ps.strata.regression: Evaluate a regression model

In OHDSI/Centaur: Centaur Propensity Score Balancing Workflow and Toolkit

Description

Evaluate a regression outcome model for the stratified dataset.

Usage

ps.strata.regression(data, strata, outcome, estimand = "ATT",
  covariates = character(), family = binomial())

Arguments

data	Data Frame - containing the stratified dataset. The data frame must contain a treatment indicator variable called 'treat' and a propensity score value called 'ps_values'.
strata	Vector - containing the stratum that each subject is assigned to
outcome	The outcome variable name
estimand	String, the desired estimand ("ATT" or "ATE"), default (ATT).
covariates	Vector, containing the names of the additional covariates to include in the outcome analysis
family	- Model family, passed through to glm

Details

This function evaluates an outcome model for each strata and then pools the models to evaluate the overall treatment effect

Value

An object of type "strata.model". This object contains the model information for each strata in object$models, as well as the pooled treatment effect information in object$treatment.effect.

Examples

## Not run: 
ps.strata.regression(myData, strata, "outcome")

## End(Not run)

OHDSI/Centaur documentation built on May 7, 2019, 8:22 p.m.