Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies

Abhisek Saha, Ling Ma, Animikh Biswas and Rajeshwari Sundaram ()
Additional contact information
Abhisek Saha: NIH, DHHS
Ling Ma: Genentech Inc
Animikh Biswas: University of Maryland Baltimore County
Rajeshwari Sundaram: NIH, DHHS

Statistics in Biosciences, 2024, vol. 16, issue 1, No 5, 86-106

Abstract: Abstract In biomedical studies, longitudinal processes are collected till time-to-event, sometimes on nested timescales (example, days within months). Most of the literature in joint modeling of longitudinal and time-to-event data has focused on modeling the mean or dispersion of the longitudinal process with the hazard for time-to-event. However, based on the motivating studies, it may be of interest to investigate how the cycle-level geometric features (such as the curvature, location and height of a peak), of a cyclical longitudinal process is associated with the time-to-event being studied. We propose a shared parameter joint model for a cyclical longitudinal process and a discrete survival time, measured on nested timescales, where the cycle-varying geometric feature is modeled through a linear mixed effects model and a proportional hazards model for the discrete survival time. The proposed approach allows for prediction of survival probabilities for future subjects based on their available longitudinal measurements. Our proposed model and approach is illustrated through simulation and analysis of Stress and Time-to-Pregnancy, a component of Oxford Conception Study. A joint modeling approach was used to assess whether the cycle-specific geometric features of the lutenizing hormone measurements, such as its peak or its curvature, are associated with time-to-pregnancy (TTP).

Keywords: Joint modeling; TTP; Longitudinal data; Hormonal profile; Curvature; Peak (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09381-x

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