@Article{信息:doi 10.2196 / / jmir.7.1。e11，作者=“Eysenbach, Gunther”，标题=“消耗定律”，期刊=“J Med Internet Res”，年=“2005”，月=“3”，日=“31”，卷=“7”，号=“1”，页=“e11”，关键词=“Internet”;临床试验;纵向研究;病人辍学;生存分析”，摘要=“本刊正在努力发展和进一步的理论，模型，以及围绕电子健康研究的最佳实践，本文认为需要一个“损耗科学”，也就是说，需要为电子健康应用程序的中断和参与者退出电子健康试验的相关现象开发模型。”我称之为“消耗定律”的是，在任何电子健康试验中，相当大比例的用户在完成或停止使用应用程序之前就退出了。与药物试验等相比，电子卫生试验的这一特点是一个明显的特点。传统的临床试验和循证医学范式规定，高辍学率会降低试验的可信度。因此，电子健康研究人员倾向于掩盖高辍学率，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。 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. ", issn="1438-8871", doi="10.2196/jmir.7.1.e11", url="//www.mybigtv.com/2005/1/e11/", url="https://doi.org/10.2196/jmir.7.1.e11", url="http://www.ncbi.nlm.nih.gov/pubmed/15829473" }