Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands.
Int J Epidemiol. 2012 Apr;41(2):514-20. doi: 10.1093/ije/dyr218. Epub 2012 Jan 9.
Authors often do not give sufficient information to draw conclusions about the size and statistical significance of interaction on the additive and multiplicative scales. To improve this, we provide four steps, template tables and examples. We distinguish two cases: when the causal effect of intervening on one exposure, across strata of another factor, is of interest ('effect modification'); and when the causal effect of intervening on two exposures is of interest ('interaction'). Assume we study whether X modifies the effect of A on D, where A, X and D are dichotomous. We propose presenting: (i) relative risks (RRs), odds ratios (ORs) or risk differences (RDs) for each (A, X) stratum with a single reference category taken as the stratum with the lowest risk of D; (ii) RRs, ORs or RDs for A within strata of X; (iii) interaction measures on additive and multiplicative scales; (iv) the A-D confounders adjusted for. Assume we study the interaction between A and B on D, where A, B and D are dichotomous. Steps (i) and (iii) are similar to presenting effect modification. (ii) Present RRs, ORs or RDs for A within strata of B and for B within strata of A. (iv) List the A-D and B-D confounders adjusted for. These four pieces of information will provide a reader the information needed to assess effect modification or interaction. The presentation can be further enriched when exposures have multiple categories. Our proposal hopefully encourages researchers to present effect modification and interaction analyses in as informative a manner as possible.
作者在对加性和乘法尺度上的交互作用进行大小和统计显著性的结论推断时,往往没有提供足够的信息。为了改善这一点,我们提供了四个步骤、模板表格和示例。我们区分了两种情况:当干预对一个暴露因素在另一个因素的分层中的因果效应感兴趣时(“效应修饰”);以及当干预对两个暴露因素的因果效应感兴趣时(“交互作用”)。假设我们研究 X 是否修饰了 A 对 D 的影响,其中 A、X 和 D 是二分变量。我们建议呈现:(i)每个(A,X)分层的相对风险(RR)、优势比(OR)或风险差异(RD),以最低风险 D 的分层作为单一参考类别;(ii)X 分层内的 A 中的 RR、OR 或 RD;(iii)加性和乘法尺度上的交互作用测量;(iv)调整后的 A-D 混杂因素。假设我们研究 A 和 B 对 D 的交互作用,其中 A、B 和 D 是二分变量。步骤(i)和(iii)类似于呈现效应修饰。(ii)呈现 B 分层内的 A 和 A 分层内的 B 的 RR、OR 或 RD。(iv)列出调整后的 A-D 和 B-D 混杂因素。这四部分信息将为读者提供评估效应修饰或交互作用所需的信息。当暴露因素有多个类别时,可以进一步丰富呈现。我们的建议希望鼓励研究人员以尽可能有信息的方式呈现效应修饰和交互作用分析。