Department of Health Service Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Bergen, Norway.
Department of Health Service Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Bergen, Norway.
J Cancer Policy. 2024 Dec;42:100507. doi: 10.1016/j.jcpo.2024.100507. Epub 2024 Sep 26.
This study investigates factors associated with use of real-world data (RWD) in economic modelling for single technology appraisals (STAs) of cancer drugs by the National Institute for Health and Care Excellence (NICE) to improve systematic understanding of the use of RWD.
The data were extracted from STAs of cancer drugs, for which NICE issued guidance between January 2011 and December 2022 (n=267). Binary regression was used to test hypotheses concerning the greater or lesser use of RWD. Bonferroni-Holm correction was used to control error rates in multiple hypotheses tests. Several explanatory variables were considered in this analysis, including time (Time), incidence rate of disease (IR), availability of direct treatment comparison (AD), generalisability of trial data (GE), maturity of survival data in trial (MS) and previous technology recommendations by NICE (PR). The primary outcome variable was any use of RWD. Secondary outcome variables were specific uses of RWD in economic models.
AD had a statistical negative association with any use of RWD whereas no associations with non-parametric and parametric use of RWD were found. Time had several statistical associations with use of RWD (validating survival distributions for the intervention, estimating progression-free survival for the intervention, estimating overall survival for comparators and transition probabilities).
RWD were more likely to be used in economic modelling of cancer drugs when randomised controlled trials failed to provide relevant clinical information of the drug for appraisals, particularly in the absence of direct treatment comparisons. These results, based on analysis of data systematically collected from previous appraisals, suggest that uses of RWD were associated with data gaps in the economic modelling. While this result may support some of the claimed advantages of using RWD when evidence is absent, the question, the extent to which use of RWD in indirect treatment comparisons reduces uncertainty is still to be determined.
本研究旨在调查英国国家卫生与保健优化研究所（NICE）在癌症药物单技术评估（STA）的经济建模中使用真实世界数据（RWD）的相关因素，以提高对 RWD 使用的系统理解。
本研究数据来自 NICE 于 2011 年 1 月至 2022 年 12 月期间发布的 STA 癌症药物评估（n=267）。采用二元回归检验与 RWD 使用程度更大或更小相关的假设。Bonferroni-Holm 校正用于控制多项假设检验中的错误率。在该分析中考虑了几个解释变量，包括时间（Time）、疾病发生率（IR）、直接治疗比较的可用性（AD）、试验数据的普遍性（GE）、试验生存数据的成熟度（MS）和 NICE 之前的技术建议（PR）。主要结果变量是任何 RWD 的使用。次要结果变量是 RWD 在经济模型中的具体使用。
AD 与任何 RWD 的使用均呈统计学负相关，而非参数和参数 RWD 的使用则无关联。时间与 RWD 的使用有多个统计学关联（验证干预措施的生存分布、估计干预措施的无进展生存期、估计对照的总生存期和转移概率）。
当随机对照试验未能为评估提供药物的相关临床信息时，RWD 更有可能用于癌症药物的经济建模，特别是在缺乏直接治疗比较的情况下。这些基于对之前评估中系统收集的数据的分析结果表明，RWD 的使用与经济建模中的数据缺口有关。虽然这一结果可能支持在证据缺失时使用 RWD 的一些优势，但使用 RWD 进行间接治疗比较在多大程度上降低了不确定性仍有待确定。
