3.3)
170+
)
171+
172+
expect_error(
173+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
174+
)
175+
176+
})
177+
178+
test_that("protected column names are blocked", {
179+
180+
df <- data.frame(
181+
id = c(1, 2, 3, 4),
182+
clone = c(0L, 1L, 0L, 1L),
183+
time_to_event = c(100, 200, 100, 200),
184+
exposure = c(T, T, F, T),
185+
time_to_exposure = c(1.1, 3.3, NA_real_, 3.3)
186+
)
187+
188+
expect_error(
189+
create_clones_check_inputs(df, id = "id", event = "clone", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
190+
)
191+
192+
df <- data.frame(
193+
id = c(1, 2, 3, 4),
194+
event = c(0L, 1L, 0L, 1L),
195+
time_to_event = c(100, 200, 100, 200),
196+
censor = c(T, T, F, T),
197+
time_to_exposure = c(1.1, 3.3, NA_real_, 3.3)
198+
)
199+
200+
expect_error(
201+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "censor", time_to_exposure = "time_to_exposure", ced_window = 200)
202+
)
203+
204+
})
205+
206+
test_that("exposure and event are just 0/1 or T/F", {
207+
208+
df <- data.frame(
209+
id = c(1, 2, 3, 4),
210+
event = c(0L, 1L, 2L, 3L),
211+
time_to_event = c(100, 200, 100, 200),
212+
exposure = c(1L, 1L, 0L, 1L),
213+
time_to_exposure = c(1.1, 3.3, NA_real_, 3.3)
214+
)
215+
216+
expect_error(
217+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
218+
)
219+
220+
df <- data.frame(
221+
id = c(1, 2, 3, 4),
222+
event = c(0L, 0L, 0L, 0L),
223+
time_to_event = c(100, 200, 100, 200),
224+
exposure = c(0L, 1L, 2L, 3L),
225+
time_to_exposure = c(NA_real_, 2.2, 3.3, 3.3)
226+
)
227+
228+
expect_error(
229+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 200)
230+
)
231+
232+
})
233+
234+
test_that("No outcomes before exposure dates", {
235+
236+
df <- data.frame(
237+
id = c(1, 2, 3, 4),
238+
event = c(1L, 1L, 0L, 0L),
239+
time_to_event = c(1, 2, 1, 2),
240+
exposure = c(1L, 1L, 0L, 1L),
241+
time_to_exposure = c(1.1, 3.3, NA_real_, 3.3)
242+
)
243+
244+
expect_error(
245+
create_clones_check_inputs(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 1.0)
246+
)
247+
248+
})

‎tests/testthat/test-create_clones.R

Lines changed: 257 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,257 @@
1+
test_that("CED trims time to exposure appropriately", {
2+
3+
df <- data.frame(
4+
id = c(1, 2, 3, 4),
5+
event = c(0L, 1L, 0L, 1L),
6+
time_to_event = c(200L, 250L, 9900L, 100L),
7+
exposure = c(0L, 1L, 0L, 1L),
8+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
9+
)
10+
11+
expect_message(
12+
ccw_df <- create_clones(df, id = "id", event = "event", time_to_event = "time_to_event", exposure = "exposure", time_to_exposure = "time_to_exposure", ced_window = 2.5),
13+
"Updating 1 patients' exposure and time-to-exposure based on CED window"
14+
)
15+
16+
expect_equal(0L, unique(ccw_df[ccw_df$id==4, "exposure"]))
17+
18+
})
19+
20+
21+
test_that("Spot check that outcomes are correctly assigned", {
22+
23+
df <- data.frame(
24+
id = 1:6,
25+
event = c(1L, 1L, 1L, 1L, 1L, 1L),
26+
time_to_event = c(10, 100, 100, 10, 100, 100),
27+
exposure = c(rep(0L, 3), rep(1L, 3)),
28+
time_to_exposure = c(rep(NA_real_, 3), 2, 8, 12)
29+
)
30+
31+
ccw_df <- create_clones(df,
32+
id = "id",
33+
event = "event",
34+
time_to_event = "time_to_event",
35+
exposure = "exposure",
36+
time_to_exposure = "time_to_exposure",
37+
ced_window = 20)
38+
39+
# Exposed clones
40+
expect_equal(
41+
ccw_df[ccw_df$id==4 & ccw_df$clone == 1, "outcome"],
42+
1L
43+
)
44+
45+
expect_equal(
46+
ccw_df[ccw_df$id==4 & ccw_df$clone == 1, "fup_outcome"],
47+
10
48+
)
49+
50+
expect_equal(
51+
ccw_df[ccw_df$id==1 & ccw_df$clone == 1, "outcome"],
52+
1L
53+
)
54+
55+
expect_equal(
56+
ccw_df[ccw_df$id==1 & ccw_df$clone == 1, "fup_outcome"],
57+
10
58+
)
59+
60+
expect_equal(
61+
ccw_df[ccw_df$id==2 & ccw_df$clone == 1, "outcome"],
62+
0L
63+
)
64+
65+
expect_equal(
66+
ccw_df[ccw_df$id==2 & ccw_df$clone == 1, "fup_outcome"],
67+
20
68+
)
69+
70+
# Unexposed clones
71+
# Exposed clones
72+
expect_equal(
73+
ccw_df[ccw_df$id==4 & ccw_df$clone == 0, "outcome"],
74+
0L
75+
)
76+
77+
expect_equal(
78+
ccw_df[ccw_df$id==4 & ccw_df$clone == 0, "fup_outcome"],
79+
2
80+
)
81+
82+
expect_equal(
83+
ccw_df[ccw_df$id==1 & ccw_df$clone == 0, "outcome"],
