Joint survival and longitudinal modelling for combined cohort data

Qian Zhao and James McVittie

Journal of Applied Statistics, 2026, vol. 53, issue 9, 1608-1627

Abstract: A primary goal of survival analysis is modelling the time from an initial event to a failure event and the factors which affect the hazard rate. In various applications, the observed data can consist of a combination of right-censored failure times and left-truncated right-censored failure times by merging the data collected from incident and prevalent cohort studies with follow-up, respectively. Furthermore, in addition to the observed failure/censoring times, the survival data typically includes time-invariant covariates as well as longitudinal measurements collected throughout the failure/censoring time durations. We introduce three novel estimation methods for a joint proportional hazards and longitudinal model where incident and prevalent cohort data are combined. Using simulated data, we compare the performance of the combined cohort estimation procedures and use these techniques to model the relationship between the mortality of mature female baboons based on their longitudinally measured glucocorticoid hormone levels.

Date: 2026
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DOI: 10.1080/02664763.2025.2567987

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