比较多类别结果的风险预测方法:二分法逻辑回归与多项式逻辑回归。

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Lei Li, Matthew A Rysavy, Georgiy Bobashev, Abhik Das
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引用次数: 0

摘要

背景:临床医生感兴趣的医疗结果可能有多个类别。研究人员面临着对此类结果进行风险预测的几种选择,包括二分法逻辑回归和多项式逻辑回归模型。我们旨在对这些方法进行比较,并提供实践所需的指导:我们介绍了二分法逻辑回归、多二项延续比逻辑回归(这是针对序数结果的标准多二项逻辑回归的替代方法)和逻辑竞争风险回归。然后,我们应用这些方法开发了基于美国国家儿童疾病防治中心极早产儿结局工具模型的存活率和神经发育结局预测模型。我们研究了这些方法在统计学和实用性方面的优势和缺陷。对估计的二分法结果逻辑模型和延续比逻辑模型的区分度和校准进行了评估:结果:在预测无神经发育障碍的死亡和存活率方面,二分法逻辑模型和多二项式延续比对数模型具有相似的辨别力和校准性。但连续比对数模型在预测神经发育障碍方面具有更好的区分度和校准性。二分法逻辑模型对结果类别的预测概率之和与100%的偏差很大,从87.7%到124.0%不等,神经发育障碍的二分法逻辑模型大大高估了低风险,低估了高风险:结论:估计二分法结果的多重逻辑回归模型可能会导致对具有多个序数类别的结果的预测校准不良。多项式延续比 logit 回归能得出更好的校准预测结果,将预测概率之和限制在 100%,并且具有模型解释简单、可灵活纳入结果类别特异性预测因子和医院患者异质性随机效应项等优点。它还考虑了多个类别之间的相互依赖性,并适应竞争风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing methods for risk prediction of multicategory outcomes: dichotomized logistic regression vs. multinomial logit regression.

Background: Medical outcomes of interest to clinicians may have multiple categories. Researchers face several options for risk prediction of such outcomes, including dichotomized logistic regression and multinomial logit regression modeling. We aimed to compare these methods and provide guidance needed for practice.

Methods: We described dichotomized logistic regression, multinomial continuation-ratio logit regression, which is an alternative to standard multinomial logit regression for ordinal outcomes, and logistic competing risks regression. We then applied these methods to develop prediction models of survival and neurodevelopmental outcomes based on the NICHD Extremely Preterm Birth Outcome Tool model. The statistical and practical advantages and flaws of these methods were examined. Both discrimination and calibration of the estimated logistic models of dichotomized outcomes and continuation-ratio logit model were assessed.

Results: The dichotomized logistic models and multinomial continuation-ratio logit model had similar discrimination and calibration in predicting death and survival without neurodevelopmental impairment. But the continuation-ratio logit model had better discrimination and calibration in predicting neurodevelopmental impairment. The sum of predicted probabilities of outcome categories from the dichotomized logistic models could deviate from 100% substantially, ranging from 87.7 to 124.0%, and the dichotomized logistic model of neurodevelopmental impairment greatly overpredicted low risks and underpredicted high risks.

Conclusions: Estimating multiple logistic regression models of dichotomized outcomes may result in poorly calibrated predictions for an outcome with multiple ordinal categories. Multinomial continuation-ratio logit regression produces better calibrated predictions, constrains the sum of predicted probabilities to 100%, and has the advantages of simplicity in model interpretation, flexibility to include outcome category-specific predictors and random-effect terms for patient heterogeneity by hospital. It also accounts for mutual dependence among multiple categories and accommodates competing risks.

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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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