心理干预
临床试验
随机对照试验
干预(咨询)
医学
数字健康
研究设计
杠杆(统计)
试点试验
医学物理学
物理疗法
医疗保健
计算机科学
护理部
统计
人工智能
病理
外科
经济
经济增长
数学
作者
Johannes O. Ferstad,Priya Prahalad,David M. Maahs,Dessi P. Zaharieva,Emily B. Fox,Manisha Desai,Ramesh Johari,David Scheinker
标识
DOI:10.1056/evidoa2300164
摘要
BackgroundDigital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.MethodsWe leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.ResultsOur approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.ConclusionsOptimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)
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