A ramdom-effects Markov transition model for poisson-distributed repeated measures with non-ignorable missing values

Statistics in Medicine

Volumn 26, Issue 12, 2007, Pages 2519-2532

  Li, Jinhui ^a,b   Yang, Xiaowei ^b,c   Wu, Yingnian ^a   Shoptaw, Steven ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b BayesSoft Inc   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Markov transition models; Non ignorable missing values; Poisson regression model; Repeated measures; Shared parameter missingness

Indexed keywords

CARBON MONOXIDE; NICOTINE;

ALGORITHM; ARTICLE; CARBON MONOXIDE BLOOD LEVEL; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; HUMAN; MEDICAL RESEARCH; MONTE CARLO METHOD; NICOTINE REPLACEMENT THERAPY; POISSON DISTRIBUTION; PROBABILITY; SMOKING CESSATION; STATISTICAL ANALYSIS; BEHAVIOR THERAPY; COMPUTER SIMULATION; LONGITUDINAL STUDY; METHODOLOGY; RANDOMIZED CONTROLLED TRIAL; STANDARD;

ALGORITHMS; BEHAVIOR THERAPY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; LONGITUDINAL STUDIES; MARKOV CHAINS; NICOTINE; POISSON DISTRIBUTION; RANDOMIZED CONTROLLED TRIALS; SMOKING CESSATION;

EID: 34249036469     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2717     Document Type: Article

References (22)

1. Albert PS, Follmann DA. A random effects transition model for longitudinal binary data with informative missingness. Statistica Neerlandica 2003; 57:100-111.
2. Yang X, Nie K, Belin RT, Liu J, Shoptaw S. Markov transition models for binary repeated measures with ignorable and non-ignorable missing values. Statistical Methods in Medical Research, in press.
3. Durrett R. Essentials of Stochastic Processes. Springer: New York, 1999.
4. Diggle P, Heagerty P. Liang K-Y, Zeger S. Analysis of Longitudinal Data (2nd edn). Oxford University Press: Oxford, 2002.
5. Follman D, Wu M. An approximate generalized linear model with random effects for informative missing data. Biometrics 1995; 51:151-168.
6. Yang X, Shoptaw S. Assessing missing data assumptions in longitudinal studies: an example using a smoking cessation trial. Drug and Alcohol Dependence 2005; 77:213-225.
7. Shoptaw S, Rotheram-Fuller E, Yang X, Frosch D, Nahom D, Jarvik ME, Rawson RA, Ling W. Smoking cessation in methadone maintenance. Addiction 2002; 97:1317-1328.
8. Rubin DB. Inference and missing data. Biometrika 1976; 63:581-582.
9. Little RJA, Rubin DB. Statistical Analysis with Missing Data (2nd edn). Wiley: New York, 2002.
10. Schafer JL. Analysis of Incomplete Multivariate Data. Chapman & Hall: London, 1997.
11. McCulloch CE, Searle SR. Generalized, Linear, and Mixed Models. Wiley: New York, 2001.
12. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
13. Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. Springer: New York, 2000.
14. Little RJA. Modeling the drop-out mechanism in longitudinal studies. Journal of the American Statistical Association 1995; 90:1112-1121.
15. Thijs H, Molenberghs G, Michiels B, Verbeke C, Curran D. Strategies to fit pattern-mixture models. Biostatistics 2002; 3-2:245-265.
16. Robert CP, Casella G. Monte Carlo Statistical Methods (2nd edn). Springer: New York, 2004.
17. Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation (with Discussion). Journal of the American Statistical Association 1987; 82:528-550.
18. Gilks WR, Wild P. Adaptive rejection sampling for Gibbs sampling. Applied Statistics 1992; 41:337-348.
19. Ritter C, Tanner MA. Facilitating the Gibbs sampler: the Gibbs stopper and the Griddy-Gibbs sampler. Journal of the American Statistical Society 1992; 87:861-868.
20. Albert PS, Follmann DA. Modeling longitudinal count data subject to dropout. Biometrics 2000; 56:602-608.
21. Cowles MK, Carlin BP. Markov Chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 1996; 91:883-904.
22. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55.