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首页> 外文期刊>The British journal of psychiatry : >Describing, explaining or predicting mental health care costs: a guide to regression models
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Describing, explaining or predicting mental health care costs: a guide to regression models

机译：描述，解释或预测精神卫生保健费用：回归模型指南

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Background Analysis of the patterns of variation in health care costs and the determinants of these costs (including treatment differences) is an increasingly important aspect of research into the performance of mental health services. Aims To encourage both investigators of the variation in health care costs and the consumers of their investigations to think more critically about the precise aims of these investigations and the choice of statistical methods appropriate to achieve them. Method We briefly describe examples of regression models that might be of use in the prediction of mental health costs and how one might choose which one to use for a particular research project. Conclusions If the investigators are primarily interested in explanatory mechanisms then they should seriously consider generalised linear models (but with careful attention being paid to the appropriate error distribution).Further insight is likely to be gained through the use of two-part models.For prediction we recommend regression on raw costs using ordinary least-square methods.Whatever method is used, investigators should consider how robust their methods might be to incorrect distributional assumptions (particularly in small samples) and they should not automatically assume that methods such as bootstrapping will allow them to ignore these problems.
机译：背景分析医疗保健费用的变化模式以及这些费用的决定因素（包括治疗差异）是研究精神保健服务绩效的一个日益重要的方面。目的鼓励卫生保健费用变化的调查者和调查的消费者都更加批判性地考虑这些调查的确切目的以及为实现这些目的而选择的统计方法。方法我们简要描述了可能用于预测心理健康成本的回归模型的示例，以及如何选择用于特定研究项目的回归模型。结论如果研究者主要对解释机制感兴趣，那么他们应该认真考虑广义线性模型（但要注意适当的误差分布），通过使用两部分模型可能会获得更多的见解。我们建议使用普通最小二乘法对原始成本进行回归。无论使用哪种方法，调查人员都应考虑其方法对不正确的分布假设（尤其是小样本）的稳健性，并且不应自动假设诸如自举法之类的方法会允许他们忽略了这些问题。

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