The law of attrition.
The law of attrition.
复制标题
消耗法则。
DOI：
10.2196/jmir.7.1.e11
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发表时间：
2005-03-31
影响因子：
7.4
通讯作者：
Eysenbach, G
中科院分区：
文献类型：
--
作者：
Eysenbach, G
在本杂志正在进行的努力，以发展和进一步的理论，模型和最佳实践，围绕电子健康研究，本文认为需要一个“科学的损耗”，也就是说，需要开发模型停止电子健康应用程序和相关的现象的参与者退出电子健康试验。我在这里所说的“自然损耗定律”是这样一种观察，即在任何电子健康试验中，都有相当一部分用户在完成之前退出或停止使用应用程序。与药物试验等相比，电子健康试验的这一特点是一个独特的特征。传统的临床试验和循证医学范式规定，高脱落率使试验不太可信。因此，电子健康研究人员往往掩盖高辍学率，或者根本不发表他们的研究结果，因为他们认为他们的研究是失败的。然而，对于许多电子保健试验，特别是那些在互联网上进行的试验，特别是自助应用程序，高辍学率可能是一个自然和典型的特点。应该强调、测量、分析和讨论使用指标和流失的决定因素。这也包括分析和报告应用程序最终“有效”的亚群特征，即那些留在试验中并使用它的人。对于什么有效和什么无效的问题，这种流失措施与意向治疗（ITT）分析的纯疗效措施一样重要。在高脱落率的情况下，疗效指标低估了应用对继续使用它的人群的影响。分析消耗曲线的方法可以从生存分析方法中得出，例如Kaplan-Meier分析和比例风险回归分析（考克斯模型）。应报告的衡量标准包括退出或停止使用某一应用程序的相对风险，以及“使用半衰期”，以及报告人口统计学和其他因素的模型，这些因素可预测人口中的使用中断情况。两种干预措施之间的差异退出率或使用率可以作为系统“可用性功效”的标准度量。一个“导入和退出”的试验设计，建议作为一种基于互联网的试验的方法创新，大量的初始脱落/非用户和一个稳定的铁杆用户组。
In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.
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影响因子：
7.4
作者：
Wu RC;Delgado D;Costigan J;Maciver J;Ross H
通讯作者：
Ross H
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7.4
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7.4
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影响因子：
7.4
作者：
Farvolden, Peter;Denisoff, Eilenna;Selby, Peter;Bagby, R Michael;Rudy, Laura
通讯作者：
Rudy, Laura