Absolute and relative risk estimation in the presence of outcome ascertainment gaps and competing risks. (27th January 2023)

• Record Type: Journal Article
• Authors: Liu, Danping; Wu, Emily; Shih, Joanna H.; Kitahara, Cari M.; Cheung, Li C.
• Is Part Of: Statistics in medicine. Volume 42:Number 8(2023)
• Page Start: 1263
• Page End: 1276
• Publication Date: 2023-01-27
• Subjects: cancer registry -- censored time‐to‐event outcome -- electronic health record data -- mover‐stayer model -- outcome mis‐ascertainment; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9668
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Incomplete coverage by cancer registries can lead to an underreporting of cancers and a resulting bias in risk estimates. When registries are defined by geographic region, gaps in observation can arise for individuals who reside outside of or migrate from the total registry catchment area. Moreover, the exact periods of non‐observation for an individual may be unknown due to intermittent reporting of residential histories. The motivating example for this work is the U.S. Radiologic Technologist (USRT) study which ascertained cancer outcomes for a national cohort through 43 state/regional registries; similar gaps in outcome ascertainment can appear in other registry or electronic health record‐ based cohort studies. We propose a two‐step procedure for estimating relative and absolute risk in these settings. First, using a mover stayer model fitted to individuals' known residential history, we obtain individual posterior probabilities of residing outside the registry catchment area each year. Second, we incorporate these probabilities in the survival data likelihood for competing risks to account for unobserved events. We assess the performance of the proposed method in extensive simulation studies. Compared to several simple alternative approaches, the proposed method reduces bias and improves efficiency. Finally, we apply the proposed method to a study of first primary lung cancers in the USRT cohort.