Gaussianization-based quasi-imputation and expansion strategies for incomplete correlated binary responses

Statistics in Medicine

Volumn 26, Issue 4, 2007, Pages 782-799

  Demirtas, Hakan ^a   Hedeker, Donald ^a  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

Longitudinal data; Missing data; Multiple imputation; Non ignorable non response

Indexed keywords

ALGORITHM; ARTICLE; DATA ANALYSIS; LONGITUDINAL STUDY; MULTIVARIATE ANALYSIS; NORMAL DISTRIBUTION; OBESITY; STATISTICAL MODEL;

ADOLESCENT; ALGORITHMS; CHILD; CHILD, PRESCHOOL; COMPUTER SIMULATION; CORONARY DISEASE; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; LONGITUDINAL STUDIES; MALE; NORMAL DISTRIBUTION; OBESITY;

EID: 33846837287     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2560     Document Type: Article

References (35)

1. Little RJA, Rubin DB. Statistical Analysis with Missing Data (2nd edn). Wiley: New York, 2002.
2. Rubin DB. Formalizing subjective notions about the effect of non-respondents in sample surveys. Journal of the American Statistical Association 1977; 72:538-543.
3. Rubin DB. Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987.
4. Rubin DB. Multiple imputation after 18+ years (with discussion). Journal of the American Statistical Association 1996; 91:473-520.
5. Schafer JL. Analysis of Incomplete Multivariate Data. Chapman & Hall: London, 1997.
6. Schafer JL. PAN: Multiple Imputation for Multivariate Panel Data, Software Library for S-PLUS. Department of Statistics, The Pennsylvania State University, University Park, PA, 1997.
7. Schafer JL. Multiple imputation with PAN. In Sayer AG, Collins LM (eds). New Methods for the Analysis of Change. American Psychological Association: Washington, DC, 2001; 355-377.
8. Schafer JL, Yucel RM. Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics 2002; 11:437-457.
9. Liu M, Taylor JM, Belin TR. Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics 2000; 56:1157-1163.
10. Demirtas H, Schafer JL. On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine 2003; 22:2553-2575.
11. Demirtas H. Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out. Statistics in Medicine 2005; 24:2345-2363.
12. Thijs H, Molenberghs G, Michiels B, Verbeke G, Curran D. Strategies to fit pattern-mixture models. Biostatistics 2002; 3:245-265.
13. Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. Springer: New York, 2000.
14. van Buuren S, van Rijckevorsel JLA. Imputation of missing categorical data by maximizing internal consistency. Psychometrika 1992; 57:567-580.
15. van Buuren S, Oudshoom CGM. MICE V1.0 Users Guide. Leiden: TNO Preventie en Gezonheid, TNO/PG/VGZ 00.038, 2000.
16. Schimert J, Schafer JL, Hesterberg T, Fraley C, Clarkson DB. Analyzing Data with Missing Values in S-plus. Data Analysis Products Division, Insightful Corp.: Seattle, WA, 2001.
17. Gelfand AE, Hills SE, Racine-Poon A, Smith AFM. Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association 1990; 85:972-985.
18. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 1999; 18:681-694.
19. Lavori PW, Dawson R, Shera D. A multiple imputation strategy for clinical trials with truncation of patient data. Statistics in Medicine 1995; 14:1913-1925.
20. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41-55.
21. Allison PD. Multiple imputation for missing data: a cautionary tale. Sociological Methods and Research 2000; 28:301-309.
22. Emrich LJ, Piedmonte RP. A method for generating high-dimensional multivariate binary outcomes. American Statistician 1991; 45:302-304.
23. Dempster AP, Laird NM, Rubin DB. Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of Royal Statistical Society, Series B 1977; 39:1-38.
24. Tanner MA, Wong WH. The calculation of posterior distributions by data augmentation. Journal of American Statistical Association 1987; 82:528-540.
25. Demirtas H. Simulation-driven inferences for multiply imputed longitudinal datasets. Statistica Neerlandica 2004; 58:466-482.
26. Park GP, Park T, Shin DW. A simple method for generating correlated binary variates. American Statistician 1996; 50:306-310.
27. Collins LM, Schafer JL, Kam CH. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods 2001; 6:330-351.
28. McCullagh P, Nelder JA. Generalized Linear Models (2nd edn). Chapman & Hall: London, 1987.
29. Liang K, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
30. Zeger SL, Liang K. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986; 42: 121-130.
31. Woolson RF, Clarke WR. Analysis of categorical incomplete longitudinal data. Journal of the Royal Statistical Society, Series A 1984; 147:87-99.
32. Ekholm A, Skinner C. The Muscatine children's obesity data reanalysed using pattern mixture models. Applied Statistics 1998; 47:251-263.
33. Birmingham J, Fitzmaurice GM. A pattern-mixture model for longitudinal binary responses with nonignorable nonresponse. Biometrics 2002; 58:989-996.
34. Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis. Wiley: New Jersey, 2004.
35. Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. Springer: New York, 2005.