A cross‐validation statistical framework for asymmetric data integration

• Published in: Biometrics Vol. 79; no. 2; pp. 1280 - 1292
• Main Authors: Tran, Lam, He, Kevin, Wang, Di, Jiang, Hui
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.06.2023
• Subjects: asymmetric cross‐validation; Bayesian analysis; Bias; Biobanks; Biomedical research; Biostatistics; Computer Simulation; data collection; Data integration; Datasets; Error analysis; Feature selection; Heterogeneity; Humans; Integration; kidney transplant; Kidney transplantation; leave‐one‐out error; Models, Statistical; Optimization; Parameter estimation; prediction; Predictions; Proportional Hazards Models; Research Design; Statistical analysis; Statistical models; asymmetric cross-validation; data integration; leave-one-out error

The proliferation of biobanks and large public clinical data sets enables their integration with a smaller amount of locally gathered data for the purposes of parameter estimation and model prediction. However, public data sets may be subject to context‐dependent confounders and the protocols behind...