Marginally specified logistic-normal models for longitudinal binary data

Biometrics
Patrick J Heagerty

Abstract

Likelihood-based inference for longitudinal binary data can be obtained using a generalized linear mixed model (Breslow, N. and Clayton, D. G., 1993, Journal of the American Statistical Association 88, 9-25; Wolfinger, R. and O'Connell, M., 1993, Journal of Statistical Computation and Simulation 48, 233-243), given the recent improvements in computational approaches. Alternatively, Fitzmaurice and Laird (1993, Biometrika 80, 141-151), Molenberghs and Lesaffre (1994, Journal of the American Statistical Association 89, 633-644), and Heagerty and Zeger (1996, Journal of the American Statistical Association 91, 1024-1036) have developed a likelihood-based inference that adopts a marginal mean regression parameter and completes full specification of the joint multivariate distribution through either canonical and/or marginal higher moment assumptions. Each of these marginal approaches is computationally intense and currently limited to small cluster sizes. In this manuscript, an alternative parameterization of the logistic-normal random effects model is adopted, and both likelihood and estimating equation approaches to parameter estimation are studied. A key feature of the proposed approach is that marginal regression parameters are...Continue Reading

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Citations

Mar 29, 2008·Biostatistics·Jonathan S Schildcrout, Patrick J Heagerty
Feb 1, 2013·Biostatistics·Keunbaik LeeYongsung Joo
Jun 23, 2010·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·John FiebergJacqueline L Frair
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