Constructing Inverse Probability Weights for Marginal Structural Models

TLDR

Inverse probability weighting adjusts for measured confounding and selection bias under assumptions of consistency, exchangeability, positivity, and correct model specification, and is increasingly used to estimate effects of time‑varying exposures while diagnostics and sensitivity analyses remain essential. The authors illustrate the four assumptions with antiretroviral therapy effects on HIV viral load in 918 patients, and propose weight truncation as a simple approach to balance bias–precision tradeoffs. They show that inverse probability weighting can reveal causal effects otherwise hidden, but epidemiologists face bias–precision tradeoffs that can be visualized as a function of confounding control.

Abstract

The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use.