Graph the PSM survival functions

Usage

graph_psm_survs(timevar, endpoint, ptdata, dpam, psmtype)

Arguments

timevar

Vector of times at which to calculate the hazards

endpoint

Endpoint for which hazard is required (TTP, PPD, PFS, OS or PPS)

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).

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 containing:

adj is the hazard adjusted for constraints
unadj is the unadjusted hazard

Examples

# \donttest{
bosonc <- create_dummydata("flexbosms")
fits <- fit_ends_mods_par(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
)
# Graphic illustrating effect of constraints on OS model
psms_simple <- graph_psm_survs(
  timevar=6*(0:10),
  endpoint="OS",
  ptdata=bosonc,
  dpam=params,
  psmtype="simple"
)
psms_simple$graph

# }