ps.wgt.fun: Calculates propensity score weights

Description Usage Arguments Details Value Author(s) References Examples

View source: R/landest_functions.R

Description

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

Usage

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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

Author(s)

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

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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 May 2, 2019, 10:12 a.m.