Regression models for mixed Poisson and continuous longitudinal data

Statistics in Medicine

Volumn 26, Issue 20, 2007, Pages 3782-3800

  Yang, Ying ^a   Kang, Jian ^a   Mao, Kai ^a   Zhang, Jie ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Correlation changing over time; Generalized estimating equation; Likelihood ratio test; Longitudinal data; Poisson responses

Indexed keywords

ARTICLE; COHORT ANALYSIS; CONTROLLED STUDY; CORRELATION COEFFICIENT; DATA ANALYSIS; HUMAN; INTERSTITIAL CYSTITIS; LINEAR REGRESSION ANALYSIS; LONGITUDINAL STUDY; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; REGRESSION ANALYSIS; STATISTICAL DISTRIBUTION;

CHINA; CYSTITIS, INTERSTITIAL; HUMANS; LIKELIHOOD FUNCTIONS; LONGITUDINAL STUDIES; POISSON DISTRIBUTION; REGRESSION ANALYSIS;

EID: 34548186666     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.2776     Document Type: Article

References (17)

1. Propert KJ, Schaeffer AJ, Brensinger CM, Kusek JW, Nyberg LM, Landis JR, The interstitial cystitis data base study group. A prospective study of interstitial cystitis: results of longitudinal followup of the interstitial cystitis data base cohort. The Journal of Urology 2000; 163:1434-1439.
2. Fitzmaurice GM, Laird NM. Regression models for a bivariate discrete and continuous outcome with clustering. Journal of the American Statistical Association 1995; 90:845-852.
3. Olkin I, Tare RF. Multivariate correlation models with mixed discrete and continuous variables. Annals of Mathematical Statistics 1961; 32:448-465 (correction in 36:343-344).
4. Little RJA, Schluchter MD. Maximum likelihood estimation for mixed continuous and categorical data with missing values. Biometrika 1985; 72:497-512.
5. Little RJA, Rubin DB. Statistical Analysis with Missing Data. Wiley: New York, 1987.
6. Regan MM, Catalano PJ. Likelihood models for clustered binary and continuous outcomes: application to developmental toxicology. Biometrics 1999; 55:760-768.
7. Cox DR, Reid N. Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society, Series B 1987; 49:1-39 (with discussion).
8. Cox DR, Reid N. On the stability of maximum-likelihood estimators of orthogonal parameters. Canadian Journal of Statistics 1989; 17:229-233.
9. Liang KY, Zeger SL, Qaqish BF. Multivariate regression analyses for categorical data. Journal of the Royal Statistical Society, Series B 1992; 54:3-40.
10. Zhang J, Boos DD. Bootstrap critical values for testing homogeneity of covariance matrices. Journal of the American Statistical Association 1992; 87(418):425-429.
11. Rotnitzky A, Jewell NP. Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data. Biometrika 1990; 77:485-497.
12. Hastie T, Tibshirani R. Varying-coefficient models. Journal of the Royal Statistical Society, Series B 1993; 55:757-796 (with discussion).
13. Shao J. Mathematical Statistics. Springer: Berlin, 1999.
14. Kirkemo A, Peabody M, Diolmo AC et al. Associations among urodynamic lindings and symptoms in women enrolled in the interstitial cystitis data base (ICDB) study. Urology 1997; 49(5A Suppl.):76-80.
15. Messing E, Pauk D, Schaeffer A et al. Associations among cystoscopic findings and symptoms and physical examination findings in women enrolled in the interstitial cystitis data base (ICDB) study. Urology 1997; 49(5A Suppl.):81-85.
16. McCullagh P, Nelder JA. Generalized Linear Models. Chapman & Hall: London, 1983; 127-148.
17. Fieuws S, Verbeke G. Joint modelling of multivariate longitudinal profiles: pitfalls of the random-effects approach. Statistics in Medicine 2004; 23:3093-3104.