Application of random-effects regression models in relapse research.

This article describes and illustrates use of random-effects regression models (RRM) in relapse research. RRM are useful in longitudinal analysis of relapse data since they allow for the presence of missing data, time-varying or invariant covariates, and subjects measured at different timepoints. Thus, RRM can deal with "unbalanced" longitudinal relapse data, where a sample of subjects are not all measured at each and every timepoint. Also, recent work has extended RRM to handle dichotomous and ordinal outcomes, which are common in relapse research. Two examples are presented from a smoking cessation study to illustrate analysis using RRM. The first illustrates use of a random-effects ordinal logistic regression model, examining longitudinal changes in smoking status, treating status as an ordinal outcome. The second example focuses on changes in motivation scores prior to and following a first relapse to smoking. This latter example illustrates how RRM can be used to examine predictors and consequences of relapse, where relapse can occur at any study timepoint.

[1]  J. Berger,et al.  Empirical Bayes Estimation of Rates in Longitudinal Studies , 1983 .

[2]  D. Hedeker,et al.  MIXOR: a computer program for mixed-effects ordinal regression analysis. , 1996, Computer methods and programs in biomedicine.

[3]  D. Hedeker,et al.  Random regression models: a comprehensive approach to the analysis of longitudinal psychiatric data. , 1988, Psychopharmacology bulletin.

[4]  D. Hedeker,et al.  A longitudinal study of plasma cortisol and depressive symptomatology by random regression analysis , 1992, Biological Psychiatry.

[5]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[6]  P. Albert,et al.  Models for longitudinal data: a generalized estimating equation approach. , 1988, Biometrics.

[7]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[8]  A random effects model for ordinal responses from a crossover trial , 1991 .

[9]  D. Hedeker,et al.  Application of random-effects probit regression models. , 1994, Journal of consulting and clinical psychology.

[10]  E. Vonesh,et al.  Mixed-effects nonlinear regression for unbalanced repeated measures. , 1992, Biometrics.

[11]  A Agresti,et al.  Tutorial on modeling ordered categorical response data. , 1989, Psychological bulletin.

[12]  T A Louis,et al.  Random effects models with non-parametric priors. , 1992, Statistics in medicine.

[13]  Anthony S. Bryk,et al.  Application of Hierarchical Linear Models to Assessing Change , 1987 .

[14]  J. Graham,et al.  Modeling transitions in latent stage-sequential processes: a substance use prevention example. , 1991, Journal of consulting and clinical psychology.

[15]  M. Feinleib,et al.  Statistical Models for Longitudinal Studies of Health , 1992 .

[16]  Dorothy D. Dunlop,et al.  Regression for Longitudinal Data: A Bridge from Least Squares Regression , 1994 .

[17]  H. Goldstein.  Nonlinear multilevel models, with an application to discrete response data , 1991 .

[18]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[19]  D. Hedeker,et al.  MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors. , 1996, Computer methods and programs in biomedicine.

[20]  Roderick J. A. Little,et al.  Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .

[21]  R. Darrell Bock,et al.  ADDENDUM: MEASUREMENT OF HUMAN VARIATION: A TWO-STAGE MODEL , 1989 .

[22]  D. Hedeker,et al.  A random-effects ordinal regression model for multilevel analysis. , 1994, Biometrics.

[23]  J J McArdle,et al.  Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. , 1992, Experimental aging research.

[24]  Donald Hedeker,et al.  Analysis of clustered data in community psychology: With an example from a worksite smoking cessation project , 1994, American journal of community psychology.

[25]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[26]  D. Hedeker,et al.  Random-effects regression models for clustered data with an example from smoking prevention research. , 1994, Journal of consulting and clinical psychology.

[27]  D. Rubin,et al.  Estimation in Covariance Components Models , 1981 .

[28]  R. McKelvey,et al.  A statistical model for the analysis of ordinal level dependent variables , 1975 .

[29]  W F Velicer,et al.  Latent transition analysis for longitudinal data. , 1996, Addiction.

[30]  A. Wald.  Tests of statistical hypotheses concerning several parameters when the number of observations is large , 1943 .

[31]  R. Jennrich,et al.  Unbalanced repeated-measures models with structured covariance matrices. , 1986, Biometrics.

[32]  G. Y. Wong,et al.  The Hierarchical Logistic Regression Model for Multilevel Analysis , 1985 .

[33]  Jan de Leeuw,et al.  Random Coefficient Models for Multilevel Analysis , 1986 .

[34]  P. Diggle.  Analysis of Longitudinal Data , 1995 .

[35]  Random Coefficient Models for Multilevel Analysis , 1986 .

[36]  Jan de Leeuw,et al.  Review of Five Multilevel Analysis Programs: BMDP-5V, GENMOD, HLM, ML3, VARCL , 1994 .

[37]  C Waternaux,et al.  Investigating drug plasma levels and clinical response using random regression models. , 1989, Psychopharmacology bulletin.

[38]  H. Weisberg,et al.  Empirical Bayes estimation of individual growth-curve parameters and their relationship to covariates. , 1983, Biometrics.

[39]  Robert D. Gibbons,et al.  Trend in correlated proportions , 1987 .

[40]  N M Laird,et al.  Mixture models for the joint distribution of repeated measures and event times. , 1997, Statistics in medicine.

[41]  Murray Aitkin,et al.  Variance Component Models with Binary Response: Interviewer Variability , 1985 .

[42]  N M Laird,et al.  Missing data in longitudinal studies. , 1988, Statistics in medicine.

[43]  J. Jansen,et al.  On the Statistical Analysis of Ordinal Data When Extravariation is Present , 1990 .

[44]  S D Imber,et al.  Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Application to the NIMH treatment of Depression Collaborative Research Program dataset. , 1993, Archives of general psychiatry.

[45]  J. Ware,et al.  Random-effects models for serial observations with binary response. , 1984, Biometrics.

[46]  R. D. Bock,et al.  Multivariate Statistical Methods in Behavioral Research , 1978 .

[47]  N. Longford.  A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects , 1987 .