The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
Abstract
The purpose of this article is to model the progression of CD4-lymphocyte count and the relationship between different features of this progression and survival time. The complicating factors in this analysis are that the CD4-lymphocyte count is observed only at certain fixed times and with a high degree of measurement error, and that the length of the vector of observations is determined, in part, by the length of survival. If probability of death depends on the true, unobserved CD4-lymphocyte count, then the survival process must be modelled. Wu and Carroll (1988, Biometrics 44, 175-188) proposed a random effects model for two-sample longitudinal data in the presence of informative censoring, in which the individual effects included only slopes and intercepts. We propose methods for fitting a broad class of models of this type, in which both the repeated CD4-lymphocyte counts and the survival time are modelled using random effects. These methods permit us to estimate parameters describing the progression of CD4-lymphocyte count as well as the effect of differences in the CD4 trajectory on survival. We apply these methods to results of AIDS clinical trials.
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Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Harville, D. A. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480998 · Cited by 2,667
Cited by
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
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
Criteria for the Validation of Surrogate Endpoints in Randomized Experiments
Buyse, M., & Molenberghs, G. (1998) · Biometrics · doi:10.2307/2533853 · Cited by 425
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
Shared Frailty Models for Recurrent Events and a Terminal Event
Liu, L., Wolfe, R. A., & Huang, X. (2004) · Biometrics · doi:10.1111/j.0006-341x.2004.00225.x · Cited by 342
Metadata
- Authors
- Gruttola, V. D., & Tu, X. M.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1994-12
- Volume
- 50
- Issue
- 4
- Pages
- 1003
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2007196674
- Cited by
- 384
- References
- 4
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
