Faculty, Staff and Student Publications
Publication Date
4-1-2022
Journal
Life Science Alliance
Abstract
Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease.
Keywords
Humans, Prognosis, Proportional Hazards Models, Risk prediction, Censored regression, Pseudo-likelihood, Validation, Perturbation
DOI
10.1007/s10985-022-09545-9
PMID
35061146
PMCID
PMC10084512
PubMedCentral® Posted Date
4-10-2023
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes