glm_IPTW: IPTW GLM, Inverse Probaibilty of Treatment Weighted GLM

In mets: Analysis of Multivariate Event Times

IPTW GLM, Inverse Probaibilty of Treatment Weighted GLM

Description

Fits GLM model with treatment weights

w(A)= \sum_a I(A=a)/P(A=a|X)

, computes standard errors via influence functions that are returned as the IID argument. Propensity scores are fitted using either logistic regression (glm) or the multinomial model (mlogit) when more than two categories for treatment. The treatment needs to be a factor and is identified on the rhs of the "treat.model".

Usage

glm_IPTW(
  formula,
  data,
  treat.model = NULL,
  family = binomial(),
  id = NULL,
  weights = NULL,
  estpr = 1,
  pi0 = 0.5,
  ...
)

Arguments

formula	for glm
data	data frame for risk averaging
treat.model	propensity score model (binary or multinomial)
family	of glm (logistic regression)
id	cluster id for standard errors
weights	may be given, and then uses weights*w(A) as the weights
estpr	to estimate propensity scores and get infuence function contribution to uncertainty
pi0	fixed simple weights
...	arguments for glm call

Details

Also works with cluster argument.

Author(s)

Thomas Scheike

mets documentation built on May 29, 2024, 3:51 a.m.