Chop-Lump Tests for Vaccine Trials
Chop-Lump Tests for Vaccine Trials
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DOI：
10.1111/j.1541-0420.2008.01131.x
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发表时间：
2009-09-01
期刊：
影响因子：
1.9
通讯作者：
Proschan, Michael
中科院分区：
文献类型：
--
作者：
Follmann, Dean;Fay, Michael P.;Proschan, Michael
这篇文章提出了在随机疫苗试验中，当一小部分志愿者被感染时，比较疫苗组和安慰剂组的新测试。一种与意向治疗原则相一致的简单方法是给未感染者分配一个分数，比如W，等于0，而感染后的一些结果等于0。然后可以测试两组之间W的倾斜分布是否相等。这种疾病负担（BOI）检验由Chang、Guess和Heyse （1994, Statistics in Medicine 13,1807 -1814）提出。如果感染很少，每组中大量的0往往会稀释疫苗的效果，这种测试可能效果不佳，特别是在X不接近于零的情况下。在感染者中比较X不再是随机分组的比较，可能会产生误导性的结论。Gilbert, Bosch, and Hudgens （2003, biometics 59, 531-541）和Hudgens， Hoering, and Self （2003, Statistics in Medicine 22, 2281-2298）引入了亚组中X相等性的检验，即在随机分配下“注定”感染的主要人群。这可能比BOI方法更强大，但需要无法检验的假设。我们建议在某些情况下使用比BOI测试更强大的新的“切块”Wilcoxon和t测试（CLW和CLT）。当每一组的志愿者人数相等时，切块测试从两组中去除相同数量的零，然后对剩余的W进行测试，这些W大多是b> 0。排列方法提供了零分布。我们表明，在局部替代方案下，如果真正的疫苗感染率和安慰剂感染率相同，CLW测试总是比通常的Wilcoxon测试更有效。我们还确定了t检验功率上0和X之间的“差距”的关键作用。通过模拟计划中的艾滋病毒和疟疾疫苗试验，将这些切片试验与已建立的试验进行比较。本文用对第一期HIV疫苗试验的再分析来说明这种方法。
P>This article proposes new tests to compare the vaccine and placebo groups in randomized vaccine trials when a small fraction of volunteers become infected. A simple approach that is consistent with the intent-to-treat principle is to assign a score, say W, equal to 0 for the uninfecteds and some postinfection outcome X > 0 for the infecteds. One can then test the equality of this skewed distribution of W between the two groups. This burden of illness (BOI) test was introduced by Chang, Guess, and Heyse (1994, Statistics in Medicine 13, 1807-1814). If infections are rare, the massive number of 0s in each group tends to dilute the vaccine effect and this test can have poor power, particularly if the X's are not close to zero. Comparing X in just the infecteds is no longer a comparison of randomized groups and can produce misleading conclusions. Gilbert, Bosch, and Hudgens (2003, Biometrics 59, 531-541) and Hudgens, Hoering, and Self (2003, Statistics in Medicine 22, 2281-2298) introduced tests of the equality of X in a subgroup-the principal stratum of those "doomed" to be infected under either randomization assignment. This can be more powerful than the BOI approach, but requires unexaminable assumptions. We suggest new "chop-lump" Wilcoxon and t-tests (CLW and CLT) that can be more powerful than the BOI tests in certain situations. When the number of volunteers in each group are equal, the chop-lump tests remove an equal number of zeros from both groups and then perform a test on the remaining W's, which are mostly > 0. A permutation approach provides a null distribution. We show that under local alternatives, the CLW test is always more powerful than the usual Wilcoxon test provided the true vaccine and placebo infection rates are the same. We also identify the crucial role of the "gap" between 0 and the X's on power for the t-tests. The chop-lump tests are compared to established tests via simulation for planned HIV and malaria vaccine trials. A reanalysis of the first phase III HIV vaccine trial is used to illustrate the method.