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

kmqte computes the Quantile 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	kmqte(out, delta, treat, probs = 0.5, xpscore, b = 1000, ci = c(0.9, 0.95, 0.99), 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)
`probs`	scalar or vector of probabilities with values in (0,1) for which the quantile treatment effect is computed. Default is 0.5, returning the median.
`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
`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 quantile treatment effect estimate, qte, and the bootstrapped ci confidence confidence interval, qte.lb (lower bound), and qte.ub (upper bound).

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