calc_haz_psm {psm3mkv}

Derive pre and post-progression hazards of death under PSM

Description

Derive the hazards of death pre- and post-progression under either simple or complex PSM formulations.

Usage

calc_haz_psm(timevar, ptdata, dpam, psmtype)

Arguments

timevar

Vector of times at which to calculate the hazards

ptdata

Dataset of patient level data. Must be a tibble with columns named:

• ptid: patient identifier

• pfs.durn: duration of PFS from baseline

• pfs.flag: event flag for PFS (=1 if progression or death occurred, 0 for censoring)

• os.durn: duration of OS from baseline

• os.flag: event flag for OS (=1 if death occurred, 0 for censoring)

• ttp.durn: duration of TTP from baseline (usually should be equal to pfs.durn)

• ttp.flag: event flag for TTP (=1 if progression occurred, 0 for censoring).

Survival data for all other endpoints (time to progression, pre-progression death, post-progression survival) are derived from PFS and OS.

dpam

List of survival regressions for each endpoint:

• pre-progression death (PPD)

• time to progression (TTP)

• progression-free survival (PFS)

• overall survival (OS)

• post-progression survival clock forward (PPS-CF) and

• post-progression survival clock reset (PPS-CR).

psmtype

Either "simple" or "complex" PSM formulation

Value

List of pre, the pre-progression hazard, and post, the post-progression hazard

Examples

bosonc <- create_dummydata("flexbosms")
fits <- fit_ends_mods_spl(bosonc)
# Pick out best distribution according to min AIC
params <- list(
  ppd = find_bestfit(fits$ppd, "aic")$fit,
  ttp = find_bestfit(fits$ttp, "aic")$fit,
  pfs = find_bestfit(fits$pfs, "aic")$fit,
  os = find_bestfit(fits$os, "aic")$fit,
  pps_cf = find_bestfit(fits$pps_cf, "aic")$fit,
  pps_cr = find_bestfit(fits$pps_cr, "aic")$fit
  )
calc_haz_psm(0:10, ptdata=bosonc, dpam=params, psmtype="simple")
calc_haz_psm(0:10, ptdata=bosonc, dpam=params, psmtype="complex")