1. Development of an international prostate cancer risk tool integrating data from multiple heterogeneous cohorts
Donna Pauler Ankerst, Johanna Tolksdorf
Department of Mathematics, Technical University Munich, Germany
Cleveland Clinic
Durham VA
Puerto Rico
Mayo Clinic
Hamburg
Milan
Canadian Consortium
UT Health
UCSF
MSKCC
8492 prostate biopsies collected from 10 centers,
6 risk factors to predict high-grade prostate cancer

2. Center variation in outcomes, risk factors and associations

3. Comparison of 5 prediction methods over all 252 ways to split 10 cohorts into 5 for training and 5 for testing, revealed no difference, so chose simple 1)
1) Pool data, ignore cohort effect, used fixed effects for prediction: 𝑝 𝑥 = 𝑒𝑥𝑝 𝛽′𝑥 1+𝑒𝑥𝑝 𝛽′𝑥
2) Pool data, cohort as random effect, median prediction: 𝛽 0𝑐 ~𝑁 0,𝑑
3) Pool data, cohort as random effect, mean prediction: −∞ ∞ 𝑒𝑥𝑝 𝛽 0 + 𝛽 0𝑐 +𝛽 ′ 𝑥 1+𝑒𝑥𝑝 𝛽 0 + 𝛽 0𝑐 + 𝛽 ′ 𝑥 𝑓 𝛽 0𝑐 𝑑 𝛽 0𝑐
4) Meta-analysis, fixed effects by center: 𝛽 𝑘 = 𝑐=1 𝐶 𝑤 𝑘𝑐 𝛽 𝑘𝑐 𝑐=1 𝐶 𝑤 𝑘𝑐 , 𝑤 𝑘𝑐 =1/𝑣𝑎𝑟( 𝛽 𝑘𝑐 )
5) Meta-analysis, random effects by center: 𝑤 𝑘𝑐 =1/ 𝑣𝑎𝑟 𝛽 𝑘𝑐 +𝑏

4. No difference in machine learning methods in leave-one-cohort out cross-validation
Logistic regression as good or better than random forests (RF), k-nearest neighbors (KNN), bagged KNN, and artificial neural networks (ANN).
Simpler, interpretable, better.
Higher HLS is worse fit

5. Final model published and available online at riskcalc.org