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- W2023244067 abstract "Censoring of a longitudinal outcome often occurs when data are collected in a biomedical study and where the interest is in the survival and or longitudinal experiences of a study population. In the setting considered herein, we encountered upper and lower censored data as the result of restrictions imposed on measurements from a kinetic model producing “biologically implausible” kidney clearances. The goal of this paper is to outline the use of a joint model to determine the association between a censored longitudinal outcome and a time to event endpoint. This paper extends Guo and Carlin's [6] paper to accommodate censored longitudinal data, in a commercially available software platform, by linking a mixed effects Tobit model to a suitable parametric survival distribution. Our simulation results showed that our joint Tobit model outperforms a joint model made up of the more naive or “fill-in” method for the longitudinal component. In this case, the upper and/or lower limits of censoring are replaced by ..." @default.
- W2023244067 created "2016-06-24" @default.
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- W2023244067 date "2012-09-21" @default.
- W2023244067 modified "2023-10-14" @default.
- W2023244067 title "Joint modeling of censored longitudinal and event time data" @default.
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- W2023244067 doi "https://doi.org/10.1080/02664763.2012.725468" @default.
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