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Prepare Survival Data With Time-Dependent Covariates

Source: R/preptdc.R
preptdc.Rd

This function prepares a counting-process style survival dataset for analyses with time-dependent covariates. It merges baseline and longitudinal data, fills in missing covariate values using last-observation-carried-forward (LOCF), restricts to time points where covariates change (optional), and constructs tstart, tstop, and event variables suitable for use in survival models.

Usage

preptdc(
  adsl,
  adtdc,
  id = "SUBJID",
  randdt = "RANDDT",
  trtsdt = "TRTSDT",
  pddt = "PDDT",
  xodt = "XODT",
  osdt = "OSDT",
  died = "DIED",
  dcutdt = "DCUTDT",
  paramcd = "PARAMCD",
  adt = "ADT",
  aval = "AVAL",
  nodup = TRUE,
  offset = TRUE,
  nthreads = 0
)

Arguments

adsl

A data set containing baseline subject-level information. It should include, at a minimum, subject ID (id), randomization date (randdt), treatment start date (trtsdt), progression date (pddt), treatment switch date (xodt), survival outcome (osdt, died), and data cut-off date (dcutdt).

adtdc

A data set containing longitudinal time-dependent covariate data, with subject ID (id), parameter code (paramcd), analysis date (adt), and covariate value (aval).

id

Character string specifying the column name for subject ID.

randdt

Character string specifying the column name for randomization date.

trtsdt

Character string specifying the column name for treatment start date.

pddt

Character string specifying the column name for progression date.

xodt

Character string specifying the column name for treatment crossover/switch date.

osdt

Character string specifying the column name for overall survival date (death date or last known alive date).

died

Character string specifying the column name for death indicator (0 = alive/censored, 1 = died).

dcutdt

Character string specifying the column name for data cut-off date.

paramcd

Character string specifying the column name for parameter code (identifying different covariates).

adt

Character string specifying the column name for analysis date in the time-dependent covariate dataset.

aval

Character string specifying the column name for analysis value (covariate values).

nodup

Logical; if TRUE (default), only rows where at least one covariate changes compared to the previous row (within each subject) are retained, along with the first row per subject (baseline).

offset

Logical; if TRUE (default), add 1-day offset when computing analysis day variables (ady, osdy, etc.).

nthreads

Integer number of threads to use for `data.table' (0 means the default data.table behavior).

Value

A data set with one row per subject and time interval, including:

  • tstart, tstop — interval start and stop times (days from randomization).

  • event — event indicator (0/1).

  • Covariates expanded to wide format.

  • Auxiliary variables such as progression indicator (pd), treatment switch indicator (swtrt), and administrative censoring time.

Details

The function performs the following steps:

  1. Merge adsl and adtdc to obtain randomization date and treatment start date.

  2. Define adt2 as adt if adt > trtsdt, and randdt if adt <= trtsdt (i.e., baseline time point). This ensures that the baseline covariate value is the last non-missing value at or before the treatment start date. Post-baseline covariate values are anchored at their actual analysis dates. The first record per subject corresponds to survival time zero at randomization and ensures availability of baseline covariates at randomization.

  3. Keep the last record per subject, adt2, and paramcd.

  4. Construct a complete skeleton so all covariates are present for each subject and time point.

  5. Fill missing covariate values using LOCF.

  6. Pivot to wide format with one row per subject and time point.

  7. Optionally drop rows without covariate changes (nodup = TRUE).

  8. Merge survival outcomes from adsl.

  9. Compute time-to-event variables (ady, osdy, etc.), as well as counting-process style variables tstart, tstop, and event.

Examples


surv_data <- preptdc(adsl, adtdc, nodup = TRUE, nthreads = 1)
head(surv_data)
#>       SUBJID     RANDDT       adt2 ECOG101 LDH SEX STRAT1V     STRAT2V TRT01P
#> 1 000028-240 2020-07-27 2020-07-27       1 202   F       1 Carboplatin Active
#> 2 000028-240 2020-07-27 2020-08-18       1 278   F       1 Carboplatin Active
#> 3 000028-240 2020-07-27 2020-09-08       1 269   F       1 Carboplatin Active
#> 4 000028-240 2020-07-27 2020-09-29       1 223   F       1 Carboplatin Active
#> 5 000028-240 2020-07-27 2020-10-08       1 267   F       1 Carboplatin Active
#> 6 000028-240 2020-07-27 2020-10-27       1 164   F       1 Carboplatin Active
#>       TRTSDT       PDDT       XODT       OSDT DIED     DCUTDT ady osdy tstart
#> 1 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20   1  416      1
#> 2 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20  23  416     23
#> 3 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20  44  416     44
#> 4 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20  65  416     65
#> 5 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20  74  416     74
#> 6 2020-07-27 2021-05-23 2021-06-17 2021-09-15    1 2023-04-20  93  416     93
#>   tstop event   pd pd_time swtrt swtrt_time censor_time
#> 1    23     0 TRUE     301  TRUE        326         998
#> 2    44     0 TRUE     301  TRUE        326         998
#> 3    65     0 TRUE     301  TRUE        326         998
#> 4    74     0 TRUE     301  TRUE        326         998
#> 5    93     0 TRUE     301  TRUE        326         998
#> 6   114     0 TRUE     301  TRUE        326         998