kmate: Kaplan-Meier Average Treatment Effect
In pedrohcgs/kmte: An R Package for Treatment Effects with Censored Outcomes

kmate computes the Average Treatment Effect for possibly right-censored outcomes. The estimator relies on the unconfoundedness assumption, and on estimating the propensity score. For details of the estimation procedure, see Sant'Anna (2016a), 'Program Evaluation with Right-Censored Data'.

1 2	kmate(out, delta, treat, xpscore, b = 1000, ci = c(0.9, 0.95, 0.99), trunc = NULL, standardize = TRUE, cores = 1)

`out`	vector containing the outcome of interest
`delta`	vector containing the censoring indicator (1 if observed, 0 if censored)
`treat`	vector containing the treatment indicator (1 if treated, 0 if control)
`xpscore`	matrix (or data frame) containing the covariates (and their transformations) to be included in the propensity score estimation. Propensity score estimation is based on Logit.
`b`	The number of bootstrap replicates to be performed. Default is 1,000.
`ci`	A scalar or vector with values in (0,1) containing the confidence level(s) of the required interval(s). Default is a vector with 0,90, 0.95 and 0.99
`trunc`	scalar that defined the truncation parameter. Default is NULL, which does not perform any kind of truncation in the computation of the ATE. When trunc is different than NULL, all outcomes which values greater than trunc are truncated.
`standardize`	Default is TRUE, which normalizes propensity score weights to sum to 1 within each treatment group. Set to FALSE to return Horvitz-Thompson weights.
`cores`	number of processesors to be used during the bootstrap (default is 1). If cores>1, the bootstrap is conducted using snow

a list containing the Average treatment effect estimate, ate, and the bootstrapped ci confidence confidence interval, ate.lb (lower bound), and ate.ub (upper bound).

pedrohcgs/kmte documentation built on May 24, 2019, 11:46 p.m.