A joint modeling approach to data with informative cluster size: Robustness to the cluster size model

Statistics in Medicine

Volumn 30, Issue 15, 2011, Pages 1825-1836

  Chen, Zhen ^a   Zhang, Bo ^a   Albert, Paul S ^a  

^a EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

Author keywords

Developmental toxicity; Informative number of observations; Non ignorable dropout; Random cluster size; Shared parameter models

Indexed keywords

ETHYLENE GLYCOL;

ARTICLE; CLUSTER ANALYSIS; LINEAR SYSTEM; NONHUMAN; SAMPLING ERROR; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; STATISTICAL PARAMETERS; TOXICITY TESTING;

ABNORMALITIES, DRUG-INDUCED; ANIMALS; BIAS (EPIDEMIOLOGY); BIOMEDICAL RESEARCH; CLUSTER ANALYSIS; EPIDEMIOLOGIC RESEARCH DESIGN; ETHYLENE GLYCOL; FEMALE; FOLLOW-UP STUDIES; LINEAR MODELS; MICE; ORGANOGENESIS; PREGNANCY; PREGNANCY OUTCOME; SAMPLE SIZE;

EID: 79958760327     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4239     Document Type: Article

References (22)

1. Price CJ, Kimmel CA, Tyl RW, Marr MC. The developmental toxicity of ethylene-glycol in rats and mice. Toxicology and Applied Pharmacology 1985; 81(1):113-127.
2. Gansky SA, Weintraub JA, Shain S. Multi-pied Investigators. Parental periodontal predictors of oral health in adult children of a community cohort. Journal of Dental Research 1998; 77:607.
3. Ecochard R, Clayton DG. Multi-level modelling of conception in artificial insemination by donor. Statistics in Medicine 1998; 17(10):1137-1156.
4. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38(4):963-974.
5. Little R. Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 1995; 90(431):1112-1121.
6. Mori M, Woolson RF, Woodworth GG. Slope estimation in the presence of informative right censoring-modeling the number of observations as a geometric random variable. Biometrics 1994; 50(1):39-50.
7. Dunson DB, Chen Z, Harry J. A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes. Biometrics 2003; 59(3):521-530.
8. Jacqmin-Gadda H, Sibillot S, Proust C, Molina JM, Thiebaut R. Robustness of the linear mixed model to misspecified error distribution. Computational Statistics and Data Analysis 2007; 51(10):5142-5154.
9. Butler SM, Louis TA. Random effects models with nonparametric priors. Statistics in Medicine 1992; 11(14-15):1981-2000.
10. Verbeke G, Lesaffre E. The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data. Computational Statistics and Data Analysis 1997; 23(4):541-556.
11. Zhang DW, Davidian M. Linear mixed models with flexible distributions of random effects for longitudinal data. Biometrics 2001; 57(3):795-802.
12. Maas CJM, Hox JJ. Robustness issues in multilevel regression analysis. Statistica Neerlandica 2004; 58(2):127-137.
13. Song X, Davidian M, Tsiatis AA. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics 2002; 58(4):742-753.
14. Hsieh F, Tseng YK, Wang JL. Joint modeling of survival and longitudinal data: likelihood approach revisited. Biometrics 2006; 62(4):1037-1043.
15. Pantazis N, Touloumi G. Robustness of a parametric model for informatively censored bivariate longitudinal data under misspecification of its distributional assumptions: a simulation study. Statistics in Medicine 2007; 26(30):5473-5485.
16. Rizopoulos D, Verbeke G, Molenberghs G. Shared parameter models under random effects misspecification. Biometrika 2008; 95(1):63-74.
17. White H. Maximum-likelihood estimation of misspecified dynamic-models. Lecture Notes in Economics and Mathematical Systems 1984; 237:1-19.
18. Pinhero JC, Bates DM. Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics 1995; 4(1):12-35.
19. Gueorguieva RV. Comments about joint modeling of cluster size and binary and continuous subunit-specific outcomes. Biometrics 2005; 61(3):862-866.
20. Hoffman EB, Sen PK, Weinberg CR. Within-cluster resampling. Biometrika 2001; 88(4):1121-1134.
21. Follmann D, Proschan M, Leifer E. Multiple outputation: Inference for complex clustered data by averaging analyses from independent data. Biometrics 2003; 59(2):420-429.
22. Williamson JM, Datta S, Satten GA. Marginal analyses of clustered data when cluster size is informative. Biometrics 2003; 59(1):36-42.