ORCI: Estimate covariate associations with survival at the cutoff...

In PDRobust: Robust Longitudinal Effects Under Truncation by Death

Estimate covariate associations with survival at the cutoff time

Description

Estimates odds ratios with confidence intervals for associations between covariates and survival at the cutoff time within a selected treatment group.

Usage

ORCI(data, formula, a, conf_level = 0.95)

Arguments

data	Data prepared by DataStandard().
formula	A logistic regression formula with the survival variable on the left-hand side and the covariates of interest on the right-hand side.
a	The treatment group to analyze at the cutoff time, either 0 or 1.
conf_level	The confidence level, expressed as a single number between 0 and 1. Defaults to 0.95.

Details

ORCI() fits the supplied logistic regression model using observations from treatment group a at the cutoff time. It reports an odds ratio and Wald confidence interval for every non-intercept coefficient that can be estimated. Covariates are not selected according to statistical significance.

Value

An odds_ratios object containing odds-ratio estimates and confidence intervals, the fitted model, model-checking information, and a forest plot. Reported estimates are rounded to three decimal places.

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)
result <- ORCI(
  pd_dat, S ~ X1 + X2 + X4, a = 0
)
result$forestplotdat

PDRobust documentation built on Oct. 2, 2026, 5:09 p.m.