84+
1L
85+
)
86+
87+
expect_equal(
88+
ccw_df[ccw_df$id==1 & ccw_df$clone == 0, "fup_outcome"],
89+
10
90+
)
91+
92+
expect_equal(
93+
ccw_df[ccw_df$id==2 & ccw_df$clone == 0, "outcome"],
94+
1L
95+
)
96+
97+
expect_equal(
98+
ccw_df[ccw_df$id==2 & ccw_df$clone == 0, "fup_outcome"],
99+
100
100+
)
101+
102+
})
103+
104+
105+
test_that("Spot check that censoring statuses are correctly assigned",{
106+
107+
df <- data.frame(
108+
id = 1:6,
109+
event = c(1L, 1L, 1L, 1L, 1L, 1L),
110+
time_to_event = c(10, 100, 100, 10, 100, 100),
111+
exposure = c(rep(0L, 3), rep(1L, 3)),
112+
time_to_exposure = c(rep(NA_real_, 3), 2, 8, 12)
113+
)
114+
115+
ccw_df <- create_clones(df,
116+
id = "id",
117+
event = "event",
118+
time_to_event = "time_to_event",
119+
exposure = "exposure",
120+
time_to_exposure = "time_to_exposure",
121+
ced_window = 20)
122+
123+
# Exposed clones
124+
expect_equal(
125+
ccw_df[ccw_df$id==4 & ccw_df$clone == 1, "censor"],
126+
0L
127+
)
128+
129+
expect_equal(
130+
ccw_df[ccw_df$id==4 & ccw_df$clone == 1, "fup_censor"],
131+
2
132+
)
133+
134+
expect_equal(
135+
ccw_df[ccw_df$id==1 & ccw_df$clone == 1, "censor"],
136+
0L
137+
)
138+
139+
expect_equal(
140+
ccw_df[ccw_df$id==1 & ccw_df$clone == 1, "fup_censor"],
141+
10
142+
)
143+
144+
expect_equal(
145+
ccw_df[ccw_df$id==2 & ccw_df$clone == 1, "censor"],
146+
1L
147+
)
148+
149+
expect_equal(
150+
ccw_df[ccw_df$id==2 & ccw_df$clone == 1, "fup_censor"],
151+
20
152+
)
153+
154+
# Unexposed clones
155+
# Exposed clones
156+
expect_equal(
157+
ccw_df[ccw_df$id==4 & ccw_df$clone == 0, "censor"],
158+
1L
159+
)
160+
161+
expect_equal(
162+
ccw_df[ccw_df$id==4 & ccw_df$clone == 0, "fup_censor"],
163+
2
164+
)
165+
166+
expect_equal(
167+
ccw_df[ccw_df$id==1 & ccw_df$clone == 0, "censor"],
168+
0L
169+
)
170+
171+
expect_equal(
172+
ccw_df[ccw_df$id==1 & ccw_df$clone == 0, "fup_censor"],
173+
10
174+
)
175+
176+
expect_equal(
177+
ccw_df[ccw_df$id==2 & ccw_df$clone == 0, "censor"],
178+
0L
179+
)
180+
181+
expect_equal(
182+
ccw_df[ccw_df$id==2 & ccw_df$clone == 0, "fup_censor"],
183+
20
184+
)
185+
186+
})
187+
188+
test_that("Compare results to Maringe", {
189+
190+
df <- toy_df |>
191+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 182.62)
192+
df <- df[order(df$id, df$clone),]
193+
194+
tab <- tab[order(tab$id, tab$clone),]
195+
196+
# Compare each
197+
for (col in c("outcome", "fup_outcome", "censor", "fup_censor")) {
198+
row.names(df) <- NULL
199+
row.names(tab) <- NULL
200+
expect_equal(
201+
df[[col]],
202+
tab[[col]],
203+
tolerance = 1e-6
204+
)
205+
}
206+
207+
})
208+
209+
test_that("attribute are passed correctly", {
210+
211+
df <- data.frame(
212+
id = 1:6,
213+
event = c(1L, 1L, 1L, 1L, 1L, 1L),
214+
time_to_event = c(10, 100, 100, 10, 100, 100),
215+
exposure = c(rep(0L, 3), rep(1L, 3)),
216+
time_to_exposure = c(rep(NA_real_, 3), 2, 8, 12)
217+
)
218+
219+
ccw_df <- create_clones(df,
220+
id = "id",
221+
event = "event",
222+
time_to_event = "time_to_event",
223+
exposure = "exposure",
224+
time_to_exposure = "time_to_exposure",
225+
ced_window = 20)
226+
227+
expect_equal(
228+
attributes(ccw_df)$id,
229+
"id"
230+
)
231+
232+
expect_equal(
233+
attributes(ccw_df)$event,
234+
"event"
235+
)
236+
237+
expect_equal(
238+
attributes(ccw_df)$time_to_event,
239+
"time_to_event"
240+
)
241+
242+
expect_equal(
243+
attributes(ccw_df)$exposure,
244+
"exposure"
245+
)
246+
247+
expect_equal(
248+
attributes(ccw_df)$time_to_exposure,
249+
"time_to_exposure"
250+
)
251+
252+
expect_equal(
253+
attributes(ccw_df)$ced_window,
254+
20
255+
)
256+
257+
})

‎tests/testthat/test-generate_ccw.R

Lines changed: 32 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,32 @@
1+
2+
test_that("weights are adequately calculated compared to Maringe", {
3+
4+
df <- toy_df |>
5+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 182.62) |>
6+
cast_clones_to_long() |>
7+
generate_ccw(predvars = c("age", "sex", "perf", "stage", "deprivation", "charlson", "emergency"))
8+
9+
df <- df[order(df$id, df$clone, df$time_id),]
10+
row.names(df) <- NULL
11+
12+
# Compare exposed
13+
data_long_cox <- data_long_cox[order(data_long_cox$id, data_long_cox$clone, data_long_cox$time_id),]
