Bias mechanisms in intention-to-treat analysis with data subject to treatment noncompliance and missing outcomes
Bias mechanisms in intention-to-treat analysis with data subject to treatment noncompliance and missing outcomes
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DOI：
10.3102/1076998607302635
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发表时间：
2008-06-01
影响因子：
2.4
通讯作者：
Jo, Booil
中科院分区：
文献类型：
--
作者：
Jo, Booil
采用分析方法比较了不同治疗不依从性和无应答行为假设条件下因果效应估计的敏感性。这种方法的核心是充分阐明所考虑的模型的偏置机制，并根据共同的参数连接这些模型。侧重于意向治疗分析，在明确的偏倚机制和模型之间的连接性的基础上进行系统的模型比较。该方法适用于约翰霍普金斯学校的干预试验，在那里评估的意向治疗对学童的心理健康的影响很可能会受到干预不依从性和无反应的假设在后续评估。这个例子提醒人们注意，在调查具有不同识别假设的因果效应估计的相对敏感性时，必须关注每一个案例，而不是追求适用于每一种情况的一般性结论。
An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis, systematic model comparisons are performed on the basis of explicit bias mechanisms and connectivity between models. The method is applied to the Johns Hopkins school intervention trial, where assessment of the intention-to-treat effect on school children's mental health is likely to be affected by assumptions about intervention noncompliance and nonresponse at follow-up assessments. The example calls attention to the importance of focusing on each case in investigating relative sensitivity of causal effect estimates with different identifying assumptions, instead of pursuing a general conclusion that applies to every occasion.