EFFICIENT AND ROBUST ESTIMATION OF τ-YEAR RISK PREDICTION MODELS LEVERAGING TIME VARYING INTERMEDIATE OUTCOMES.

ABSTRACT: Accurate risk prediction models play a key role in precision medicine, where optimal individualized disease prevention and treatment strategies can be formed based on predicted risks. In many clinical settings, it is of great interest to predict the τ-year risk of developing a clinical event using baseline covariates. Such τ-year risk models can be estimated by fitting standard survival models, including the Cox proportional hazards model and the more flexible τ-year specific generalized linear model (τ-GLM). However, an efficient and robust estimation of the risk model is challenging under heavy censoring and potential model misspecification. Intermediate outcomes observed prior to censoring can be highly predictive of the outcome and, thus, may be used to improve the efficiency of the mo

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC10705046 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature