• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Health services & outcomes research methodology >A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes
【24h】

A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes

机译：有关选择和解释序数健康相关结果的预测模型的教程

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Ordinal variables are very often objects of study in health sciences. However, due to the lack of dissemination of models suited for ordinal variables, users often adopt other practices that result in the loss of statistical power. In this tutorial, different models from the family of logistic regression models are introduced as alternatives to handle and interpret ordinal outcomes. The models that were considered include: ordinal regression model (ORM), continuation ratio model (CRM), adjacent category model (ACM), generalised ordered logit model, sequential model, multinomial logit model, partial proportional odds model, partial continuation ratio model and stereotype ordered regression model. By using the relationship of hospital length of stay in a public hospital in Mexico with patient characteristics as an example, the models were used to describe the nature of such relationship and to predict the length of stay category to which a patient is most likely to belong. After an initial analysis, the ORM, CRM and ACM proved to be unsuitable for our data due to the transgression of the parallel regression assumption. The rest of the models were estimated in STATA. The results suggested analogous directionality of the parameter estimates between models, although the interpretation of the odds ratios varied from one model to another. Performance measurements indicated that the models had similar prediction performance. Therefore, when there is an interest in exploiting the ordinal nature of an outcome, there is no reason to maintain practices that ignore such nature since the models discussed here proved to be computationally inexpensive and easy to estimate, analyse and interpret.
机译：序数变量经常是卫生科学的研究对象。但是，由于缺乏适用于序数变量的模型的传播，用户经常采用其他做法，从而导致统计能力的丧失。在本教程中，引入了逻辑回归模型家族中的不同模型作为处理和解释序数结果的替代方法。考虑的模型包括：顺序回归模型（ORM），连续比率模型（CRM），相邻类别模型（ACM），广义有序logit模型，顺序模型，多项式logit模型，偏比例赔率模型，偏连续比率模型和刻板印象有序回归模型。以墨西哥某公立医院住院时间与患者特征的关系为例，使用该模型描述这种关系的性质，并预测患者最有可能属于的住院时间类别。经过初步分析，由于违反了平行回归假设，因此ORM，CRM和ACM不适合我们的数据。其余模型在STATA中估算。结果表明模型之间的参数估计具有相似的方向性，尽管从一个模型到另一个模型，优势比的解释也有所不同。性能测量表明，这些模型具有相似的预测性能。因此，当有兴趣利用结果的序数性质时，没有理由保持忽略这种性质的做法，因为此处讨论的模型被证明在计算上不昂贵并且易于估计，分析和解释。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号