在观察性研究中研究中等效应修正的有力方法
A powerful approach to the study of moderate effect modification in observational studies
摘要：
效应修饰是指处理效应的幅度或稳定性随观察到的协变量而变化。一般来说，较大和较稳定的处理效应对来自未测量协变量的较大偏倚不敏感，因此如果出现这种模式，则因果关系结论可能更加可靠。我们提出了一种新的策略，称为次最大值方法，结合探索性和验证性的努力，以确定是否有更强的因果关系的证据-也就是说，更大的不敏感性未测量的混杂-在一些亚组的个人。它使用检验统计量的联合分布，根据某些观察到的协变量以各种方式分割数据。对于$L$二元协变量，该方法将总体$L$倍分为两个亚群，可能首先是男性和女性，然后可能是吸烟者和非吸烟者，计算每个亚群的检验统计量，并附加整个总体的检验统计量，从而得到总的$2L+1$检验统计量。尽管$L$二元协变量定义了$2^{L}$交互作用组，但仅执行了$2L+1$检验，并且这些检验中至少有$L+1$使用了至少一半的数据。次最大值方法实现了最高的设计灵敏度和最高的Bahadur效率的组件测试。此外，测试的形式是足够容易处理的，其大样本功率可以分析研究。仿真结果表明，次最大的方法表现出优越的性能，与使用CART的方法相比，当有中等大小的效果修改。使用NHANES I流行病学随访调查的数据，体力活动对生存率影响的观察性研究用于说明该方法。该方法在包含NHANES示例的$\texttt{R}$包$\texttt{submax}$中实现。
英文摘要：
Effect modification means the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if this pattern is noted if it occurs. We propose a new strategy, called the submax-method, that combines exploratory and confirmatory efforts to determine whether there is stronger evidence of causality - that is, greater insensitivity to unmeasured confounding - in some subgroups of individuals. It uses the joint distribution of test statistics that split the data in various ways based on certain observed covariates. For $L$ binary covariates, the method splits the population $L$ times into two subpopulations, perhaps first men and women, perhaps then smokers and nonsmokers, computing a test statistic from each subpopulation, and appends the test statistic for the whole population, making $2L+1$ test statistics in total. Although $L$ binary covariates define $2^{L}$ interaction groups, only $2L+1$ tests are performed, and at least $L+1$ of these tests use at least half of the data. The submax-method achieves the highest design sensitivity and the highest Bahadur efficiency of its component tests. Moreover, the form of the test is sufficiently tractable that its large sample power may be studied analytically. The simulation suggests that the submax method exhibits superior performance, in comparison with an approach using CART, when there is effect modification of moderate size. Using data from the NHANES I Epidemiologic Follow-Up Survey, an observational study of the effects of physical activity on survival is used to illustrate the method. The method is implemented in the $\texttt{R}$ package $\texttt{submax}$ which contains the NHANES example.
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