Flexible Joint Models For Screening Studies

Abstract

In this dissertation, we study and develop statistical methods to jointly analyze longitudinal biomarkers with time-to-event outcomes motivated by risk assessment in cancer screening. Cancer screening studies collect longitudinal biopsies to allow the identification of additional longitudinal biomarker measurements for risk stratification. However, these studies present several challenges for current joint modeling approaches. In Chapter 2, we develop a dynamic risk prediction approach that links both continuous and binary biomarkers to the interval-censored precancer outcome with shared high dimensional random effects. A cancer screening dataset shows improved risk stratification compared to univariate joint models. In Chapter 3, we develop a latent health model for at-risk patients that can both deteriorate to case status and improve to low-risk status. We link the change in the health process to a longitudinal biomarker whose trajectory can change based on the event. We see that treating individuals who become risk-free as right censored and ignoring the event's impact on the biomarker trajectory can result in significantly biased risk estimates. In Chapter 4, we compare three common approaches to identify longitudinal biomarkers associated with survival outcomes: joint models, conditional models, and time-dependent Cox models. We use simulations to evaluate how well the methods identify and distinguish biomarkers that are useful for long-term risk assessment and early detection.