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Reducing bias in ecological studies: an evaluation of different methodologies

机译:减少生态研究中的偏见:对不同方法的评价

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Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between-area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.
机译:为了减少生态偏差,对生态分析的统计方法进行了经验评估,以确定每种方法在哪种情况下都是可行的。减少生态偏差最成功的方法是分层生态回归。它允许将各个级别的协变量信息纳入分层的生态分析中,以及将疾病和危险因素信息组合到两个单独的数据源中,例如来自癌症登记处的结果和来自匿名记录数据集的人口普查样本中的危险因素信息。聚合的个人级别模型与此模型相比具有优势,但存在收敛性问题。此外,事实表明,地方政府辖区所覆盖的大片区域似乎减少了区域间的差异,因此可能不如进行病房级分析那样提供信息。这具有政策含义,因为对病房级别数据的访问受到限制。

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