Estimation and inference of predictive discrimination for survival outcome risk prediction models

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 rigorou...

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Vydáno v:Lifetime data analysis Ročník 28; číslo 2; s. 219 - 240
Hlavní autoři: Li, Ruosha, Ning, Jing, Feng, Ziding
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2022
Springer Nature B.V
Témata:
Discrimination
Economics
Estimators
Failure times
Finance
Health Sciences
Humans
Insurance
Lung cancer
Management
Mathematics and Statistics
Medical prognosis
Medical research
Medical screening
Medicine
Methods
Operations Research/Decision Theory
Prediction models
Predictions
Prognosis
Proportional Hazards Models
Quality Control
Regression models
Reliability
Risk analysis
Risk factors
Safety and Risk
Statistical models
Statistics
Statistics for Business
Statistics for Life Sciences
Survival
Time dependence
Validation
Pseudo-likelihood
Risk prediction
Perturbation
Censored regression
ISSN:1380-7870, 1572-9249, 1572-9249
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Shrnutí: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.
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ISSN:1380-7870
1572-9249
1572-9249
DOI:10.1007/s10985-022-09545-9