A high-dimensional joint model for longitudinal outcomes of different nature

Statistics in Medicine

Volumn 27, Issue 22, 2008, Pages 4408-4427

  Faes, Christel ^a   Aerts, Marc ^a   Molenberghs, Geert ^a   Geys, Helena ^b   Teuns, Greet ^b   Bijnens, Luc ^b  

^a HASSELT UNIVERSITY   (Belgium)

^b JOHNSON AND JOHNSON   (United States)

Author keywords

High dimensional joint model; Longitudinal data; Mixed outcomes; Pseudo likelihood

Indexed keywords

CHEMICAL COMPOUND; PSYCHOTROPIC AGENT;

ANALYTIC METHOD; ANIMAL BEHAVIOR; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DOSE RESPONSE; DRUG RESPONSE; EXPERIMENTAL STUDY; GRIP STRENGTH; LOCOMOTION; LONGITUDINAL STUDY; MALE; MATHEMATICAL COMPUTING; METHODOLOGY; NEUROTOXICITY; NONHUMAN; OUTCOMES RESEARCH; PUPIL; RAT; REFLEX; REPEATED DRUG DOSE; RISK ASSESSMENT; STATISTICAL MODEL; TEMPERATURE; TOXICOLOGY; VOCALIZATION;

ANIMALS; DOSE-RESPONSE RELATIONSHIP, DRUG; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; RATS; TOXICITY TESTS;

EID: 53349174434     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3314     Document Type: Article

References (24)

1. Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis (5th edn). Prentice-Hall: Englewood Cliffs, NJ, 2002.
2. Aerts M, Geys H, Molenberghs G, Ryan L. Topics in Modelling of Clustered Data. Chapman & Hall: London, 2002.
3. Tate RF. Correlation between a discrete and a continuous variable. Annals of Mathematical Statistics 1954; 25:603-607.
4. Olkin I, Tate RF. Multivariate correlation models with mixed discrete and continuous variables. Annals of Mathematical Statistics 1961; 32:448-465 (with correction in 36:343-344).
5. Little RJA, Schluchter MD. Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika 1985; 72:497-512.
6. Krzanowski WJ. Principles of Multivariate Analysis. Clarendon Press: Oxford, 1988.
7. Cox DR, Wermuth N. Response models for mixed binary and quantitative variables. Biometrika 1992; 79:441-461.
8. Cox DR, Wermuth N. A note on the quadratic exponential binary distribution. Biometrika 1994; 81:403-408.
9. Geys H, Regan M, Catalano P, Molenberghs G. Two latent variable risk assessment approaches for mixed continuous and discrete outcomes from developmental toxicity data. Journal of Agricultural Biological and Environment Statistics 2001; 6:340-355.
10. Catalano PJ, Ryan LM. Bivariate latent variable models for clustered discrete and continuous outcomes. Journal of the American Statistical Association 1992; 87:651-658.
11. Molenberghs G, Geys H, Buyse M. Evaluations of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes. Statistics in Medicine 2001; 20:3023-3038.
12. Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. Springer Series in Statistics. Springer: New York, 2005.
13. Fieuws S, Verbeke G. Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics 2006; 62:424-431.
14. Diggle PJ, Heagerty P, Liang KY, Zeger SL. Analysis of Longitudinal Data. Clarendon Press: Oxford, 2002.
15. Arnold BC, Strauss D. Pseudolikelihood estimation: some examples. Sankhya B 1991; 53:233-243.
16. Faes C, Geys H, Aerts M, Molenberghs G, Catalano P. Modelling combined continuous and ordinal outcomes in a clustered setting. Journal of Agricultural, Biological and Environmental Statistics 2004; 9:515-530.
17. Irwin S. Comprehensive observational assessment. Ia. A systematic, quantitative procedure for assessing the behavioral and physiologic state of the mouse. Physiopharmacologia 1964; 13:222-257.
18. Laenen A, Vangeneugden T, Geys H, Molenberghs G. Generalized reliability estimation using repeated measurements. British Journal of Mathematical and Statistical Psychology 2006; 59:113-131.
19. Geys H, Molenberghs G, Ryan L. Pseudo-likelihood inference for clustered binary data. Communications in Statistics: Theory and Methods 1997; 26:2743-2767.
20. Molenberghs G, Kenward MG. Missing Data in Clinical Studies. Wiley: Chichester, 2007.
21. Rubin DB. Inference and missing data (with discussion). Biometrika 1976; 63:581-592.
22. Liang KY, Zeger S. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.
23. Robins JM, Romitzky A, Scharfstein DO. Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the American Statistical Association 1998; 93:1321-1339.
24. 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.