Estimate cumulative principal scores

Fits the principal score model and returns each row's estimated probability of surviving from baseline through its observed time under treatment a. All observed times from baseline through cutoff are used, and the model is fitted again each time the function is called.

Usage

PrinPred(prin_fo, fit_dat, pred_dat, a, mapping, ...)

Arguments

prin_fo

principal score model formula

fit_dat

A data frame containing the observations used to fit the model.

pred_dat

A data frame containing the observations for which cumulative survival probabilities are requested.

a

The treatment level under which survival probabilities are predicted, either 0 or 1.

mapping

A pd_mapping object that identifies the variables and analysis times.

...

Additional arguments passed to stats::glm().

Value

A numeric vector of cumulative survival probabilities, one for each row of pred_dat, rounded to three decimal places.

Details

When the data contain multiple times, each post-baseline observation is used to model the next survival step only if the subject was alive at the previous observed time. If the data contain only one observed time, all complete observations at that time are used.

Examples

data("BiSample", package = "PDRobust")
map <- Mapping(
  id = "id", time = "time", treatment = "A",
  survival = "S", outcome = "Y",
  baseline_time = 0, cutoff_time = 2,
  covariates = c("X1", "X2", "X4"),
  interest_vars = c("X1", "X2"), y_type = "B"
)
pd_dat <- DataStandard(BiSample, map)
score0 <- PrinPred(
  S ~ X1 + X2 + X4 + A + time,
  pd_dat, pd_dat, a = 0, mapping = map
)
head(score0)
#> [1] 1.000 0.920 0.814 1.000 0.947 0.873