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Activity Number:
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383
- Longitudinal/Repeated Measures and Terminal Events
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Type:
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Invited
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Lifetime Data Analysis Interest Group
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Abstract #322126
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View Presentation
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Title:
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Conditional Models for Longitudinal Data with a Temrinal Event
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Author(s):
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Bin Nan and Shengchun Kong and John Kalbfleisch*
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Companies:
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University of Michigan and Gilead Sciences, Inc and University of Michigan
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Keywords:
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Cox regression ;
Empirical process ;
Mixed effects model ;
Pseudo-maxiumu lieklihood
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Abstract:
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We consider a random effects model for longitudinal data with a terminal event that is subject to right censoring. Existing methods for analyzing such data include shared latent frailty models and the estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a simple interpretation and retains the usual relationship between the longitudinal response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-terminal event time given other covariates is estimated and then the likelihood for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided.
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Authors who are presenting talks have a * after their name.
