# ps.wgt.fun: Calculates propensity score weights In landest: Landmark Estimation of Survival and Treatment Effect

## Description

Calculates propensity score (or inverse probability of treatment) weights given the treatment indicator and available baseline (pretreatment) covariates.

## Usage

 1 ps.wgt.fun(treat, cov.for.ps, weight = NULL) 

## Arguments

 treat treatment indicator, should be 0/1. cov.for.ps matrix of covariates to be used to estimate propensity score (or inverse probability of treatment) weights weight a (n1+n0) by x matrix of weights where n1 = number of observations in treatment group 1 and n0 = number of observations in treatment group 0; used for perturbation-resampling, default is null.

## Details

Let Z_{i} denote the matrix of baseline (pretreatment) covariates and G_i be the treatment group indicator such that G_i = 1 indicates treatment and G_i = 0 indicates control. This function estimates P = P(G_i = 1 | Z_i) using logistic regression. The propensity score (or inverse probability of treatment) weights are then equal to 1/\hat{P} for those in treatment group 1 and 1/(1-\hat{P}) for those in treatment group 0. These weights reflect the situation where the average treatment effect (ATE) is of interest, not average treatment effect in the treated (ATT).

## Value

propensity score (or inverse probability of treatment) weights

Layla Parast

## References

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516-524.

## Examples

 1 2 3 4 data(example_obs) W.weight = ps.wgt.fun(treat = example_obs$treat, cov.for.ps = as.matrix(example_obs$Z)) delta.iptw.km(tl=example_obs$TL, dl = example_obs$DL, treat = example_obs$treat, tt=2, ps.weights = W.weight)  ### Example output $S.estimate.1
[1] 0.4781018

$S.estimate.0 [1] 0.4211204$delta.estimate
[1] 0.05698143


landest documentation built on Nov. 16, 2021, 9:24 a.m.