Simultaneous monitoring for regression coefficients and baseline hazard profile in Cox modeling of time-to-event data
| Autor: | Jun Yan, Yishu Xue, Elizabeth D. Schifano |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
021103 operations research Proportional hazards model Computer science 0211 other engineering and technologies Nonparametric statistics Context (language use) 02 engineering and technology General Medicine Survival Analysis 01 natural sciences Censoring (statistics) 010104 statistics & probability Research Design Sample size determination Sample Size Linear regression Statistics Humans Computer Simulation Control chart 0101 mathematics Statistics Probability and Uncertainty Proportional Hazards Models Parametric statistics |
| Zdroj: | Biostatistics. 22:756-771 |
| ISSN: | 1468-4357 1465-4644 |
| DOI: | 10.1093/biostatistics/kxz064 |
| Popis: | Summary The Cox model is the most popular tool for analyzing time-to-event data. The nonparametric baseline hazard function can be as important as the regression coefficients in practice, especially when prediction is needed. In the context of stochastic process control, we propose a simultaneous monitoring method that combines a multivariate control chart for the regression coefficients and a profile control chart for the cumulative baseline hazard function that allows for data blocks of possibly different censoring rates and sample sizes. The method can detect changes in either the parametric or the nonparametric part of the Cox model. In simulation studies, the proposed method maintains its size and has substantial power in detecting changes in either part of the Cox model. An application in lymphoma survival analysis in which patients were enrolled by 2-month intervals in the Surveillance, Epidemiology, and End Results program identifies data blocks with structural model changes. |
| Databáze: | OpenAIRE |
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