14+
row.names(data_long_cox) <- NULL
15+
16+
# Compare all columns
17+
for (col in colnames(data_long_cox)[colnames(data_long_cox) %in% colnames(df)]) {
18+
row.names(df[[col]]) <- NULL
19+
row.names(data_long_cox[[col]]) <- NULL
20+
expect_equal(
21+
df[[col]],
22+
data_long_cox[[col]],
23+
tolerance = 1e-6
24+
)
25+
}
26+
27+
cox_df <- survival::coxph(survival::Surv(t_start, t_stop, outcome) ~ clone, data = df, weights = weight_cox)
28+
cox_data_long_cox <- survival::coxph(survival::Surv(t_start, t_stop, outcome) ~ clone, data = data_long_cox, weights = weight_cox)
29+
30+
expect_equal(cox_df$coefficients, cox_data_long_cox$coefficients, tolerance = 1e-6)
31+
32+
})

‎tests/testthat/test-generate_ccw_on_long_df.R

Lines changed: 128 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,128 @@
1+
test_that("casting clones requires ccw_clones_long class", {
2+
3+
df <- data.frame(
4+
id = c(1, 2, 3, 4),
5+
event = c(0L, 1L, 0L, 1L),
6+
time_to_event = c(100, 200, 100, 200),
7+
exposure = c(0L, 1L, 0L, 1L),
8+
time_to_exposure = c(NA_real_, 2.3, NA_real_, 3.3)
9+
)
10+
11+
expect_error(
12+
generate_ccw_on_long_df(df)
13+
)
14+
15+
df_long <- toy_df |>
16+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 365.25/2) |>
17+
cast_clones_to_long()
18+
19+
attributes(df_long)$id <- NULL
20+
21+
expect_error(
22+
generate_ccw_on_long_df(df_long)
23+
)
24+
25+
df_long$t_start <- NULL
26+
27+
expect_error(
28+
generate_ccw_on_long_df(df_long)
29+
)
30+
})
31+
32+
test_that("when predvars columns are missing, an error is thrown", {
33+
34+
df_long <- toy_df |>
35+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 365.25/2) |>
36+
cast_clones_to_long()
37+
38+
expect_error(
39+
generate_ccw_on_long_df(df_long, predvars = "hamburger")
40+
)
41+
42+
expect_error(
43+
generate_ccw_on_long_df(df_long, predvars = NULL)
44+
)
45+
46+
})
47+
48+
test_that("categorical vars are dealt with", {
49+
50+
toy_df_s <- toy_df
51+
toy_df_s$sandwich <- rep(c("ham", "turkey", "cheese"), length.out = NROW(toy_df_s))
52+
53+
df_long <- toy_df_s |>
54+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 365.25/2) |>
55+
cast_clones_to_long()
56+
57+
expect_error(
58+
generate_ccw_on_long_df(df_long, predvars = "sandwich")
59+
)
60+
61+
df_long$sandwich_f <- factor(df_long$sandwich)
62+
63+
expect_error(
64+
generate_ccw_on_long_df(df_long, predvars = "sandwich_f")
65+
)
66+
67+
expect_error(
68+
generate_ccw_on_long_df(df_long, predvars = NULL)
69+
)
70+
71+
})
72+
73+
test_that("weights are adequately calculated compared to Maringe", {
74+
75+
df <- toy_df |>
76+
create_clones(id = "id", event = "death", time_to_event = "fup_obs", exposure = "surgery", time_to_exposure = "timetosurgery", ced_window = 182.62) |>
77+
cast_clones_to_long()
78+
79+
id <- attributes(df)$id
80+
event <- attributes(df)$event
81+
exposure <- attributes(df)$exposure
82+
time_to_event <- attributes(df)$time_to_event
83+
time_to_exposure <- attributes(df)$time_to_exposure
84+
ced_window <- attributes(df)$ced_window
85+
event_times_df <- attributes(df)$event_times_df
86+
87+
# Create weights
88+
predvars <- c("age", "sex", "perf", "stage", "deprivation", "charlson", "emergency")
89+
90+
df_1 <- generate_ccw_calc_weights(df[df$clone == 1L, ], event_times_df, predvars)
91+
df_1 <- df_1[order(df_1$id, df_1$time_id),]
92+
row.names(df_1) <- NULL
93+
94+
df_0 <- generate_ccw_calc_weights(df[df$clone == 0L, ], event_times_df, predvars)
95+
df_0 <- df_0[order(df_0$id, df_0$time_id),]
96+
row.names(df_0) <- NULL
97+
98+
# Compare exposed
99+
data_long <- data_long[order(data_long$id, data_long$time_id),]
100+
row.names(data_long) <- NULL
101+
102+
data_long_2 <- data_long_2[order(data_long_2$id, data_long_2$time_id),]
103+
row.names(data_long_2) <- NULL
104+
105+
# Compare all columns
106+
for (col in c("time_id", "lp", "t", "hazard", "p_uncens", "weight_cox")) {
107+
row.names(df_1[[col]]) <- NULL
108+
row.names(data_long[[col]]) <- NULL
109+
expect_equal(
110+
df_1[[col]],
111+
data_long[[col]],
112+
tolerance = 1e-6
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)
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}
