Modern mental health studies often result in large complicated data sets. In particular, longitudinal studies that follow individuals over time measure a variety of variables at each time point, yielding data that are often complicated by the presence of missing values or attrition from the sample. Survey data to detect the incidence and prevalence of depression involve lengthy questionnaires to measure mental disorders, and often are analyzed by methods that ignore complexities in the sample design. The effort and expense required to assemble these data sets is considerable, so it seems prudent to analyze them using the best methods available. However, often the analysis is confined to relatively basic statistical methods. Our objectives are to develop statistical methods for efficient and appropriate analysis of mental health data, and to make these methods accessible to other researchers. One of the most common problems facing NIMH researchers is the analysis of unbalanced repeated measures data. Data with this structure are often treated by inefficient and inappropriate methods, and state of the art methods, although an improvement, are usually based on assumptions that may not be appropriate for mental health outcomes. Statistical tests are largely based on large-sample theory, which is inappropriate for the small data sets that are often collected in NIMH studies. We propose to tackle these two problems, and to develop further the class of useful repeated measures models for NIMH data. A second focus of our research concerns the analysis of mental health surveys, with emphasis on nonresponse adjustments. Problems of outliers and missing data are often a significant reason why survey data are left incompletely analyzed. We propose to develop missing-data adjustments that improve considerably on naive methods of imputation, or methods that simply discard the incomplete cases. Our methods will build on the recently developed methodology of multiple imputation, and on the common technique of weighting adjustment for unit nonresponse.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH037188-08
Application #
3376080
Study Section
Epidemiologic and Services Research Review Committee (EPS)
Project Start
1982-09-28
Project End
1992-12-31
Budget Start
1991-03-01
Budget End
1991-12-31
Support Year
8
Fiscal Year
1991
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Type
Schools of Medicine
DUNS #
119132785
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Little, R J; Wang, Y (1996) Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52:98-111
Dorey, F J; Little, R J; Schenker, N (1993) Multiple imputation for threshold-crossing data with interval censoring. Stat Med 12:1589-603
Gornbein, J A; Lazaro, C G; Little, R J (1992) Incomplete data in repeated measures analysis. Stat Methods Med Res 1:275-95
Weiss, R E; Lazaro, C G (1992) Residual plots for repeated measures. Stat Med 11:115-24
Rubin, D B; Schenker, N (1991) Multiple imputation in health-care databases: an overview and some applications. Stat Med 10:585-98
Schluchter, M D (1988) Analysis of incomplete multivariate data using linear models with structured covariance matrices. Stat Med 7:317-24
Berk, K (1987) Computing for incomplete repeated measures. Biometrics 43:385-98