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Document Type

Open Access

Degree Program

Public Health

Degree Type

Master of Science (M.S.)

Year Degree Awarded

2013

Month Degree Awarded

September

Keywords

Antidepressant medication, type 2 diabetes, epidemiology, marginal structural models, propensity score, survival analysis

Abstract

Objective: The project aimed to compare marginal structural models, and propensity score adjusted models with Cox Proportional Hazards models to address confounding by indication due to time-dependent confounders. These methods were applied to data from approximately 120,000 women in the Women’s Health Initiative to evaluate the causal effect of antidepressant medication with respect to diabetes risk.

Methods: Four approaches were compared. Three Cox Models were used. The first used baseline covariates. The second used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates. The third used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates and propensity to taking antidepressants at baseline. Our fourth method used a Marginal Structural Cox Model with Inverse Probability of Treatment Weighting that included time-varying antidepressant medication, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates.

Results: All approaches showed an increase in diabetes risk for those taking antidepressants. Diabetes risk increased with adjustment for time-dependent confounding and results for these three approaches were similar. All models were statistically significant. Ninety-five percent confidence intervals overlapped for all approaches showing they were not different from one another.

Conclusions: Our analyses did not find a difference between Cox Proportional Hazards Models and Marginal Structural Cox Models in the WHI cohorts. Estimates of the Inverse Probability of Treatment Weights were very close to 1 which explains why we observed similar results.

First Advisor

Raji Balasubramanian

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