The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Methods for the analysis of informatively censored longitudinal data
Abstract
This paper describes the problem of informative censoring in longitudinal studies where the primary outcome is rate of change in a continuous variable. Standard approaches based on the linear random effects model are valid only when the data are missing in a non‐ignorable fashion. Informative censoring, which is a special type of non‐ignorably missing data, occurs when the probability of early termination is related to an individual subject's true rate of change. When present, informative censoring causes bias in standard likelihood‐based analyses, as well as in weighted averages of individual least‐squares slopes. This paper reviews several methods proposed by others for analysis of informatively censored longitudinal data, and outlines a new approach based on a log‐normal survival model. Maximum likelihood estimates may be obtained via the EM algorithm. Advantages of this approach are that it allows general unbalanced data caused by staggered entry and unequally‐timed visits, it utilizes all available data, including data from patients with only a single measurement, and it provides a unified method for estimating all model parameters. Issues related to study design when informative censoring may occur are also discussed.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Unbalanced Repeated-Measures Models with Structured Covariance Matrices
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Self-citationNewton—Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
Lindstrom, M. J., & Bates, D. M. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478693 · Cited by 862
The modification of diet in renal disease study group
Beck, G. J., Berg, R. L., Coggins, C. H., Gassman, J. J., Hunsicker, L. G., Schluchter, M. D., & Williams, G. W. (1991) · Controlled Clinical Trials · doi:10.1016/0197-2456(91)90069-x · Cited by 818
Self-citationMissing data in longitudinal studies
Laird, N. M. (1988) · Statistics in Medicine · doi:10.1002/sim.4780070131 · Cited by 713
Wu, M. C., & Carroll, R. J. (1988) · Biometrics · doi:10.2307/2531905 · Cited by 536
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Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
Lindstrom, M. J., & Bates, D. M. (1988) · Journal of the American Statistical Association · doi:10.2307/2290128 · Cited by 232
Efficient Inference for Random-Coefficient Growth Curve Models with Unbalanced Data
Vonesh, E. F., & Carter, R. L. (1987) · Biometrics · doi:10.2307/2531999 · Cited by 149
Empirical Bayes Estimation of Rates in Longitudinal Studies
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Cited by
Klahr, S., Levey, A. S., Beck, G. J., Caggiula, A. W., Hunsicker, L., Kusek, J. W., & Striker, G. (1994) · New England Journal of Medicine · doi:10.1056/nejm199403313301301 · Cited by 2,328
Modeling the Drop-Out Mechanism in Repeated-Measures Studies
Little, R. J. A. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476615 · Cited by 1,522
Statistical Considerations in the Intent-to-Treat Principle
Lachin, J. M. (2000) · Controlled Clinical Trials · doi:10.1016/s0197-2456(00)00046-5 · Cited by 652
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Missing data methods in longitudinal studies: a review
Ibrahim, J. G., & Molenberghs, G. (2009) · TEST · doi:10.1007/s11749-009-0138-x · Cited by 512
Metadata
- Authors
- Schluchter, M. D.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1992-01
- Volume
- 11
- Issue
- 14-15
- Pages
- 1861-1870
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2147739992
- Cited by
- 287
- References
- 13
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- closed
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