A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival.
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 R(2), 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 (R(2) =0.298; 95% CI: 0.236-0.358) than the Cox model (R(2) =0.156; 95% CI: 0.111-0.203). The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.
Center for Evidence Based Medicine and Health Outcomes Research, University of South Florida, Tampa, Florida, USA. bmiladin@health.usf.edu
Branko
B
Miladinovic
Ambuj
A
Kumar
Rahul
R
Mhaskar
Sehwan
S
Kim
Ronald
R
Schonwetter
Benjamin
B
Djulbegovic
D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005431:Florida; D006738:Hospices / Q000706:statistics & numerical data*; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D010166:Palliative Care; D011379:Prognosis; D016016:Proportional Hazards Models; D013997:Time Factors