EXTENDING MODELS VIA GRADIENT BOOSTING: AN APPLICATION TO MENDELIAN MODELS

• 成果类型: Article
• 署名作者: Huang, Theodore; Idos, Gregory; Hong, Christine; Gruber, Stephen B.; Parmigiani, Giovanni; Braun, Danielle
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; City of Hope
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1482
• 发表日期: 2021
• 页码: 1126-1146
• 关键词: logistic-regression; colorectal-cancer; predicting brca1; ovarian-cancer; lynch syndrome; mutations; breast; RISK; performance; validation

摘要

Improving existing widely-adopted prediction models is often a more efficient and robust way toward progress than training new models from scratch. Existing models may: (a) incorporate complex mechanistic knowledge, (b) leverage proprietary information, and (c) have surmounted barriers to adoption. Compared to model training, model improvement and modification receive little attention. In this paper we propose a general approach to model improvement: we combine gradient boosting with any previously developed model to improve model performance while retaining important existing characteristics. To exemplify, we consider the context of Mendelian models which estimate the probability of carrying genetic mutations that confer susceptibility to disease by using family pedigrees and health histories of family members. Via simulations, we show that integration of gradient boosting with an existing Mendelian model can produce an improved model that outperforms both that model and the model built using gradient boosting alone. We illustrate the approach on genetic testing data from the USC-Stanford Cancer Genetics Hereditary Cancer Panel (HCP) study.