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A likelihood-based method for analysing longitudinal binary responses
Abstract
In this paper, we discuss a likelihood-based method for analysing correlated binary responses based on a multivariate model. It is related to the pseudo-maximum likelihood approach suggested recently by Zhao & Prentice (1990). Their parameterization results in a simple pairwise model, in which the association between responses is modelled in terms of correlations, while the present paper uses conditional log odds-ratios. With this approach, higher-order associations can be incorporated in a natural way. One important advantage of this parameterization is that the maximum likelihood estimates of the marginal mean parameters are robust to misspecification of the time dependence. We describe an iterative two-stage procedure for obtaining the maximum likelihood estimates. Two examples are presented to illustrate this methodology.
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Cites
Deming, W. E., & Stephan, F. F. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731829 · Cited by 1,537
Cited by
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Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Analysis of multivariate probit models
Chib, S. (1998) · Biometrika · doi:10.1093/biomet/85.2.347 · Cited by 834
The validation of surrogate endpoints in meta-analyses of randomized experiments
Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000) · Biostatistics · doi:10.1093/biostatistics/1.1.49 · Cited by 624
Modelling multivariate binary data with alternating logistic regressions
CAREY, V., ZEGER, S. L., & DIGGLE, P. (1993) · Biometrika · doi:10.1093/biomet/80.3.517 · Cited by 514
Handling drop‐out in longitudinal studies
Hogan, J. W., Roy, J., & Korkontzelou, C. (2004) · Statistics in Medicine · doi:10.1002/sim.1728 · Cited by 404
Metadata
- Authors
- FITZMAURICE, G. M., & LAIRD, N. M.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1993
- Volume
- 80
- Issue
- 1
- Pages
- 141-151
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W1999154462
- Cited by
- 328
- References
- 1
- FWCI
- 13.43
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
