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Empirical Estimation of a Distribution Function with Truncated and Doubly Interval-Censored Data and Its Application to AIDS Studies

Jianguo Sun
Biometrics
Vol. 51, No. 3 (Sep., 1995), pp. 1096-1104
DOI: 10.2307/2533008
Stable URL: http://www.jstor.org/stable/2533008
Page Count: 9
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Empirical Estimation of a Distribution Function with Truncated and Doubly Interval-Censored Data and Its Application to AIDS Studies
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Abstract

In this paper we discuss the non-parametric estimation of a distribution function based on incomplete data for which the measurement origin of a survival time or the date of enrollment in a study is known only to belong to an interval. Also the survival time of interest itself is observed from a truncated distribution and is known only to lie in an interval. To estimate the distribution function, a simple self-consistency algorithm, a generalization of Turnbull's (1976, Journal of the Royal Statistical Association, Series B 38, 290-295) self-consistency algorithm, is proposed. This method is then used to analyze two AIDS cohort studies, for which direct use of the EM algorithm (Dempster, Laird and Rubin, 1976, Journal of the Royal Statistical Association, Series B 39, 1-38), which is computationally complicated, has previously been the usual method of the analysis.

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