An analytic method for randomized trials with informative censoring: Part II.
An analytic method for randomized trials with informative censoring: Part II.
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DOI：
10.1007/bf00985453
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发表时间：
1995-01-01
影响因子：
1.3
通讯作者：
Robins, J M
中科院分区：
文献类型：
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作者：
Robins, J M
考虑一项随机试验，其中特定疾病（例如艾滋病试验中的肺囊性肺炎或乳房X光筛查试验中的乳腺癌）发生的时间是主要关注的失败时间。假设至疾病时间受至死亡、失访和结束随访时间的最小值的信息删失。在这样的试验中，观察所有研究受试者的潜在删失时间，包括失败。在存在信息删失的情况下，不可能在不施加额外的不可识别假设的情况下一致地估计治疗对至疾病时间的影响。Robins（1995）规定了两个不可识别的假设，允许在存在信息删失的情况下检验和估计治疗对至疾病时间的影响。本文的目的是提供一类一致的和合理有效的半参数检验和估计的治疗效果在这些假设。在我们的类中的测试，像标准的加权对数秩检验，是渐近分布自由的α水平检验下的治疗时间的无因果关系的治疗效果的零假设，只要删失和失败的分布是有条件的独立给定的治疗arm.However，我们的测试仍然是渐近分布自由的α水平检验中存在的信息删失提供我们的假设是真的。相比之下，仅当（1）我们的两个不可识别假设之一成立，以及（2）两个治疗组中至删失时间的分布相同时，加权对数秩检验才是存在信息性删失的α水平检验。我们还研究了在信息删失的情况下，治疗对重复测量结果（如CD4计数）平均值随时间演变的影响的估计。
Consider a randomized trial in which time to the occurrence of a particular disease, say pneumocystic pneumonia in an AIDS trial or breast cancer in a mammographic screening trial, is the failure time of primary interest. Suppose that time to disease is subject to informative censoring by the minimum of time to death, loss to and end of follow-up. In such a trial, the potential censoring time is observed for all study subjects, including failure. In the presence of informative censoring, it is not possible to consistently estimate the effect of treatment on time to disease without imposing additional non-identifiable assumptions. Robins (1995) specified two non-identifiable assumptions that allow one to test for and estimate an effect of treatment on time to disease in the presence of informative censoring. The goal of this paper is to provide a class of consistent and reasonably efficient semiparametric tests and estimators for the treatment effect under these assumptions. The tests in our class, like standard weighted-log-rank tests, are asymptotically distribution-free alpha-level tests under the null hypothesis of no causal effect of treatment on time to disease whenever the censoring and failure distributions are conditionally independent given treatment arm. However, our tests remain asymptotically distribution-free alpha-level tests in the presence of informative censoring provided either of our assumptions are true. In contrast, a weighted log-rank test will be an alpha-level test in the presence of informative censoring only if (1) one of our two non-identifiable assumptions hold, and (2) the distribution of time to censoring is the same in the two treatment arms. We also study the estimation, in the presence of informative censoring, of the effect of treatment on the evolution over time of the mean of repeated measures outcome such as CD4 count.