Which of these things is not like the others?
Which of these things is not like the others?
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DOI：
10.1002/cncr.28359
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发表时间：
2013-12-15
期刊：
影响因子：
6.2
通讯作者：
MacLehose RF
中科院分区：
文献类型：
--
作者：
Kaufman JS;MacLehose RF
在病因学研究中，目标是估计暴露对疾病结局的因果影响，这意味着如果可以在不考虑研究参与者基线特征的情况下分配暴露，则在具有完美依从性的大型随机试验中将获得的结果。[1]但是，没有理由认为在随机试验或观察性研究中，各单位之间的效应一定是均匀的。在按年龄、性别或任何其他背景特征（无论是否测量）定义的亚组中，暴露可能导致或多或少的疾病。事实上，对人群的汇总估计可能反映了不同效应幅度的混合，甚至是同一治疗受益和受损的受试者的混合。2假设我们从一个完美的随机对照试验中获得因果效应的汇总估计，例如相对危险度（RR）= 1.74。然而，在基线变量的不同分层中，效应估计值通常会有所不同。假设男性的RR= 1.87，女性的RR= 1.65。现在有必要在两种对立的现实观点之间做出二元决定。第一种可能性（图1，图A）是两个层特异性估计值（1.87和1.65）是来自同质效应的单个基础抽样分布的两个独立数据。因此，这两个值之间的差异仅是由于采样变异性造成的。第二种可能性（图1，图B）是两个特定阶层的估计值（1.87和1.65）均来自其各自独特的特定阶层分布，因为男性和女性不具有相同的共同潜在效应量。在这种情况下，未分层的值1.74保证位于两个层特定值之间的某个位置。
In etiologic research, the goal is to estimate the causal effect of an exposure on a disease outcome, which means the result that would be obtained in a large randomized trial with perfect adherence if exposure could be assigned without regard to the baseline characteristics of the study participants. 1 But there is no reason to think that effects must be homogeneous across units in a randomized trial or in an observational study. Exposure may cause more or less disease in subgroups defined by age, sex, or any other background characteristic, whether measured or unmeasured. Indeed, the summary estimate over the population may reflect a mix of different effect magnitudes, or even a mix of subjects who are benefitted and harmed by the same treatment. 2Suppose that we obtain a summary estimate of causal effect from a perfectly conducted randomized control trial, for example a relative risk (RR)= 1.74. Across strata of baseline variables, however, the effect estimate will generally differ. Suppose that for men we observe RR= 1.87 and for an equal number of women, RR= 1.65. Now it is necessary to make a binary decision between two opposing views of reality. The first possibility (Figure 1, Panel A) is that the two stratum-specific estimates (1.87 and 1.65) are two independent draws from a single underlying sampling distribution of the homogenous effect. The difference between these two values is therefore due to sampling variability alone. The second possibility (Figure 1, Panel B) is that the two stratum-specific estimates (1.87 and 1.65) are each a draw from their own distinct stratum-specific distribution because men and women do not share the same common underlying effect magnitude. In this case, the unstratified value of 1.74 is guaranteed to lie somewhere between the two stratum-specific
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影响因子：
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作者：
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影响因子：
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作者：
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