Estimating the extent of tracking in interval-censored chain-of-events data

Biometrics
G A Satten

Abstract

This paper describes a method for determining whether the times between a chain of successive events (which all individuals experience in the same order) are correlated, for data in which the exact event times are not observed. Such data arise when individuals are only observed occasionally to determine which events have occurred. In such data, the (unknown) event times are interval censored. In addition, some individuals may have experienced some of the events before their first observation and may be lost to follow-up before experiencing the last event. Using a frailty model proposed by Aalen (1988, Mathematical Scientist 13, 90-103) but which has never been used to analyze real data, we examine whether individuals who develop early markers of HIV infection can also be expected to develop antibody and other indicators of HIV infection more rapidly.

Citations

Dec 16, 2003·Statistics in Medicine·P Saint-PierreP Godard
Dec 14, 2005·Statistics in Medicine·Janet M Box-Steffensmeier, Suzanna De Boef
Sep 6, 2007·Statistics in Medicine·Andrew C Titman, Linda D Sharples
Aug 6, 2009·Statistical Methods in Medical Research·Andrew C Titman, Linda D Sharples
May 15, 2018·AIDS Research and Human Retroviruses·Joanne D SteklerAnn C Collier
Apr 12, 2019·Statistics in Medicine·Shu Jiang, Richard J Cook
Aug 3, 2017·Statistics in Medicine·Grace Y YiFeng He
May 21, 2019·Journal of the Royal Statistical Society. Series C, Applied Statistics·Aidan G O'KeeffeVernon T Farewell

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