Analyzing doubly censored data with covariates, with application to AIDS

M. Y. Kim; V. G. De Gruttola; S. W. Lagakos

doi:10.2307/2532598

Analyzing doubly censored data with covariates, with application to AIDS

M. Y. Kim, V. G. De Gruttola, S. W. Lagakos

Research output: Contribution to journal › Article › peer-review

92 Scopus citations

Abstract

This paper proposes a method for incorporating covariate information in the analysis of survival data when both the time of the originating event and the failure event can be right- or interval-censored. This method generalizes the one-sample estimation results of De Gruttola and Lagakos by allowing the distribution of time between the two events to be a function of covariates under a proportional hazards model. Estimates for the model coefficients, as well as the underlying distributions, are obtained by an iterative fitting procedure based on Turnbull's self-consistency algorithm in combination with the Newton-Raphson algorithm. The method is illustrated with data from a study of hemophiliacs infected with the human immunodeficiency virus.

Original language	English (US)
Pages (from-to)	13-22
Number of pages	10
Journal	Biometrics
Volume	49
Issue number	1
DOIs	https://doi.org/10.2307/2532598
State	Published - 1993
Externally published	Yes

Keywords

AIDS
Interval censoring
Proportional hazards
Self-consistency

ASJC Scopus subject areas

Statistics and Probability
Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Agricultural and Biological Sciences(all)
Applied Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2307/2532598

Cite this

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abstract = "This paper proposes a method for incorporating covariate information in the analysis of survival data when both the time of the originating event and the failure event can be right- or interval-censored. This method generalizes the one-sample estimation results of De Gruttola and Lagakos by allowing the distribution of time between the two events to be a function of covariates under a proportional hazards model. Estimates for the model coefficients, as well as the underlying distributions, are obtained by an iterative fitting procedure based on Turnbull's self-consistency algorithm in combination with the Newton-Raphson algorithm. The method is illustrated with data from a study of hemophiliacs infected with the human immunodeficiency virus.",

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doi = "10.2307/2532598",

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AU - Kim, M. Y.

AU - De Gruttola, V. G.

AU - Lagakos, S. W.

PY - 1993

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N2 - This paper proposes a method for incorporating covariate information in the analysis of survival data when both the time of the originating event and the failure event can be right- or interval-censored. This method generalizes the one-sample estimation results of De Gruttola and Lagakos by allowing the distribution of time between the two events to be a function of covariates under a proportional hazards model. Estimates for the model coefficients, as well as the underlying distributions, are obtained by an iterative fitting procedure based on Turnbull's self-consistency algorithm in combination with the Newton-Raphson algorithm. The method is illustrated with data from a study of hemophiliacs infected with the human immunodeficiency virus.

AB - This paper proposes a method for incorporating covariate information in the analysis of survival data when both the time of the originating event and the failure event can be right- or interval-censored. This method generalizes the one-sample estimation results of De Gruttola and Lagakos by allowing the distribution of time between the two events to be a function of covariates under a proportional hazards model. Estimates for the model coefficients, as well as the underlying distributions, are obtained by an iterative fitting procedure based on Turnbull's self-consistency algorithm in combination with the Newton-Raphson algorithm. The method is illustrated with data from a study of hemophiliacs infected with the human immunodeficiency virus.

KW - AIDS

KW - Interval censoring

KW - Proportional hazards

KW - Self-consistency

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Analyzing doubly censored data with covariates, with application to AIDS

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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