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# Compare all columns
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for (col in c("time_id", "lp", "t", "hazard", "p_uncens", "weight_cox")) {
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row.names(df_0[[col]]) <- NULL
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row.names(data_long_2[[col]]) <- NULL
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expect_equal(
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df_0[[col]],
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data_long_2[[col]],
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tolerance = 1e-6
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)
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}
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})
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‎vignettes/.gitignore

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*.html
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*.R

‎vignettes/conduct-ccw-analysis.Rmd

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---
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title: "How to Conduct a Clone Censor-Weight Survival Analysis using survivalCCW"
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author: Matthew Secrest
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{How to Conduct a Clone Censor-Weight Survival Analysis using survivalCCW}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>"
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)
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```
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# Clone Censor Weighting
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This lightweight package describes how to conduct clone-censor weighting (CCW) to address the problem of immortal time bias in survival analysis. This vignette will walk through the applied tutorial published by [Maringe et al 2020](https://academic.oup.com/ije/article/49/5/1719/5835351). Refer to [Hernan and Robins 2016](https://doi.org/10.1093/aje/kwv254) and [Hernan et al 2016](https://doi.org/10.1016/j.jclinepi.2016.04.014) for more technical details.
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# Context
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CCW is useful in the presence of immortal person-time bias in observational studies. For instance, when comparing surgery recipients vs non-recipients in non-small cell lung cancer (NSCLC), the surgery group will have a longer survival time than the non-surgery group because the non-surgery group includes patients who died before they could receive surgery. This is a form of immortal time bias.
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The CCW toy dataset published by Maringe uses this exact setting as the motivating example. Let's explore the dataset, which comes with `survivalCCW`.
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```{r}
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library(survivalCCW)
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head(toy_df)
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```
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Column descriptions can be found with `?toy_df`:
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- `id`: patient identifier
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- `fup_obs`: observed follow-up time (time to death or 1 year if censored alive)
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- `death`: observed event of interest (all-cause death) 1: dead, 0: alive
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- `timetosurgery`: time to surgery (NA if no surgery)
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- `age`: age at diagnosis
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- `sex`: patient's sex
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- `perf`: performance status at diagnosis
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- `stage`: stage at diagnosis
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- `deprivation`: deprivation score
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- `charlson`: Charlson's comorbidity index
