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Activity Number: 277 - SPEED: Biometrics and Environmental Statistics Part 1
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #320911
Title: Accommodating Heterogeneous Missing Data Patterns for Prostate Cancer Risk Prediction
Author(s): Matthias Neumair* and Donna P. Ankerst
Companies: Technical University of Munich and Technical University of Munich
Keywords: clinical risk prediction; missing data; prostate cancer; validation

We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Ten North American and European cohorts from the Prostate Biopsy Collaborative Group were used for fitting a risk prediction tool for clinically significant prostate cancer on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and AUC were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL and obtained an AUC of 75.7%. Imputation had the worst CIL. The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. Full paper available at

Authors who are presenting talks have a * after their name.

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