Covariate balance in simple, stratified and clustered comparative studies
Covariate balance in simple, stratified and clustered comparative studies
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DOI：
10.1214/08-sts254
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发表时间：
2008-05-01
影响因子：
5.7
通讯作者：
Bowers, Jake
中科院分区：
文献类型：
--
作者：
Hansen, Ben B.;Bowers, Jake
在随机试验中，治疗组和对照组在前处理变量的分布上应该大致相同--平衡。但有多接近呢？描述性比较能与显着性检验配对吗？如果是这样，是否应该有几个这样的测试，每个前处理变量一个，还是应该有一个单一的，综合的测试？这样的测试能否设计成在中等大小的样本中提供可靠的易于计算的p值，或者需要进行模拟才能进行可靠的校准？集群的随机分配带来了哪些新的问题？哪些平衡测试是最优的？为了解决这些问题，费舍尔的随机化推理被应用到平衡问题上。它的应用表明，发表的关于两项研究的结论发生了逆转，一项是临床研究，另一项是关于政治参与的实地实验。
In randomized experiments, treatment and control groups should be roughly the same-balanced-in their distributions of pretreatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should there be a single, omnibus test? Could such a test be engineered to give easily computed p-values that are reliable in samples of moderate size, or would simulation be needed for reliable calibration? What new concerns are introduced by random assignment of clusters? Which tests of balance would be optimal?To address these questions, Fisher's randomization inference is applied to the question of balance. Its application suggests the reversal of published conclusions about two studies, one clinical and the other a field experiment in political participation.