Document Detail

Predicting longitudinal trajectories of health probabilities with random-effects multinomial logit regression.
MedLine Citation:
PMID:  22825754     Owner:  NLM     Status:  MEDLINE    
Researchers often encounter longitudinal health data characterized with three or more ordinal or nominal categories. Random-effects multinomial logit models are generally applied to account for potential lack of independence inherent in such clustered data. When parameter estimates are used to describe longitudinal processes, however, random effects, both between and within individuals, need to be retransformed for correctly predicting outcome probabilities. This study attempts to go beyond existing work by developing a retransformation method that derives longitudinal growth trajectories of unbiased health probabilities. We estimated variances of the predicted probabilities by using the delta method. Additionally, we transformed the covariates' regression coefficients on the multinomial logit function, not substantively meaningful, to the conditional effects on the predicted probabilities. The empirical illustration uses the longitudinal data from the Asset and Health Dynamics among the Oldest Old. Our analysis compared three sets of the predicted probabilities of three health states at six time points, obtained from, respectively, the retransformation method, the best linear unbiased prediction, and the fixed-effects approach. The results demonstrate that neglect of retransforming random errors in the random-effects multinomial logit model results in severely biased longitudinal trajectories of health probabilities as well as overestimated effects of covariates on the probabilities.
Xian Liu; Charles C Engel
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2012-07-24
Journal Detail:
Title:  Statistics in medicine     Volume:  31     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-23     Completed Date:  2013-05-27     Revised Date:  2014-01-16    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  4087-101     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 John Wiley & Sons, Ltd.
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MeSH Terms
Aged, 80 and over
Disabled Persons / statistics & numerical data*
Logistic Models*
Longitudinal Studies / statistics & numerical data*
Mortality / trends*
Predictive Value of Tests
United States
Grant Support
R03 AG020140/AG/NIA NIH HHS; R03AG20140-01/AG/NIA NIH HHS

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