Longitudinal studies occupy an important role in modern psychiatric research; however, the statistical methods for analyzing these data are rarely commensurate with the level of effort and expense required to obtain such data. Often, a simple """"""""endpoint"""""""" analysis is performed or records with missing data are discarded. The results from either compromise can be extremely misleading. The purpose of this proposal is to develop and make available, a general statistical model for the analysis of within-subject psychiatric data. Based on the concepts of """"""""random regression"""""""" we will develop a general model for continuous, discrete and ordinal data that permit missing data, irregularly spaced observations, correlated errors, time varying covariates (e.g., plasma level) and time invariant covariates (e.g., treatment group). Unlike traditional methods based on average change, the random regression approach can also provide estimates of the rate of change for each individual subject. This particularly useful in the psychiatric setting where a proportion of subjects may respond to therapy in quite different ways from the average response. In addition to the statistical derivation, the general model will be tested using simulated data and illustrated using 5 existing psychiatric data sets. We will develop user-friendly public-domain software running on a micro-computer and a primer designed to illustrate the use of the model on the five data sets. Finally, the statistical properties of the method will be examined in terms of power, robustness, model fit and model selection.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH044826-02
Application #
2246246
Study Section
Epidemiologic and Services Research Review Committee (EPS)
Project Start
1991-03-01
Project End
1993-11-30
Budget Start
1992-03-01
Budget End
1993-11-30
Support Year
2
Fiscal Year
1992
Total Cost
Indirect Cost
Name
University of Illinois at Chicago
Department
Psychiatry
Type
Schools of Medicine
DUNS #
121911077
City
Chicago
State
IL
Country
United States
Zip Code
60612
Hedeker, D; Mermelstein, R J (2000) Analysis of longitudinal substance use outcomes using ordinal random-effects regression models. Addiction 95 Suppl 3:S381-94
Hedeker, D; Flay, B R; Petraitis, J (1996) Estimating individual influences of behavioral intentions: an application of random-effects modeling to the theory of reasoned action. J Consult Clin Psychol 64:109-20
Hedeker, D; Gibbons, R D (1996) MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors. Comput Methods Programs Biomed 49:229-52
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Hedeker, D; Gibbons, R D (1996) MIXOR: a computer program for mixed-effects ordinal regression analysis. Comput Methods Programs Biomed 49:157-76
Hedeker, D; Gibbons, R D (1994) A random-effects ordinal regression model for multilevel analysis. Biometrics 50:933-44
Gibbons, R D; Hedeker, D (1994) Application of random-effects probit regression models. J Consult Clin Psychol 62:285-96
Hedeker, D; McMahon, S D; Jason, L A et al. (1994) Analysis of clustered data in community psychology: with an example from a worksite smoking cessation project. Am J Community Psychol 22:595-615
Hedeker, D; Gibbons, R D; Flay, B R (1994) Random-effects regression models for clustered data with an example from smoking prevention research. J Consult Clin Psychol 62:757-65
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