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- `emergency`: route to diagnosis
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Note that this package addresses situations in which the covariates are all defined at baseline.
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# Create clones
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The first step is to create the clones. This can be done for any time-to-event outcome using the `survivalCCW` function `create_clones`. For `create_clones` to work, we need to pass a one-row-per-patient `data.frame` with the following columns:
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- The id variable (in this case, `id`)
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- The traditional outcome variable which denotes censorship (0) or event (1) (in this case, `death`). Note that additional values are not yet permitted.
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- The time to the first event (in this case, `fup_obs`)
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- The exposure variable, with exposure defined **at any time prior to censorship/event** (in this case, `surgery`). Must be (0) or (1),
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- The time to exposure variable (in this case, `timetosurgery`)
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- The clinical eligibility window (in this case, we'll do 6 months)
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All other columns will be propogated for each patient. Let's see what this looks like in practice.
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```{r}
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# Create clones
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clones <- create_clones(
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df = toy_df,
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id = 'id',
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event = 'death',
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time_to_event = 'fup_obs',
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exposure = 'surgery',
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time_to_exposure = 'timetosurgery',
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ced_window = 182.62
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)
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head(clones)
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```
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Note that this object is just a `data.frame` with an additional custom class which future functions will evaluate:
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```{r}
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class(clones)
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```
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# Cast to long format
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Now we simply need to cast the data to long format. The `survivalCCW` function `cast_to_long` will do this for us.
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No additional arguments are needed (the `clones` object is an artifact that allows you to better see and understand the method):
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```{r}
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clones_long <- cast_clones_to_long(clones)
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head(clones_long, row.names = FALSE)
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```
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Let's pick out a single patient and look at their data:
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```{r}
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print(clones_long[clones_long$id == "P5913", ], row.names = FALSE)
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```
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# Generate weights
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Now we simply need to generate the weights. The `survivalCCW` function `generate_ccw()` will do this for us.
