A semiparametric method to measure predictive accuracy of covariates for doubly censored survival outcomes
- Authors
- Han, Seungbong; Lee, JungBok
- Issue Date
- Jul-2016
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- double censoring; model evaluation; predictive ability; prostate cancer; variable selection
- Citation
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.23, no.4, pp.343 - 353
- Journal Title
- COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
- Volume
- 23
- Number
- 4
- Start Page
- 343
- End Page
- 353
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/8129
- DOI
- 10.5351/CSAM.2016.23.4.343
- ISSN
- 2287-7843
- Abstract
- In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.
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