This methodological project proposes to evaluate and apply an innovative 2-staged statistical procedure that reduces the impact of self-selection in treatment effectiveness analyses. It is a dynamic adaptation of the Rosenbaum and Rubin propensity adjustment that accounts for changes in treatment and psychopathology over the course of an illness. It is capable of adjusting for selection bias, incorporating multiple observations per subject, and comparing effectiveness of ordinal doses. In the first stage of analyses, a model of propensity for treatment examines demographic and clinical characteristics that distinguish among subjects who receive various levels of treatment (Aim 1). The propensity adjustment is then applied to examine effectiveness of medication and psychotherapeutic interventions in longitudinal, observational studies of Personality Disorders (PD), Body Dysmorphic Disorder (BDD), and Bipolar Depression (Aim 2). In addition, the methodology will be used to account for what is typically an observational aspect of randomized clinical trials (RCTs): missing data due to dropout. It will be used to adjust for the propensity for dropout in treatment effectiveness analyses of archival data from RCTs for the treatment of geriatric depression and chronic depression (Aim 3). Finally, a series of simulation studies will evaluate the performance of the proposed statistical approaches under various specifications, separately for conditions which mirror data from observational and RCT designs (Aim 4). Specifically, Type I error rates, statistical power, and bias will be estimated for each of the statistical procedures. These results will be compared with the performance of more customary approaches. In summary, this interdisciplinary project will inform methodologists about the proposed data analytic strategy and inform clinicians about effectiveness of treatments for PD, BDD, and bipolar depression.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH060447-06
Application #
7247134
Study Section
Special Emphasis Panel (ZMH1-DEA-Z (03))
Program Officer
Rupp, Agnes
Project Start
2001-02-15
Project End
2009-06-30
Budget Start
2007-07-01
Budget End
2008-06-30
Support Year
6
Fiscal Year
2007
Total Cost
$189,348
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
060217502
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; Leon, Andrew C (2010) Sample sizes required to detect two-way and three-way interactions involving slope differences in mixed-effects linear models. J Biopharm Stat 20:787-802

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