Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-03-27 DOI:10.1093/biomtc/ujae054
Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty
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引用次数: 0

Abstract

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

采用非参数方法估计零膨胀计数结果移动健康中的因果偏移效应。
在移动医疗领域,为实时交付量身定制的干预措施至关重要。微型随机试验已成为开发此类干预措施的 "黄金标准 "方法。通过分析这些试验的数据,可以深入了解干预措施的效果以及特定协变量的潜在调节作用。因果偏离效应 "是一类新的因果估计值,可以解决这些问题。然而,现有的研究主要集中在连续或二元数据上,而对计数数据的研究还很少。目前的研究是受英国 "少喝酒 "微观随机试验的启发,该试验侧重于零膨胀的近端结果,即干预决策点后一小时内的屏幕浏览次数。具体来说,我们重新审视了因果偏移效应的概念,特别是针对零膨胀计数结果,并引入了结合非参数技术的新型估算方法。我们为所提出的估计方法建立了双向渐近线。我们还进行了模拟研究,以评估所提出方法的性能。作为说明,我们还将这些方法应用于饮酒少试验数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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