THE ROLE OF KNOWN EFFECTS IN OBSERVATIONAL STUDIES
THE ROLE OF KNOWN EFFECTS IN OBSERVATIONAL STUDIES
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DOI：
10.2307/2531497
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发表时间：
1989-06-01
期刊：
影响因子：
1.9
通讯作者：
ROSENBAUM, PR
中科院分区：
文献类型：
--
作者：
ROSENBAUM, PR
当治疗不是随机分配时，治疗组和对照组受试者在治疗前可能会有很大不同，因此直接比较治疗组和对照组的反应可能会对治疗效果产生扭曲的印象。虽然对观察到的治疗前差异进行调整可能有所帮助，但通常有理由担心重要差异未被测量，也未通过统计调整进行控制。本文涉及的方法检测和指示这种未观察到的预处理差异。这里讨论的方法使用治疗对研究中包含的某些补充反应的已知影响，以提供有关未观察到的治疗前差异的信息。以前的工作已经指出，已知的影响提供了一个假设，即观察到的协变量调整足以消除偏见的统计检验的基础。在此，通过解决以下问题，进一步采取了几个步骤。这种检验的形式属性是什么？在什么情况下，睾丸可以有效检测未观察到的治疗前差异？或者换句话说，什么样的补充反应变量提供了最有效的检查？如果检测到未观察到的治疗前差异，那么关于它们产生的偏差的方向可以说些什么？当然，这些统计学检验受可用样本量的影响，因为它们受其预期检测的未观察到的治疗前差异的大小的影响。考虑到没有直接观察到所讨论的治疗前差异，关于它们的大小可以得出什么结论？为了激发讨论，两个例子在整个文件进行了讨论。
When treatments are not randomly assigned, treated and control subjects may be quite different prior to treatment, so that straight forward comparisons of responses in treated and control groups may give a distorted impression of the effect of the treatment. While adjustments for observed pretreatment differences can help, there is often reason for concern that important differences were not measured and not controlled by statistical adjustments. This paper concerns methods for detecting and indicating of such unobserved pretreatment differences. The methods discussed here use known effects of the treatment on certain supplementary responses included in the study to provide information about unobserved pretreatment differences. Previous work has noted that know effects provide the basis for a statistical test of the assumption that adjustments for observed covariates suffice removing bias. Here, the matter is taken several steps further by addressing the following questions. What are the formal properties of such test? Under what circumstances are the testes effective at detecting unobserved pretreatment differences? Or to put it another way, what sorts of supplementary response variables provide the most effevctive checks? If unobserved pretreatment differences are detected, what can be said about the direction of the biases they produce? These statistical tests are, of course, as influenced by the available sample size as they are by the magnitude of the unobserved pretreatment differences they are intended to detect. Given that the pretreatment differences in question are not observed directly, what can be concluded about their magnitude? To motivate the discussion, two examples are discussed throughout the paper.