Integrative Survival Analysis with Uncertain Event Times in Application to a Suicide Risk Study

• Activity Number: 346 - New Methods with Applications in Mental Health Statistics
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Wenjie Wang* and Robert Aseltine and Kun Chen and Jun Yan
• Companies: University of Connecticut and University of Connecticut Health Center and University of Connecticut and University of Connecticut
• Keywords: Cox model; Data linkage; ECM algorithm; Integrative learning; Suicide prevention

Abstract

The concept of integrating data from disparate sources to accelerate scientific discovery has generated tremendous excitement in many fields. The potential benefits from data integration, however, may be compromised by the uncertainty due to imperfect record linkage. Motivated by a suicide risk study, we propose an approach for analyzing survival data with uncertain event times arising from data integration. We develop an integrative Cox proportional hazards regression, in which the uncertainty in the matched event times is modeled probabilistically. The estimation procedure combines the ideas of profile likelihood and the expectation conditional maximization algorithm (ECM). Simulation studies demonstrate that under realistic settings of imperfect data linkage, the proposed method outperforms several competing approaches including multiple imputation. A marginal screening analysis using the proposed integrative Cox model is performed to identify risk factors associated with death following suicide-related hospitalization in Connecticut. The identified diagnostics codes are consistent with existing literature and provide several new insights on suicide risk prediction.