Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups.
Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups.
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DOI：
10.1002/sim.6083
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发表时间：
2014-06-15
影响因子：
2
通讯作者：
Murray, David M.
中科院分区：
文献类型：
--
作者：
Andridge, Rebecca R.;Shoben, Abigail B.;Muller, Keith E.;Murray, David M.
关键词：
参与试验的受试者可以单独或分组随机分配，可以完全单独、完全分组或部分单独和部分分组接受治疗。本文涉及的情况下，参与者接受他们的治疗，无论是完全或部分分组，无论他们是如何随机。分组随机试验（GRT）的参与者是随机分组的，而个体随机分组治疗（IRGT）试验的参与者是单独随机的，但这两种类型试验的参与者都是通过分组或通过共同的变更剂接受部分或全部治疗。在一个组中接受部分或全部治疗的参与者预期具有正相关的结果测量。本文讨论的情况下，发生在GRT和IRGT试验-参与者接受治疗，通过一个以上的组。作为动机，我们考虑了儿童肥胖预防和治疗研究联盟（COPTR）的试验，其中每个儿童参与者至少接受两组治疗。在模拟研究中，我们考虑了几种可能的分析方法在各种可能的组结构。两组随机效应的混合模型提供了唯一一致的保护，防止膨胀的I型错误率，这样做的代价是，当组内相关性不大时，功率只有适度的损失。我们建议将方差估计值限制为正值，并对自由度使用Kenward-Roger调整;这种组合提供了额外的功效，但将I类错误率维持在标称水平。
Participants in trials may be randomized either individually or in groups, and may receive their treatment either entirely individually, entirely in groups, or partially individually and partially in groups. This paper concerns cases in which participants receive their treatment either entirely or partially in groups, regardless of how they were randomized. Participants in Group-Randomized Trials (GRTs) are randomized in groups and participants in Individually Randomized Group Treatment (IRGT) trials are individually randomized, but participants in both types of trials receive part or all of their treatment in groups or through common change agents. Participants who receive part or all of their treatment in a group are expected to have positively correlated outcome measurements. This paper addresses a situation that occurs in GRTs and IRGT trials – participants receive treatment through more than one group. As motivation, we consider trials in The Childhood Obesity Prevention and Treatment Research Consortium (COPTR), in which each child participant receives treatment in at least two groups. In simulation studies we considered several possible analytic approaches over a variety of possible group structures. A mixed model with random effects for both groups provided the only consistent protection against inflated type I error rates and did so at the cost of only moderate loss of power when intraclass correlations were not large. We recommend constraining variance estimates to be positive and using the Kenward-Roger adjustment for degrees of freedom; this combination provided additional power but maintained type I error rates at the nominal level.
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