Longitudinal Data Analysis in Depression Studies: Assessment of Intermediate-Outcome-Dependent Dynamic Interventions

Longitudinal studies in the treatment of mental diseases, such as chronic forms of major depressive disorders, frequently use sequential randomization design to investigate treatment strategies. Outcomes in such studies often consist of repeated measurements of scores, such as the 24-item Hamilton Rating Scale for Depression, throughout the duration of the therapy. The goal is to compare different sequences of treatments to find the most beneficial one for each patient. Note that since treatments are applied sequentially, the eligibility of receiving one treatment assignment depends on previous treatments and outcomes. Two issues that make the analysis of data from such sequential designs different from standard longitudinal data are: (1) the randomization in the subsequent stages for patients who fail to respond in the previous stage; and (2) the drop-out of patients, for which the assumption of missing completely at random is usually not realistic. In this dissertation, we show how the inverse-probability-weighted generalized estimating equations (IPWGEE) method can be used to draw inference for treatment regimes from two-stage studies. Specifically, we show how to construct weights and use them in the IPWGEE to derive consistent estimators for the effects of treatment regimes, and compare them. Large-sample properties of the proposed estimators are derived analytically, and examined through simulations. We demonstrate our methods by applying them to a depression dataset. Public Health Significance: Mental illness is becoming a major public health challenge. Strategies of multiple treatments have been introduced by many investigators to serve as an alternative to single strategy in treating patients with chronic depressive disorders. As the complexity of study design increases, developing sophisticated statistical method is necessary in order to provide valid inference. This dissertation demonstrates the importance of statistical aspects to estimate the effects of depression treatment regimes from two-stage longitudinal studies