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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| Published in: | Lifetime data analysis Vol. 28; no. 2; pp. 219 - 240 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer US
01.04.2022
Springer Nature B.V |
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| ISSN: | 1380-7870, 1572-9249, 1572-9249 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Ning, Jing Li, Ruosha Feng, Ziding |
| AuthorAffiliation | 1 Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA 3 Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, WA, USA 2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA – name: 2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – name: 3 Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, WA, USA |
| Author_xml | – sequence: 1 givenname: Ruosha orcidid: 0000-0003-3595-4392 surname: Li fullname: Li, Ruosha email: ruosha.li@uth.tmc.edu organization: Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston – sequence: 2 givenname: Jing surname: Ning fullname: Ning, Jing organization: Department of Biostatistics, The University of Texas MD Anderson Cancer Center – sequence: 3 givenname: Ziding surname: Feng fullname: Feng, Ziding organization: Division of Public Health Sciences, Fred Hutchison Cancer Research Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35061146$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. |
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| DOI | 10.1007/s10985-022-09545-9 |
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| Keywords | Validation Pseudo-likelihood Risk prediction Perturbation Censored regression |
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| SubjectTerms | 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 |
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