A new strategy for meta-analysis of continuous covariates in observational studies

When several studies are available, a meta-analytic assessment of the effect of a risk or prognostic factor on an outcome is often required. We propose a new strategy, requiring individual participant data, to provide a summary estimate of the functional relationship between a continuous covariate and the outcome in a regression model, adjusting for confounding factors. Our procedure comprises three steps. First, we determine a confounder model. Ideally, the latter should include the same variables across studies, but this may be impossible. Next, we estimate the functional form for the continuous variable of interest in each study, adjusted for the confounder model. Finally, we combine the individual functions by weighted averaging to obtain a summary estimate of the function. Fractional polynomial methodology and pointwise weighted averaging of functions are the key components. In contrast to a pooled analysis, our approach can reflect more variability between functions from different studies and more flexibility with respect to confounders. We illustrate the procedure by using data from breast cancer patients in the Surveillance, Epidemiology, and End Results Program database, where we consider data from nine individual registries as separate studies. We estimate the functional forms for the number of positive lymph nodes and age. The former is an example where a strong prognostic effect has long been recognized, whereas the prognostic effect of the latter is weak or even controversial. We further discuss some general issues that are found in meta-analyses of observational studies.     

基于阴性对照理论的未测量混杂因素控制方法

非随机对照研究中未测量混杂因素的控制极具挑战。阴性对照理论基于“阴性对照的检测结果必须阴性”的思想，在进行人群研究时，额外设置合适的阴性对照，将关联特异度的思想融入人群研究中进行未测量混杂因素的识别和控制。本文从统计学角度解析阴性对照理论控制未测量混杂因素的基本原理，详细介绍基于阴性对照理论的系列衍生方法：死亡率标准化校正法、校正P值法、广义双重差分模型以及双向阴性对照法，并结合代表性案例对其合理应用进行述评。阴性对照是识别、校正和控制未测量混杂因素的重要统计设计思想，是基于现实世界数据开展实效比较研究的重要方法。