随访持续时间和数据收集滞后对适应性临床试验表现的影响。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-03-01 Epub Date: 2023-10-14 DOI:10.1002/pst.2342
Anders Granholm, Theis Lange, Michael O Harhay, Aksel Karl Georg Jensen, Anders Perner, Morten Hylander Møller, Benjamin Skov Kaas-Hansen
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引用次数: 0

摘要

不同的综合结果数据滞后(随访持续时间加上数据收集滞后)可能会影响适应性临床试验设计的性能。我们评估了不同结果数据滞后(0-105 天)对各种多阶段自适应试验设计(2/4组,有/没有共同对照,固定/反应自适应随机化)的性能的影响,根据不同的纳入率(3.33/6.67/10名患者/天),在没有、小和大差异的情况下,具有不期望的二元结果。在贝叶斯框架下进行模拟,校准优势/劣势的恒定停止阈值,以将1型错误率保持在约5%。我们评估了多个性能指标,包括平均样本量、事件计数/概率、结论性概率、所选组中估计效果的均方根误差(RMSE),以及停止时的分析与最终分析之间的均方根错误,包括来自所有随机患者的数据。由于结果数据滞后时间较长或纳入速度较快,具有可用数据的随机患者比例较小时,绩效指标通常会恶化,即平均样本量、事件计数/概率和RMSE较大,而结论性概率较低。结果数据滞后的绩效指标损伤≤45 与≥60的天数相比,天数相对较小 滞后天数。对于大多数指标,不同结果数据滞后和具有可用数据的随机患者比例较低的影响大于不同设计选择的影响,例如,使用固定与反应自适应随机化。结果数据滞后的增加显著影响了适应性试验设计的性能。Trialist在规划适应性试验时应考虑结果数据滞后的影响。
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Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials.

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups. Pre-Posterior Distributions in Drug Development and Their Properties. Beyond the Fragility Index. A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data.
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