Global Health Economics, Amgen (Europe) GmbH, Suurstoffi 22, 6343, Rotkreuz, Zug, Switzerland.
Amgen, Amgen Oncology, Cambridge, UK.
Pharmacoeconomics. 2019 May;37(5):727-737. doi: 10.1007/s40273-018-0759-6.
In economic evaluations in oncology, adjusted survival should be generated if imbalances in prognostic/predictive factors across treatment arms are present. To date, no formal guidance has been developed regarding how such adjustments should be made. We compared various covariate-adjusted survival modeling approaches, as applied to the ENDEAVOR trial in multiple myeloma that assessed carfilzomib plus dexamethasone (Cd) versus bortezomib plus dexamethasone (Vd).
Overall survival (OS) data and baseline characteristics were used for a subgroup (bortezomib-naïve/one prior therapy). Four adjusted survival modeling approaches were compared: propensity score weighting followed by fitting a Weibull model to the two arms of the balanced data (weighted data approach); fitting a multiple Weibull regression model including prognostic/predictive covariates to the two arms to predict survival using the mean value of each covariate and using the average of patient-specific survival predictions; and applying an adjusted hazard ratio (HR) derived from a Cox proportional hazard model to the baseline risk estimated for Vd.
The mean OS estimated by the weighted data approach was 6.85 years (95% confidence interval [CI] 4.62-10.70) for Cd, 4.68 years (95% CI 3.46-6.74) for Vd, and 2.17 years (95% CI 0.18-5.06) for the difference. Although other approaches estimated similar differences, using the mean value of covariates appeared to yield skewed survival estimates (mean OS was 7.65 years for Cd and 5.40 years for Vd), using the average of individual predictions had limited external validity (implausible long-term OS predictions with > 10% of the Vd population alive after 30 years), and using the adjusted HR approach overestimated uncertainty (difference in mean OS was 2.03, 95% CI - 0.17 to 6.19).
Adjusted survival modeling based on weighted or matched data approaches provides a flexible and robust method to correct for covariate imbalances in economic evaluations. The conclusions of our study may be generalizable to other settings.
ClinicalTrials.gov identifier NCT01568866 (ENDEAVOR trial).
在肿瘤学的经济评估中，如果治疗组之间存在预后/预测因素的不平衡，应生成调整后的生存数据。迄今为止，尚未制定关于如何进行此类调整的正式指南。我们比较了各种调整后的生存模型方法，这些方法应用于评估卡非佐米加地塞米松（Cd）与硼替佐米加地塞米松（Vd）的 ENDEAVOR 试验中的多发性骨髓瘤亚组（硼替佐米初治/一线治疗前）。
使用总生存（OS）数据和基线特征，对亚组（硼替佐米初治/一线治疗前）进行分析。比较了四种调整后的生存模型方法：倾向评分加权，然后对平衡数据的两臂拟合威布尔模型（加权数据方法）；拟合包含预后/预测因素的多威布尔回归模型，以每个协变量的平均值预测两臂的生存，并使用患者特定生存预测的平均值；以及应用 Cox 比例风险模型得出的调整后风险比（HR）应用于 Vd 基线风险估计。
加权数据方法估计的平均 OS 分别为 Cd 组 6.85 年（95%置信区间 [CI] 4.62-10.70），Vd 组 4.68 年（95% CI 3.46-6.74），差异为 2.17 年（95% CI 0.18-5.06）。尽管其他方法估计的差异相似，但使用协变量的平均值似乎会产生偏斜的生存估计（Cd 的平均 OS 为 7.65 年，Vd 的平均 OS 为 5.40 年），使用个体预测的平均值具有有限的外部有效性（Vd 人群中超过 10%的人在 30 年后仍存活，不合理的长期 OS 预测），而使用调整后的 HR 方法会高估不确定性（平均 OS 差异为 2.03，95%CI -0.17 至 6.19）。
基于加权或匹配数据方法的调整后生存模型为经济评估中纠正协变量不平衡提供了一种灵活且稳健的方法。我们研究的结论可能适用于其他情况。
ClinicalTrials.gov 标识符 NCT01568866（ENDEAVOR 试验）。
