derived_outcome_vals: Derive Outcome Values Based on Log Hazard Ratio

In RobinCar2: ROBust INference for Covariate Adjustment in Randomized Clinical Trials

Derive Outcome Values Based on Log Hazard Ratio

Description

Compute the derived outcome values based on a given log hazard ratio.

Usage

h_derived_outcome_vals(
  theta,
  df,
  treatment,
  time,
  status,
  covariates,
  n = nrow(df)
)

h_strat_derived_outcome_vals(
  theta,
  df,
  treatment,
  time,
  status,
  strata,
  covariates
)

Arguments

theta	(number) The assumed log hazard ratio of the second vs. the first level of the treatment arm variable.
df	(data.frame) The data frame containing the survival data.
treatment	(string) The name of the treatment arm variable in df. It should be a factor with two levels, where the first level is the reference group.
time	(string) The name of the time variable in df, representing the survival time.
status	(string) The name of the status variable in df, with 0 for censored and 1 for event.
covariates	(character) The column names in df to be used for covariate adjustment.
n	(count) The number of observations. Note that this can be higher than the number of rows when used in stratified analyses computations.
strata	(string) The name of the strata variable in df, which must be a factor.

Details

Please note that the covariates must not include index, treatment, time, status to avoid naming conflicts.

Value

A data frame containing the same data as the input df, but restructured with standardized column names index, treatment, time, status, the covariates, and an additional column O_hat containing the derived outcome values. For the stratified version, the list of data frames is returned, one for each stratum.

Functions

• h_derived_outcome_vals(): calculates the derived outcome values for the overall data set.

• h_strat_derived_outcome_vals(): calculates the derived outcome values for each stratum separately.

RobinCar2 documentation built on Sept. 9, 2025, 5:28 p.m.