An augmented illness‐death model for semi‐competing risks with clinically immediate terminal events

• Published in: Statistics in medicine Vol. 43; no. 21; pp. 4194 - 4211
• Main Authors: Reeder, Harrison T., Lee, Kyu Ha, Papatheodorou, Stefania I., Haneuse, Sebastien
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.09.2024; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Bayesian survival analysis; Computer Simulation; Female; Humans; logistic model; Models, Statistical; Morbidity; Mortality; multi‐state model; Pre-Eclampsia - epidemiology; Pre-Eclampsia - mortality; Preeclampsia; Pregnancy; Regression analysis; Risk Assessment - methods; risk prediction; semi‐competing risks; time‐to‐event analysis; time‐to‐event analysis; semi‐competing risks; logistic model; multi‐state model; Bayesian survival analysis; risk prediction
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10181

Preeclampsia is a pregnancy‐associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time‐to‐event setting of “semi‐competing risks” in which a non‐terminal event of interest is subject to the occurrence of a terminal event of interest. The semi‐competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non‐terminal event, which we call “clinically immediate” and “non‐immediate” terminal events. Though such phenomena arise in many clinical contexts, to‐date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty‐based illness‐death model with a binary submodel to distinguish risk of immediate terminal event following the non‐terminal event. The model admits direct dependence of the terminal event on the non‐terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.