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```{r}
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clones_long_weights <- generate_ccw(clones_long, predvars = c("age", "sex", "perf", "stage", "deprivation", "charlson", "emergency"))
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head(clones_long_weights, row.names = FALSE)
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```
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Let's pick out a single patient and look at their data:
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```{r}
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print(clones_long_weights[clones_long_weights$id == "P5913", ], row.names = FALSE)
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```
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# Evaluate outcomes
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We now have everything we need to conduct a CCW analysis. For instance, we can pipe things together to evaluate the hazard ratio for surgery vs no surgery:
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```{r}
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library(survival)
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df <- toy_df |>
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create_clones(
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id = 'id',
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event = 'death',
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time_to_event = 'fup_obs',
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exposure = 'surgery',
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time_to_exposure = 'timetosurgery',
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ced_window = 365.25/2
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) |>
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cast_clones_to_long() |>
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generate_ccw(c('age', 'sex', 'perf', 'stage', 'deprivation', 'charlson', 'emergency'))
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coxph(Surv(t_start, t_stop, outcome) ~ clone, data = df, weights = weight_cox)
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```
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Note that we used `outcome` and not `death` in the `coxph()` model. Still, there is of course a problem with this analysis, as the cloning process renders the variance invalid. The simplest approach to addressing this is
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to bootstrap the variance. I have not made a function to do this yet, but leave the below as an example of how to do this.
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```{r}
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library(boot)
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boot_cox <- function(data, indices) {
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# Make long data.frame with weights
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ccw_df <- data[indices, ] |>
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create_clones(
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id = 'id',
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event = 'death',
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time_to_event = 'fup_obs',
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exposure = 'surgery',
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time_to_exposure = 'timetosurgery',
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ced_window = 182.62
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) |>
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cast_clones_to_long() |>
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generate_ccw(c('age', 'sex', 'perf', 'stage', 'deprivation', 'charlson', 'emergency'))
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# Extract HR from CoxPH
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cox_ccw <- coxph(Surv(t_start, t_stop, outcome) ~ clone, data = ccw_df, weights = weight_cox)
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hr <- cox_ccw |>
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coef() |>
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exp()
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out <- c("hr" = hr)
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# Create survfit objects for each of treated and untreated
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surv_1 <- survfit(Surv(t_start, t_stop, outcome) ~ 1L, data = ccw_df[ccw_df$clone == 1, ], weights = weight_cox)
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surv_0 <- survfit(Surv(t_start, t_stop, outcome) ~ 1L, data = ccw_df[ccw_df$clone == 0, ], weights = weight_cox)
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# RMST difference
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rmst_1 <- surv_1 |>
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summary(rmean = 365) |>
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(\(summary) summary$table)() |>
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(\(table) table["rmean"])()
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rmst_0 <- surv_0 |>
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summary(rmean = 365) |>
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(\(summary) summary$table)() |>
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(\(table) table["rmean"])()
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rmst_diff <- rmst_1 - rmst_0
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out <- c(out, "rmst_diff" = rmst_diff)
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# 1-year survival difference
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# Find the index of the time point closest to 1 year
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index_1yr_1 <- which.min(abs(surv_1$time - 365))
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index_1yr_0 <- which.min(abs(surv_0$time - 365))
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# Get the 1-year survival probabilities
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surv_1_1yr <- surv_1$surv[index_1yr_1]
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surv_0_1yr <- surv_0$surv[index_1yr_0]
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surv_diff_1yr <- surv_1_1yr - surv_0_1yr
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out <- c(out, "surv_diff_1yr" = surv_diff_1yr)
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}
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boot_out <- boot(data = toy_df, statistic = boot_cox, R = 10)
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```
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## Hazard ratios
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```{r}
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boot.ci(boot_out, type = "norm", index = 1)
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```
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## RMST
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```{r}
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boot.ci(boot_out, type = "norm", index = 2)
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```
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## 1-year survival
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```{r}
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boot.ci(boot_out, type = "norm", index = 3)
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```

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