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2014 | 2 | 1 |
Tytuł artykułu

Some New Random Effect Models for Correlated Binary Responses

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Exchangeable copulas are used to model an extra-binomial variation in Bernoulli experiments with a variable number of trials. Maximum likelihood inference procedures for the intra-cluster correlation are constructed for several copula families. The selection of a particular model is carried out using the Akaike information criterion (AIC). Profile likelihood confidence intervals for the intra-cluster correlation are constructed and their performance are assessed in a simulation experiment. The sensitivity of the inference to the specification of the copula family is also investigated through simulations. Numerical examples are presented.
Wydawca
Czasopismo
Rocznik
Tom
2
Numer
1
Opis fizyczny
Daty
otrzymano
2014-05-12
zaakceptowano
2014-10-16
online
2014-12-01
Twórcy
  • Department of Mathematics and Statistics, Université Laval„ 1045 av. de la
    Médecine, Québec (Québec) G1V 0A6 Canada
  • Department of Mathematics and Statistics, Université Laval„ 1045 av. de la
    Médecine, Québec (Québec) G1V 0A6 Canada
Bibliografia
  • [1] Ahmed, M. and Shoukri, M. (2010). A bayesian estimator of the intracluster correlation coefficient from correlated binaryresponses. J. Data Sci., 8:127–137.
  • [2] Alanko, T. and Duffy, J. C. (1996). Compound binomial distribution for modeling consumption data. The Statistician, 45:269–286.[Crossref]
  • [3] Ananth, C. V. and Preisser, J. S. (1999). Bivariate logistic regression: Modeling the association of small for gestational agebirths in twin gestations. Stat. Med., 18:2011–2023.[Crossref]
  • [4] Chakraborty, H., Moore, J., Carlo, W. A., Hartwell, T. D., and Wright, L. L. (2009). A simulation based technique to estimateintracluster correlation for a binary variable. Contemporary Clinical Trials, 30:71–80.[WoS][Crossref][PubMed]
  • [5] Eldridge, S. and Kerry, S. (2012). A Practical Guide to Cluster Randomised Trials in Health Services Research. Wiley, New York.
  • [6] Feng, Z. and Crizzle, J. E. (1992). Correlated binomial variates: Properties of estimators of intraclass correlation and its effecton sample size calculation. Stat. Med., 11:1600–1614.
  • [7] Fleiss, J. L. and Cuzick, J. (1979). The reliability of dichotomous judgments: Unequal numbers of judges per subject. Appl.Psychol. Meas., 3:537–542.[Crossref]
  • [8] Kuk, A. Y. C. (2004). A generalized estimating equation approach to modelling foetal response in developmental toxicitystudies when the number of implants is dose dependent. J. Roy. Statist. Soc. Ser. C, 52:52–61.
  • [9] Légaré, F., Labrecque, M., LeBlanc, A., Njoya, M., Laurier, C., Côté, L., Godin, G., Thivierge, R. L., O’Connor, A., and S., S.-J.(2011). Training family physicians in shared decision making for the use of antibiotics for acute respiratory infections: a pilotclustered randomized controlled trial. Health Expect., 1:96–110.[WoS][Crossref]
  • [10] Liang, K. Y., Qaqish, B., and Zeger, S. (1992). Multivariate regression analyses for categorical data. J. Roy. Statist. Soc. Ser.B., 54:3–40.
  • [11] Lipsitz, S. R., Laird, N. M., and Harrington, D. P. (1991). Generalized estimating equations for correlated binary data: usingthe odds ratio as a measure of association. Biometrika, 78:153–160.[Crossref]
  • [12] Madsen, R. W. (1993). Generalized binomial distribution. Comm. Statist. Theory Methods, 22:3065–3086.[Crossref]
  • [13] Mai, J.-M. and Scherer, M. (2012). Simulating Copulas; Stochastic Models, Sampling Algorithms and Applications. Series inQuantitative Finance: Volume 4. World Scientific Publishing Company, New York.[WoS]
  • [14] Nikoloulopoulos, A. K. and Karlis, D. (2008). Multivariate logit copula model with an application to dental data. Stat. Med.,27:6393–6406.[PubMed][WoS][Crossref]
  • [15] Ochi, Y. and Prentice, R. L. (1984). Likelihood inference in a correlated probit regression model. Biometrika, 71:531–542.[Crossref]
  • [16] Pals, S. L., Beaty, B. L., Posner, S. F., and Bull, S. (2009). Estimates of intraclass correlation for variables related to behavioralhiv/std prevention in a predominantly african american and hispanic sample of young women. Health Education & Behavior,36:182–194.[WoS]
  • [17] Ridout, M. S., Demétrio, C. G. B., and Firth, D. (1999). Estimating intraclass correlation for binary data. Biometrics, 55:137–148.[PubMed][Crossref]
  • [18] Saha, K. K. (2012). Profile likelihood-based confidence interval of the intraclass correlation for binary outcome data sampledfrom clusters. Stat. Med., 31:3982–4002.[WoS][Crossref][PubMed]
  • [19] Shoukri, M. M., Kumar, P., and Colak, D. (2011). Analyzing dependent proportions in cluster randomized trials: Modelinginter-cluster correlation via copula function. Comput. Statist. Data Anal., 55:1226–1235.[WoS][Crossref]
  • [20] Stefanescu, C. and Turnbull, B. W. (2003). Likelihood inference for exchangeable binary data with varying cluster sizes.Biometrics, 59:18–24.[Crossref][PubMed]
  • [21] Turner, R. M., Omar, R. Z., and Thompson, S. G. (2001). Bayesian methods of analysis for cluster randomized trials withbinary outcome data. Stat. Med., 20:453–472.[Crossref][PubMed]
  • [22] Turner, R. M., Omar, R. Z., and Thompson, S. G. (2006). Constructing intervals for the intracluster correlation coefficientusing bayesian modelling, and application in cluster randomized trials. Stat. Med., 25:1443–1456.[PubMed][Crossref]
  • [23] Williams, D. A. (1975). The analysis of binary response from toxicological experiments involving reproduction and teratogenicity.Biometrics, 31:949–952.[Crossref][PubMed]
  • [24] Williams, D. A. (1982). Extra-binomial variation in logistic linear models. J. Appl. Statist., 31:305–309.
  • [25] Zou, G. and Donner A., (2004). Confidence interval estimation of the intraclass correlation coefficient for binary outcomedata. Biometrics., 60:807–811.[PubMed][Crossref]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_2478_demo-2014-0006
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