Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK.
Eur J Epidemiol. 2009;24(12):727-31. doi: 10.1007/s10654-009-9412-1. Epub 2009 Dec 5.
Efforts to identify risk factors for chronic diseases have tended to involve observational studies characterised by relatively few disease outcomes. In the absence of individual studies of sufficiently large size, synthesis of available evidence from multiple smaller studies can help enhance statistical power and aid appropriate interpretation. While meta-analyses of published findings can help prioritize research hypotheses, they are inherently limited by the scale of the evidence available for review and by vulnerability to potential reporting biases. By contrast, collaborative analyses of individual participant data from a comprehensive set of relevant epidemiological studies can offer several advantages over moderately sized individual studies or meta-analyses of aggregated data. This review describes those advantages with reference to selected examples.
为了确定慢性病的风险因素，人们往往倾向于进行观察性研究，这些研究的疾病结局相对较少。在缺乏足够大规模的个体研究的情况下，对多个较小研究的可用证据进行综合分析有助于提高统计学效力并有助于进行适当的解释。虽然对已发表研究结果的荟萃分析有助于确定研究假设的优先级，但它们本质上受到可供审查的证据规模以及潜在报告偏倚的影响。相比之下，对一组全面的相关流行病学研究的个体参与者数据进行协作分析，可以为适度规模的个体研究或汇总数据的荟萃分析提供几个优势。本文通过引用一些示例来描述这些优势。
