Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm.
Stat Med. 2013 Aug 15;32(18):3067-76. doi: 10.1002/sim.5767. Epub 2013 Mar 3.
A popular way to control for confounding in observational studies is to identify clusters of individuals (e.g., twin pairs), such that a large set of potential confounders are constant (shared) within each cluster. By studying the exposure-outcome association within clusters, we are in effect controlling for the whole set of shared confounders. An increasingly popular analysis tool is the between-within (BW) model, which decomposes the exposure-outcome association into a 'within-cluster effect' and a 'between-cluster effect'. BW models are relatively common for nonsurvival outcomes and have been studied in the theoretical literature. Although it is straightforward to use BW models for survival outcomes, this has rarely been carried out in practice, and such models have not been studied in the theoretical literature. In this paper, we propose a gamma BW model for survival outcomes. We compare the properties of this model with the more standard stratified Cox regression model and use the proposed model to analyze data from a twin study of obesity and mortality. We find the following: (i) the gamma BW model often produces a more powerful test of the 'within-cluster effect' than stratified Cox regression; and (ii) the gamma BW model is robust against model misspecification, although there are situations where it could give biased estimates.
一种常用于观察性研究的控制混杂因素的方法是识别个体簇（例如，双胞胎对），从而使每个簇内的大量潜在混杂因素保持恒定（共享）。通过在簇内研究暴露-结局关联，我们实际上可以控制整个共享混杂因素集。一种越来越流行的分析工具是组间-组内（BW）模型，它将暴露-结局关联分解为“组内效应”和“组间效应”。BW 模型对于非生存结局相对常见，并且已经在理论文献中进行了研究。尽管对于生存结局可以直接使用 BW 模型，但在实践中很少这样做，并且此类模型在理论文献中也没有得到研究。在本文中，我们提出了一种用于生存结局的伽马 BW 模型。我们比较了该模型与更标准的分层 Cox 回归模型的特性，并使用所提出的模型来分析肥胖和死亡率的双胞胎研究数据。我们发现：（i）与分层 Cox 回归相比，伽马 BW 模型通常对“组内效应”进行更有力的检验；（ii）伽马 BW 模型对模型误设具有稳健性，尽管在某些情况下它可能会给出有偏的估计。
