Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

ABSTRACT:

Background

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.

Methods

Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated.

SUBMITTER: Neumair M 

PROVIDER: S-EPMC9306143 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature