Extrapolation of Relative Treatment Effects using Change-point Survival Models
利用变点生存模型外推相对治疗效应AI 解读 ↓
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利用变点生存模型外推相对治疗效应
- 研究问题
- 在长期生存外推中，如何刻画治疗起效延迟、效应消失和风险率趋同，使相对治疗效应及生存预测更具临床合理性？
- 方法与思路
- 作者用贝叶斯变点生存模型估计相对治疗效应的变化，传播包括变点位置在内的参数不确定性，并开展模拟与数据比较。
- 材料陈述的贡献
- 摘要称变点模型通常改善生存外推，尤其在相对效应变化较大时；真实案例中部分预测比灵活样条模型更具临床合理性。
- 阅读关注点
- 应核对变点前后风险函数、先验与删失处理、外推评价标准，以及数据能够识别的变化与临床情景假设之间的区别。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明因果识别设计、具体预测误差或外推假设的可靠性。
原文摘要
Introduction: Modelling of relative treatment effects is an important aspect to consider when extrapolating the long-term survival outcomes of treatments. Flexible parametric models offer the ability to accurately model the observed data, however, the extrapolated relative treatment effects and subsequent survival function may lack face validity. Methods: We investigate the ability of change-point survival models to estimate changes in the relative treatment effects, specifically treatment delay, loss of treatment effects and converging hazards. These models are implemented using standard Bayesian statistical software and propagate the uncertainty associate with all model parameters including the change-point location. A simulation study was conducted to assess the predictive performance of these models compared with other parametric survival models. Change-point survival models were applied to three datasets, two of which were used in previous health technology assessments. Results: Change-point survival models typically provided improved extrapolated survival predictions, particularly when the changes in relative treatment effects are large. When applied to the real world examples they provided good fit to the observed data while and in some situations produced more clinically plausible extrapolations than those generated by flexible spline models. Change-point models also provided support to a previously implemented modelling approach which was justified by visual inspection only and not goodness of fit to the observed data. Conclusions: We believe change-point survival models offer the ability to flexibly model observed data while also modelling and investigating clinically plausible scenarios with respect to the relative treatment effects.