Random effects and latent processes approaches for analyzing binary longitudinal data with missingness: A comparison of approaches using opiate clinical trial data

Statistical Methods in Medical Research

Volumn 16, Issue 5, 2007, Pages 417-439

  Albert, Paul S ^a   Follmann, Dean A ^b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b National Institute of Allergy and Infectious Disease   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUPRENORPHINE; METHADONE; OPIATE;

ARTICLE; BIOSTATISTICS; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DATA ANALYSIS; DRUG RESPONSE; HUMAN; LONGITUDINAL STUDY; LOW DRUG DOSE; MAJOR CLINICAL STUDY; METHODOLOGY; OPIATE ADDICTION; RANDOMIZED CONTROLLED TRIAL; STATISTICAL ANALYSIS; URINALYSIS;

CLINICAL TRIALS AS TOPIC; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; OPIATE ALKALOIDS; PATIENT DROPOUTS;

EID: 35649000012     PISSN: 09622802     EISSN: None     Source Type: Journal    
DOI: 10.1177/0962280206075308     Document Type: Article

References (24)

1. Little RJA, Rubin D. Statistical analysis with missing-data. Wiley, 1987.
2. Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 1988; 44: 175-88.
3. Follmann D, Wu M. An approximate generalized linear model with random effects for informative missing data. Biometrics 1995; 51: 151-68.
4. Albert PS, Follmann DA, Wang SA, Suh EB. A latent autoregressive model for longitudinal binary data subject to informative missingness. Biometrics 2002; 58: 631-42.
5. Johnson RE, Jaffe JH, Fudala PJ. A controlled trial of buprenorphine treatment for opioid dependence. Journal of the American Medical Association 1992; 267: 2750-55.
6. Aptech. GAUSS system, version 3.0. Aptech Systems, 1992.
7. Abramowitz M, Stegun IA. Handbook of mathematical functions. Dover, 1972.
8. Ten Have TR, Kunselman AR, Pulkstenis EP, Landis JR. Mixed effects logistic regression models for longitudinal binary response data with informative dropout. Biometrics 1998; 54: 367-83.
9. Evans M, Swartz T. Approximating integrals via Monte Carlo and deterministic metbods. Oxford University Press, 2000.
10. Efron B, Tibshirani RJ. An introduction to the bootstrap. Chapman and Hall, 1993.
11. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44: 1049-60.
12. Heagerty PJ. Marginally specified logistic-normal models for longitudinal binary data. Biometrics 1999; 55: 688-98.
13. Karlin S, Taylor HM. A second course in stocbastic processes. Academic Press, 1981.
14. McCulloch CE. Maximum-likelihood algorithms for generalized linear mixed models. Journal of the American Statistical Association 1997; 92: 162-70.
15. Booth JG, Hobert JP. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B 1999; 61: 265-85.
16. Albert PS, Follmann DA. A random effects transition model for longitudinal binary data with informative missingness. Statistica Neerlandica 2003; 57: 100-11.
17. Chiang CL. An introduction to stochastic processes and their application. Krieger, 1968.
18. Wu MC, Bailey KR. Estimation and comparison of changes in the presence of informative right censoring: conditional model. Biometrics 1989; 45: 939-55.
19. Little RJ, Raghunathan T. On summary measures analysis of the linear mixed effects model for repeated measures when data are not missing completely at random. Statistics and Medicine 1999; 18: 2465-78.
20. Albert PS. A transitional model for longitudinal binary data subject to nonignorable missing data. Biometrics 2000; 56: 602-608.
21. Zeger SL, Qaqish B. Markov regression models for time-series: a quasi-likelihood approach. Biometrics 1988; 44: 1019-31.
22. Baum LE, Petrie T, Soules G, Weiss, N. A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Annals of Statistics 1970; 41: 164-71.
23. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 12-22.
24. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for non-ignorable drop-out using semiparametric non-response models (with discussion). Journal of the American Statistical Association 1999; 94: 1096-146.