  Objective: 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. Study Design and Setting: 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 greater or equal 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. Ten-fold leave-one-cohort-internal validation further validated the
optimal missing data approach. Results: Among 12,703 biopsies from 10 training
cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to
1,757 of 5,540 (32%) in the external validation cohort. In external validation,
the available cases method that pooled individual patient data containing all
risk factors input by an end-user had best CIL, under-predicting risks as
percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had
the worst CIL (-13.3%). The available cases method was further validated as
optimal in internal cross-validation and thus used for development of an online
risk tool. For end-users of the risk tool, two risk factors were mandatory:
serum prostate-specific antigen (PSA) and age, and ten were optional: digital
rectal exam, prostate volume, prior negative biopsy,
5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic
ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer
family history.
