Real World Health Care Data Analysis

Chapter 11: Marginal Structural Models with Inverse Probability Weighting

11.1 Introduction

11.2 Marginal Structural Models with Inverse Probability of Treatment Weighting

11.3 Example: MSM Analysis of the Simulated REFLECTIONS Data

11.3.1 Study Description

11.3.2 Data Overview

11.3.3 Causal Graph

11.3.4 Computation of Weights

11.3.5 Analysis of Causal Treatment Effects Using a Marginal Structural Model

11.4 Summary

References

11.1 Introduction

Assessing treatment effectiveness in longitudinal, observational data can be complex because patients might change medications over time due to different reasons. In addition to the need to control for confounding at baseline due to the lack of randomization, time-dependent confounding can influence treatment ...