Joint modeling of longitudinal and survival data

Yulia Marchenko ()
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Yulia Marchenko: StataCorp LP

United Kingdom Stata Users' Group Meetings 2016 from Stata Users Group

Abstract: Joint modeling of longitudinal and survival-time data has been gaining more and more attention in recent years. Many studies collect both longitudinal and survival-time data. Longitudinal, panel, or repeated-measures data record data measured repeatedly at different time points. Survival-time or event history data record times to an event of interest such as death or onset of a disease. The longitudinal and survival-time outcomes are often related and should thus be analyzed jointly. Three types of joint analysis may be considered: 1) evaluation of the effects of time-dependent covariates on the survival time; 2) adjustment for informative dropout in the analysis of longitudinal data; and 3) joint assessment of the effects of baseline covariates on the two types of outcomes. In this presentation, I will provide a brief introduction to the methodology and demonstrate how to perform these three types of joint analysis in Stata.

Date: 2016-09-16
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug16:15

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