Weighting estimation in the cause-specific Cox regression with partially missing causes of failureopen access
- Authors
- Lee, Jooyoung; Ogino, Shuji; Wang, Molin
- Issue Date
- Jun-2024
- Publisher
- WILEY
- Keywords
- augmented inverse probability weighting; competing risks; etiologic heterogeneity; partially missing causes
- Citation
- STATISTICS IN MEDICINE, v.43, no.13, pp 2575 - 2591
- Pages
- 17
- Journal Title
- STATISTICS IN MEDICINE
- Volume
- 43
- Number
- 13
- Start Page
- 2575
- End Page
- 2591
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73678
- DOI
- 10.1002/sim.10084
- ISSN
- 0277-6715
1097-0258
- Abstract
- Complex diseases are often analyzed using disease subtypes classified by multiple biomarkers to study pathogenic heterogeneity. In such molecular pathological epidemiology research, we consider a weighted Cox proportional hazard model to evaluate the effect of exposures on various disease subtypes under competing-risk settings in the presence of partially or completely missing biomarkers. The asymptotic properties of the inverse and augmented inverse probability-weighted estimating equation methods are studied with a general pattern of missing data. Simulation studies have been conducted to demonstrate the double robustness of the estimators. For illustration, we applied this method to examine the association between pack-years of smoking before the age of 30 and the incidence of colorectal cancer subtypes defined by a combination of four tumor molecular biomarkers (statuses of microsatellite instability, CpG island methylator phenotype, BRAF mutation, and KRAS mutation) in the Nurses' Health Study cohort.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73678)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.