Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
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DOI：
10.1080/19466315.2023.2211023
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发表时间：
2022-06
影响因子：
1.8
通讯作者：
S. Calderazzo;S. Tarima;Carissa P. Reid;N. Flournoy;T. Friede;N. Geller;J. L. Rosenberger;N. Stallard;M. Ursino;M. Vandemeulebroecke;K. Van Lancker;S. Zohar
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文献类型：
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作者：
S. Calderazzo;S. Tarima;Carissa P. Reid;N. Flournoy;T. Friede;N. Geller;J. L. Rosenberger;N. Stallard;M. Ursino;M. Vandemeulebroecke;K. Van Lancker;S. Zohar
虽然SARS-CoV-2（COVID-19）大流行导致了令人印象深刻的和前所未有的临床研究启动，但它也导致了其他疾病领域的临床试验的相当大的中断，约80%的非COVID-19试验在大流行期间停止或中断。在许多情况下，中断的试验将不具有产生可解释结果所需的计划统计功效。本文描述了通过合并辅助数据源提供的额外信息来补偿试验中断所导致的信息损失的方法。所述方法包括使用试验本身提供的基线和早期结局数据的辅助数据，以及频率论和贝叶斯方法，以纳入来自外部数据源的信息。这些方法通过应用于基于初级保健儿科学习活动营养（PLAN）研究的人工数据分析来说明，该研究是一项评估受COVID-19大流行影响的超重儿童的饮食和运动干预的临床试验。我们展示了所有提出的方法如何导致精度的增加，相对于使用完整的案例数据。
Abstract While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This article describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only.