Joint Analysis of Longitudinal and Time-to-Event Data

Zheng Lu (), Emmanuel Chigutsa () and Xiao Tong ()
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Zheng Lu: Clinical Pharmacology and Exploratory Development, Astellas Pharma
Emmanuel Chigutsa: Pharmacometrics, Eli Lilly and Company
Xiao Tong: Clinical Pharmacology, Biogen

Chapter 97 in Principles and Practice of Clinical Trials, 2022, pp 1919-1935 from Springer

Abstract: Abstract The longitudinal and time-to-event data are two kinds of common data generated from various clinical trials across different therapeutic areas. Joint modeling is appropriate to estimate unbiased effect of covariates that are measured longitudinally and are related to the event on the time to an event and then could be applied to predict the time to an event. An underlying random effects structure links the survival and longitudinal sub-models and allows for individual-specific predictions. This chapter provided the basic backgrounds of longitudinal and time-to-event data commonly generated from clinical trials and derivations of the joint likelihood function to be maximized when jointly modeling longitudinal and time-to-event data. In addition to these theoretical backgrounds, different applications of joint modeling of longitudinal and time-to-event data across different therapeutic areas and individual dynamic prediction were also extensively discussed.

Keywords: Longitudinal; Time-to-event; Joint (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_131

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