Inverse Probability Weighting

If one is interested in estimating a casual effect, there are several analysis options to remove confounding. These options are restriction, matching, adjustment and weighting. One approach to remove confounding using weights is Inverse probability weighting. Inverse probability weighting relies on building a logistic regression model to estimate the probability of the exposure observed for a particular person, and using the predicted probability as a weight in subsequent analyses.

RESOURCE LIST

EDUCATIONAL WEBSITES

METHODOLOGICAL ARTICLES

Marginal structural models as a tool for standardization

Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men

Estimating causal effects from epidemiological data

Marginal structural models and causal inference in epidemiology

EXAMPLE APPLICATION ARTICLES

Associations between aldosterone antagonist therapy and risks of mortality and readmission among patients with heart failure and reduced ejection fraction

Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models