Adjusting a subject-specific time of event in longitudinal studies

Abstract

Biomedical studies often involve an event that occurs to individuals at different times and has a significant influence on individual trajectories of response variables over time. We propose a statistical model to capture the mean trajectory alteration caused by not only the occurrence of the event but also the subject-specific time of the event. The proposed model provides a post-event mean trajectory smoothly connected with the pre-event mean trajectory by allowing the model parameters associated with the post-event mean trajectory to vary over time of the event. A goodness-of-fit test is considered to investigate how well the proposed model is fit to the data. Hypothesis tests are also developed to assess the influence of the subject-specific time of event on the mean trajectory. Theoretical and simulation studies confirm that the proposed tests choose the correctly specified model consistently and examine the effect of the subject-specific time of event successfully. The proposed model and tests are also illustrated by the analysis of two real-life data from a biomarker study for HIV patients along with their own time of treatment initiation and a body fatness study in girls with different age of menarche.

Modeling the Population Mean Outcome Trajectory in Observational Studies With Varying Time to Intervention

“…However, an intervention that has an impact on the changes in the outcome can occur at different times during the course of longitudinal studies. When the impact of the intervention depends on time to intervention (TTI), it is crucial to adjust for the TTI in modeling the longitudinal outcome trajectory; see Wu and Tian (2008), Xing and Ying (2012), Liu et al (2018), and Cho et al (2020).…”

“…Intervention can occur at different time across individuals in many studies where an impact of the TTI on the repeated outcome remains uncertain. In recent years,Wu and Tian (2008),Xing and Ying (2012),Liu et al (2018), andCho et al (2020) have proposed longitudinal models that account for the varying TTI impact on the repeated outcome in cases where none of factors that confound associations between TTI and the outcome exist. In observational studies, the intervention is rather initiated on the basis of other factors that may confound the TTI impact.…”