a flexible alternative to the cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival灵活的替代cox比例风险模型评估预后的准确性临终关怀病人的生存.pdfVIP

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a flexible alternative to the cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival灵活的替代cox比例风险模型评估预后的准确性临终关怀病人的生存.pdf

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a flexible alternative to the cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival灵活的替代cox比例风险模型评估预后的准确性临终关怀病人的生存

A Flexible Alternative to the Cox Proportional Hazards Model for Assessing the Prognostic Accuracy of Hospice Patient Survival 1 1 1 2 2 Branko Miladinovic *, Ambuj Kumar , Rahul Mhaskar , Sehwan Kim , Ronald Schonwetter , Benjamin Djulbegovic1,3 1 Center for Evidence Based Medicine and Health Outcomes Research, University of South Florida, Tampa, Florida, United States of America, 2 HPC Healthcare, Temple Terrace, Florida, United States of America, 3 H. Lee Moffitt Cancer Center Research Institute, Tampa, Florida, United States of America Abstract Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models’ predictive performance were the explained variation statistic R2, scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R2 = 0.298; 95% CI: 0.236–0.358) than the Cox model (R2 = 0.156; 95% CI: 0