Estimating multilevel logistic regression models when the number of clusters is low: A comparison of different statistical software procedures

International Journal of Biostatistics

Volumn 6, Issue 1, 2010, Pages

  Austin, Peter C ^a  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

Author keywords

Bayesian analysis; BUGS; Generalized linear mixed models; Hierarchical models; Mixed effects model; Monte Carlo simulations; Multilevel models; R; Random effects model; SAS; Stata; Statistical software

Indexed keywords

ACCURACY; ARTICLE; BAYES THEOREM; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER PROGRAM; MONTE CARLO METHOD; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; NORMAL DISTRIBUTION; PERFORMANCE; QUALITATIVE ANALYSIS; QUASI EXPERIMENTAL STUDY; STATISTICAL MODEL; HUMAN; STATISTICAL ANALYSIS;

BAYES THEOREM; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; HUMANS; LINEAR MODELS; LOGISTIC MODELS; MODELS, STATISTICAL; MONTE CARLO METHOD; SOFTWARE;

EID: 77952026461     PISSN: 15574679     EISSN: None     Source Type: Journal    
DOI: 10.2202/1557-4679.1195     Document Type: Article

References (19)

1. Austin PC, Goel V, van Walraven C (2001). An Introduction to Multilevel Regression Models. Canadian Journal of Public Health. 92:150-154. (Pubitemid 32440329)
2. Austin PC, Manca A, Zwarenstein M, Juurlink DN, Stanbrook MB (2010). A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals. Journal of Clinical Epidemiology. 63:142-153. DOI:10.1016/j.jclinepi. 2009.06.002.
3. Breslow NE, Clayton DG (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association. 88:9-25.
4. Browne WJ, Draper D (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis. 1:473-514.
5. Bryk AS, Raudenbush SW, Congdon RT Jr (1996). HLM: Hierarchical Linear and Nonlinear Modeling with the HLM/2L and HLM/3L Programs. Chicago, IL: Scientific Software International Inc.
6. Geweke J (1994). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In: Bayesian Statistics 4 (editors: Bernardo JM, Berger JO, Dawid AP, Smith AFM), pp. 169-193. Oxford: Clarendon Press.
7. Gilks WR, Thomas A, Spiegelhalter DJ (1994). A language and program for complex Bayesian modelling. The Statistician. 43:169-78
8. Gilks WR, Richardson S, Spiegelhalter DJ (1996). Introducing Markov chain Monte Carlo. In: Markov chain Monte Carlo in practice (editors: Gilks WR, Richardson S, Spiegelhalter DJ), pp. 1-19. London: Chapman & Hall.
9. Goldstein H (1995). Multilevel Statistical Models, second edition. London: Edward Arnold.
10. Kreft I, de Leeuw J (1998). Introduction to Multilevel Modeling. Thousand Oaks, CA: Sage Publications Inc.
11. Liu Q, Pierce DA (1994). A note on Gauss-Hermite Quadrature. Biometrika. 81:624-629.
12. Pinheiro JC, Bates DM (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical Statistics. 4:12-35.
13. Schall R (1991). Estimation in generalized linear models with random effects. Biometrika. 78:719-727.
14. R Core Development Team (2005). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
15. Raudenbush SW (2008). Many small groups. In: Handbook of Multilevel Analysis (editors: de Leeuw J, Meijer E), pp. 207-236. New York, NY: Springer.
16. Raudenbush SW, Bryk as (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, second edition. Thousand Oaks, CA: Sage Publications Inc.
17. Rodriquez G, Goldman N (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A. 158:73-89.
18. Snijders T, Boskers R (1999). Multilevel Analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage Publications Inc.
19. Wolfinger R, O'Connell M (1993). Generalized linear mixed models: A pseudo-likelihood approach. Journal of Statistical Computation and Simulation. 48:223-243. (Pubitemid 24825198)