Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.

翻译：因此,对病人分组同样有效的治疗对不同病例组合的病人群体可能具有不同的效力,因此,元分析对于病人群体描述的治疗效果必须明确。为了实现这一点,我们制定了一种对随机临床试验进行元分析的新方法,利用所有试验的个别病人数据(IPD)来推断在特定试验中对病人群体的治疗效果,其依据是直接标准化,使用结果回归或反概率加权法(IPW)进行直接标准化。同时开发了随机效应元分析模型。新的方法使得由于案件与案件混合的原因混合而导致的异异。