This methodological project proposes a novel statistical strategy. The strategy integrates two components that are fundamental to longitudinal analysis of treatment effectiveness in an observational study. First, treatment status and level of psychopathology are dynamic processes, in that they change over the course of an illness. Second, there are clinical and demographic characteristics, which define, in part, the propensity of an individual to be treated. Standard data analytic strategies fail to capture the complex nature of treatment effectiveness over extended follow-up. Rosenbaum and Rubin (1983) have shown that the propensity scoring method can be used for causal inference from observational data. This proposal extends their approach to a dynamic model for analysis of longitudinal treatment effectiveness data. The procedure that is proposed here will adapt a mixed-model approach to propensity score methodology (Aim 1). It will be used to examine antidepressant treatment effectiveness in subjects who were initially identified with affective disorders and have been followed-up over 15 years as part of the NIMH Collaborative Study of the Psychobiology of Depression. This dynamic data analytic approach provides a framework for incorporating multiple observations per subject, over the repeated episodes and recoveries that typically comprise a chronic illness, into an evaluation of treatment effectiveness (Aim 2). Furthermore, incorporating the propensity for treatment in the analyses reduces the bias that is inherent in an observational study of effectiveness. The methodology that is proposed here will also be applied to archival randomized clinical trial (RCT) data sets. The propensity for study completion and the propensity for missingness will be accounted for in the evaluation of treatment effectiveness in RCTs (Aim 3). Its use could preclude the need for both endpoint and completer analyses. The performance of the proposed methodology will be evaluated and compared to standard procedures in a Simulation Study (Aim 4).

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH060447-02
Application #
6499354
Study Section
Special Emphasis Panel (ZMH1-SRV-C (01))
Program Officer
Hohmann, Ann A
Project Start
2001-02-15
Project End
2004-01-31
Budget Start
2002-02-01
Budget End
2003-01-31
Support Year
2
Fiscal Year
2002
Total Cost
$169,500
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
201373169
City
New York
State
NY
Country
United States
Zip Code
10065
Leon, Andrew C; Fiedorowicz, Jess G; Solomon, David A et al. (2014) Risk of suicidal behavior with antidepressants in bipolar and unipolar disorders. J Clin Psychiatry 75:720-7
Leon, Andrew C; Hedeker, Donald; Li, Chunshan et al. (2012) Performance of a propensity score adjustment in longitudinal studies with covariate-dependent representation. Stat Med 31:2262-74
Leon, Andrew C (2011) Two clinical trial designs to examine personalized treatments for psychiatric disorders. J Clin Psychiatry 72:593-7
Leon, Andrew C; Solomon, David A; Li, Chunshan et al. (2011) Antidepressants and risks of suicide and suicide attempts: a 27-year observational study. J Clin Psychiatry 72:580-6
Leon, Andrew C (2011) Evaluation of psychiatric interventions in an observational study: issues in design and analysis. Dialogues Clin Neurosci 13:191-8
Leon, Andrew C; Davis, Lori L; Kraemer, Helena C (2011) The role and interpretation of pilot studies in clinical research. J Psychiatr Res 45:626-9
Leon, Andrew C (2011) Comparative effectiveness clinical trials in psychiatry: superiority, noninferiority, and the role of active comparators. J Clin Psychiatry 72:1344-9
Abrams, Robert C; Leon, Andrew C; Tardiff, Kenneth et al. (2011) Suicidal overdoses of psychotropic drugs by elderly in New York City: comparison with younger adults. Psychiatry Res 188:459-61
Leon, Andrew C (2011) Evolution of psychopharmacology trial design and analysis: six decades in the making. J Clin Psychiatry 72:331-40
Heo, Moonseong; Kim, Yongman; Xue, Xiaonan et al. (2010) Sample size requirement to detect an intervention effect at the end of follow-up in a longitudinal cluster randomized trial. Stat Med 29:382-90

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