Supplementary figures: Improved estimation of overall survival and progression-free survival for state transition modeling

These are peer-reviewed supplementary figures for the article 'Improved estimation of overall survival and progression-free survival for state transition modeling' published in the Journal of Comparative Effectiveness Research.Figure S1: Standard approach: Overview of distributions by health state transitionsFigure S2: Novel approach: Overview of distributions by health state transitionsAim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan–Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan–Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

本数据集为发表于《比较疗效研究杂志》的论文《改进状态转换模型中对总生存期和无进展生存期的估计》的同行评审补充图表。图S1：标准方法：按健康状况转换概述分布；图S2：新颖方法：按健康状况转换概述分布。研究目标：英国国家卫生与临床优化研究所的指导文件（技术支持文件19）指出，状态转换模型（STMs）的关键挑战在于其难以与试验中观察到的终点数据达到令人满意的拟合。若在试验期间与数据拟合不佳，则可能对长期外推产生影响。定义了一种新颖的估计方法，该方法通过优化STM预测的总生存期（OS）和无进展生存期（PFS）外推，以提供更接近试验中观察到的终点的估计。材料与方法：将STM拟合至SQUIRE临床试验数据（来自Project Data Sphere项目），使用了两种方法：一种为标准方法，即利用最大似然估计个体转换，并根据AIC/BIC选择最佳拟合参数模型；另一种为新颖方法，通过最小化STM预测的OS和PFS曲线与相应的OS和PFS Kaplan-Meier曲线之间的面积来优化参数。进行了敏感性分析以评估不确定性。结果：与标准方法相比，新颖方法对所有分析参数分布组合的OS和PFS Kaplan-Meier估计均更为接近。尽管新颖方法的不确定性略大，但在12个参数分布中，它对10个分布的受限平均生存时间提供了更好的估计。结论：定义了一种新颖方法，该方法提供了一种替代的STM估计方法，能够改善对建模终点的拟合，并易于扩展到更复杂的模型结构。