Marginalized models for longitudinal ordinal data with application to quality of life studies

Statistics in Medicine

Volumn 27, Issue 21, 2008, Pages 4359-4380

  Lee, Keunbaik ^a   Daniels, Michael J ^b,c  

^a LOUISIANA STATE UNIVERSITY HEALTH SCIENCES CENTER   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

^c University of Florida   (United States)

Author keywords

Dropout; Marginalized likelihood based models; Ordinal data models

Indexed keywords

ANTINEOPLASTIC AGENT; FLUOROURACIL; FOLINIC ACID; IRINOTECAN; OXALIPLATIN;

ALGORITHM; ARTICLE; CANCER GROWTH; CANCER MORTALITY; CANCER SURVIVAL; CANCER SURVIVOR; CLINICAL TRIAL; COLORECTAL CANCER; COMBINATION CHEMOTHERAPY; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; HUMAN; LINEAR SYSTEM; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; PATIENT PARTICIPATION; QUALITY OF LIFE; RANDOMIZED CONTROLLED TRIAL; REGRESSION ANALYSIS; SIMULATION; SURVIVAL TIME;

ANTINEOPLASTIC AGENTS; COLORECTAL NEOPLASMS; COMPUTER SIMULATION; HUMANS; LINEAR MODELS; LONGITUDINAL STUDIES; MODELS, BIOLOGICAL; PATIENT DROPOUTS; QUALITY OF LIFE;

EID: 50849089293     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3352     Document Type: Article

References (44)

1. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
2. Fitzmaurice GM, Laird NM. A likelihood-based method for analysing longitudinal binary responses. Biometrika 1993; 80:141-151.
3. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44:1049-1060.
4. Zeger SL, Karin MR. Generalized linear model with random effects: a Gibbs sampling approach. Journal of the American Statistical Association 1991; 86:79-86.
5. Breslow NE, Clayton DG. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 1993; 88:125-134.
6. Laird NM. Missing data in longitudinal studies. Statistics in Medicine 1988; 7:305-315.
7. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974; 19:716-723.
8. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 1978; 6:461-464.
9. Heagerty PJ. Marginally specified logistic-normal models for longitudinal binary data. Biometrics 1999; 55: 688-698.
10. Heagerty PJ. Marginalized transition models and likelihood inference for longitudinal categorical data. Biometrics 2002; 58:342-351.
11. Heagerty PJ, Zeger SL. Marginalized multilevel models and likelihood inference (with Discussion). Statistical Science 2000; 15:1-26.
12. Miglioretti D, Heagerty P. Marginal modeling of multilevel binary data with time-varying covariates. Biostatistics 2004; 5:381-398.
13. Lee K, Daniels M. A class of Markov models for longitudinal ordinal data. Biometrics 2007; 63:1060-1067.
14. Dale JR. Global cross-ratio models for bivariate, discrete, ordered responses. Biometrics 1986; 42:909-917.
15. Molenberghs G, Lesaffre E. Marginal modeling of correlated ordinal data using a multivariate Plackett distribution. Journal of the American Statistical Association 1994; 89:633-644.
16. Williamson JM, Kim K, Lipsitz SR. Analyzing bivariate ordinal data using a global odds ratio. Journal of the American Statistical Association 1995; 90:1432-1437.
17. Heagerty PJ, Zeger SL. Marginal regression models for clustered ordinal measurements. Journal of the American Statistical Association 1996; 91:1024-1036.
18. Gibbons R, Hedeker D. Random effects probit and logistic regression models for three-level data. Biometrics 1997; 53:1527-1537.
19. Todem D, Kim K, Lesaffre E. Latent-variable models for longitudinal data with bivariate ordinal outcomes. Statistics in Medicine 2006; 26:1034-1054.
20. Liu LC, Hedeker D. A mixed-effects regression model for longitudinal multivariate ordinal data. Biometrics 2006; 62:261-268.
21. Liu I, Agresti A. The analysis of ordered categorical data: an overview and a survey of recent developments. Test 2005; 14:1-73.
22. Hogan JW, Roy J, Korkontzelou C. Tutorial in biostatistics handling drop-out in longitudinal studies. Statistics in Medicine 2004; 23:1455-1497.
23. Hogan JW, Laird NM. Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 1997; 16:239-257.
24. Pauler DK, McCoy S, Moinpour C. Pattern mixture models for longitudinal quality of life studies in advanced stage disease. Statistics in Medicine 2003; 22:795-809.
25. Kurland BF, Heagerty PJ. Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths. Biostatistics 2005; 6:241-258.
26. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics 2002; 58:21-29.
27. Egleston H, Scharfstein DO, Freeman ER, West SK. Causal inference for non-mortality outcomes in the presence of death. Biostatistics 2007; 8:526-545.
28. Rubin DB. Comment on causal inference without counterfactuals by A. P. Dawid. Journal of the American Statistical Association 2000; 6:34-58.
29. Hayden D, Pauler DK, Schoenfeld D. An estimator for treatment comparisons among survivors in randomized trials. Biometrics 2005; 61:305-310.
30. Rubin DB. Causal inference through potential outcomes and principal stratification: application to studies with 'censoring' due to death. Statistical Science 2006; 21:299-309.
31. Goldberg RM, Sargent DJ, Morton RF, Fuchs CS, Ramanathan RK, Williamson SK, Findlay BP, Pitot HC, Albets SR. A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. Journal of Clinical Oncology 2004; 22:23-30.
32. McCullagh P. Regression models for ordinal data. Journal of the Royal Statistical Society, Series B 1980; 42:109-142.
33. Heagerty PJ, Kurland BF. Misspecified maximum likelihood estimates and generalised linear mixed models. Biometrika 2001; 88:973-985.
34. Gibbons R, Bock R. Trend in correlated proportions. Psychometrika 1987; 52:113-124.
35. Hedeker D, Gibbons RD. A random-effects ordinal regression model for multilevel analysis. Biometrics 1994; 50:933-944.
36. Liu Q, Pierce DA. A note on Gauss-Hermite quadrature. Biometrika 1994; 81:624-629.
37. Pinheiro JC, Bates DM. Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics 1995; 4:12-35.
38. Fitzmaurice GM, Laird NM. Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies. Biostatistics 2000; 1:141-156.
39. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66:688-701.
40. Rubin DB. Bayesian inference for causal effects. Annals of Statistics 1978; 6:34-58.
41. Holland P. Statistics and causal inference. Journal of the American Statistical Association 1986; 81:945-961.
42. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90:106-121.
43. Schildcrout J, Heagerty PJ. Marginalized models for moderate to long series of longitudinal binary response data. Biometrics 2007; 63:322-331.
44. Ilk O, Daniels M. Marginalized transition random effects models for multivariate longitudinal binary data. Canadian Journal of Statistics 2007; 35:105-123.