Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada; Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada.
Pharmacoepidemiol Drug Saf. 2013 Nov;22(11):1178-88. doi: 10.1002/pds.3485. Epub 2013 Aug 13.
Interaction and subgroup analyses remain controversial topics in epidemiology. A recent theoretical paper suggested that a combination of no overall treatment-outcome association and treatment effect limited to a single subgroup would imply a clinically implausible interaction, with opposite treatment effects in the two subgroups. However, this argument was based entirely on point estimates and ignored sampling error and statistical inference.
We simulated hypothetical studies in which treatment truly affected the outcome in only one subgroup, with no effect in the other subgroup. We generated 1000 random samples for three study designs (small clinical study, case-control, and large cohort), and different values of total sample size (N), relative size of the affected subgroup, and treatment effect. We estimated the frequency of significant results for tests of overall and subgroup-specific treatment effects, and treatment-by-subgroup interaction.
Combination of statistically non-significant overall treatment effect and significant treatment-by-subgroup interaction occurred frequently, especially if the affected subgroup was proportionally smaller, even in studies with high power to detect the overall effect (e.g. in 37.1% of samples with N = 20 000, with 600 outcomes, and an effect (odds ratio of 1.5) limited to 30% of subjects). Furthermore, in most samples with a significant interaction, subgroup analyses correctly indicated that the significant effect was limited to one subgroup.
In studies where the treatment truly affects the risks in only one subgroup, a non-significant overall effect will often coincide with a statistically significant treatment-by-subgroup interaction. Thus, a non-significant overall effect should not prevent testing plausible interactions.
在流行病学中，交互作用和亚组分析仍然是有争议的话题。最近的一篇理论论文认为，如果没有总体治疗效果关联且治疗效果仅限于单个亚组，这将意味着存在临床上不合理的交互作用，即两个亚组的治疗效果相反。然而，这一论点完全基于点估计，忽略了抽样误差和统计推断。
我们模拟了治疗仅对一个亚组的结局有影响而对另一亚组无影响的真实情况的假设性研究。我们为三种研究设计（小型临床研究、病例对照和大型队列研究）生成了 1000 个随机样本，并针对不同的总样本量（N）、受影响亚组的相对大小和治疗效果进行了生成。我们估计了用于检验总体和亚组特异性治疗效果以及治疗与亚组交互作用的检验的显著结果的频率。
即使在具有高检测总体效果能力的研究中（例如，在 N=20000，结局数为 600，且效果（优势比为 1.5）仅限于 30%的受试者的样本中，有 37.1%的样本中，统计学上非显著的总体治疗效果与显著的治疗与亚组交互作用相结合的情况经常发生，特别是如果受影响的亚组相对较小。此外，在大多数具有显著交互作用的样本中，亚组分析正确地表明，显著的效果仅限于一个亚组。
在治疗仅对一个亚组的风险有影响的研究中，无统计学意义的总体效果通常与统计学上显著的治疗与亚组交互作用一致。因此，无统计学意义的总体效果不应阻止对合理交互作用的检验。
