Department of Clinical Epidemiology, C7-P, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands.
Eur J Epidemiol. 2010 Jul;25(7):459-66. doi: 10.1007/s10654-010-9475-z. Epub 2010 Jun 15.
We discuss the analytic and practical considerations in a large case-control study that had two control groups; the first control group consisting of partners of patients and the second obtained by random digit dialling (RDD). As an example of the evaluation of a general lifestyle factor, we present body mass index (BMI). Both control groups had lower BMIs than the patients. The distribution in the partner controls was closer to that of the patients, likely due to similar lifestyles. A statistical approach was used to pool the results of both analyses, wherein partners were analyzed with a matched analysis, while RDDs were analyzed without matching. Even with a matched analysis, the odds ratio with partner controls remained closer to unity than with RDD controls, which is probably due to unmeasured confounders in the comparison with the random controls as well as intermediary factors. However, when studying injuries as a risk factor, the odds ratio remained higher with partner control subjects than with RRD control subjects, even after taking the matching into account. Finally we used factor V Leiden as an example of a genetic risk factor. The frequencies of factor V Leiden were identical in both control groups, indicating that for the analyses of this genetic risk factor the two control groups could be combined in a single unmatched analysis. In conclusion, the effect measures with the two control groups were in the same direction, and of the same order of magnitude. Moreover, it was not always the same control group that produced the higher or lower estimates, and a matched analysis did not remedy the differences. Our experience with the intricacies of dealing with two control groups may be useful to others when thinking about an optimal research design or the best statistical approach.
我们讨论了一项大型病例对照研究中的分析和实际考虑因素，该研究有两个对照组；第一对照组由患者的伴侣组成，第二对照组通过随机数字拨号（RDD）获得。作为评估一般生活方式因素的一个例子，我们展示了体重指数（BMI）。两个对照组的 BMI 均低于患者。伴侣对照组的分布更接近患者，可能是由于生活方式相似。我们使用一种统计方法来汇总两种分析的结果，其中伴侣对照组采用匹配分析，而 RDD 对照组则不进行匹配。即使进行了匹配分析，伴侣对照组的比值比仍然更接近 1，而不是与 RDD 对照组相比，这可能是由于与随机对照组相比存在未测量的混杂因素以及中介因素。然而，当研究损伤作为危险因素时，即使考虑到匹配，伴侣对照组的比值比仍然高于 RRD 对照组。最后，我们以因子 V 莱顿（factor V Leiden）为例，说明了遗传危险因素。两个对照组中的因子 V 莱顿频率相同，表明对于该遗传危险因素的分析，可以将两个对照组合并为一个不匹配的单一分析。总之，两个对照组的效应测量值方向相同，数量级相同。此外，并非总是同一个对照组产生更高或更低的估计值，匹配分析也无法纠正这些差异。我们在处理两个对照组时的复杂经验可能对其他人在考虑最佳研究设计或最佳统计方法时有所帮助。
