基于logistic回归的连续型解释变量离散化方法效果比较

doi:10.3969/j.issn.1006-5253.2022.04.016

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Abstract Objective To explore the advantages and disadvantages of different data discretization methods when the continuous independent variable and logit-π-do not satisfy the linear relationship, and to provide reference for the continuous independent variable discretization in logistic regression analysis.Methods A casecontrol study was used to generate simulated data from the three perspectives of effect size, number of independent variables, and sample size with R software. The continuous independent variables were further processed by different discretization to fit logistic regression models, and the fitting effects of different methods were compared.Results The results of different simulation data sets showed that when the four continuous variable processing methods were used to fit the logistic regression model, "maximum OR values method" could better screen out meaningful influencing factors. At the same time, its model fitting effect was also the best, which was shown as the smallest-AIC, the largest Nagelkerke R², and the higher correct total rate.Conclusion It is recommended to use "maximum-OR-values method" to discrete continuous variables if the relationship between these variables and logit^π is non-monotonic.

Key words： logistic regression semi-parametric regression discretization maximum-OR-value method

Received: 14 May 2022

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	He Xianying

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He Xianying. Comparison of the effects of discretization methods of continuous explanatory variables based on logistic regression[J]. journal1, 2022, 29(4): 312-317.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2022.04.016 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2022/V29/I4/312

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