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Evaluating Disease Management Program Effectiveness: An Introduction to Survival Analysis

机译：评估疾病管理计划的有效性：生存分析简介

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Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.
机译：当前，在疾病管理行业中用于评估程序有效性的最广泛使用的方法是“总人口方法”。该模型是前测-后测设计，最基本的限制是没有对照组，可能有偏见和/或竞争性的外部混杂因素，这些因素为解释从基线的变化提供了合理的理由。生存分析允许包含受检查病例的数据，这些受试者要么“幸存”了该程序而没有经历过该事件（例如，达到目标临床水平，住院），要么由于从健康计划中取消注册而过早离开了该程序，或者程序，或者被遗忘了。此外，模型中可以包含自变量，以帮助解释结果度量的可变性。为了最大程度地发挥这种统计方法的潜力，必须确保模型和研究设计的有效性。本文将生存分析作为一种评估DM计划有效性的替代方法，比目前的总人口方法更合适。

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