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Joint modeling of longitudinal and survival data

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  • Yulia Marchenko

    (StataCorp LP)

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.

Suggested Citation

  • Yulia Marchenko, 2016. "Joint modeling of longitudinal and survival data," United Kingdom Stata Users' Group Meetings 2016 15, Stata Users Group.
    • Handle: RePEc:boc:usug16:15
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