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Graph the observed and fitted state membership probabilities

Source: R/probgraphs.R
graph_survs.Rd

Graph the observed and fitted state membership probabilities for PF, PD, OS and PPS.

Usage

graph_survs(ptdata, dpam, cuttime = 0)

Arguments

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).

cuttime

is the cut-off time for a two-piece model (default 0, indicating a one-piece model)

Value

List of two items as follows. data is a tibble containing data derived and used in the derivation of the graphics. graph is a list of four graphics as follows:

  • pf: Membership probability in PF (progression-free) state versus time since baseline, by method

  • pd: Membership probability in PD (progressive disease) state versus time since baseline, by method

  • os: Probability alive versus time since baseline, by method

  • pps: Probability alive versus time since progression, by method

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
)
# Create graphics
gs <- graph_survs(ptdata=bosonc, dpam=params)
gs$graph$pd
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).

# }