Implementation of conditional Logistic regression in case-crossover study in Python

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Author: Qing-Yu ZHANG Pei-Zheng LI Chen-Xi LUO Xiang-Ying LI  Lu MA

Affiliation: School of Public Health, Wuhan University, Wuhan 430071, China

Keywords: Python Conditional Logistic regression Case-crossover study

DOI: 10.12173/j.issn.1004-5511.202302031

Reference: Zhang QY, Li PZ, Luo CX, Li XY, Ma L. Implementation of conditional Logistic regression in case-crossover study in Python[J]. Journal of Mathematical Medicine, 2023, 36(5): 321-325. DOI:10.12173/j.issn.1004-5511.202302031[Article in Chinese]  Copied

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Abstract

Objective To explore the implementation of conditional Logistic regression in Python software for case-crossover study.

Methods The relationship between exposure to air pol-lutant nitrogen dioxide and hospitalization due to pulmonary infection was studied as an example. The conditional Logistic regression model was constructed by using Python to compare the modeling process and statistical analysis results with common statistical software R and SAS.

Results The modeling logic of Python, R and SAS is similar. Compared with the statistical softwares, the modeling language of Python is a little more complicated, and it is also slightly different from SAS in parameter test methods, but the parameter estimation results of the three softwares are identical.

Conclusion Python software could realize conditional Logistic regression analysis, further expanding the applica-tion scenarios of Python in statistical analysis.

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References

1.张政, 詹思延. 病例交叉设计[J]. 中华流行病学杂志, 2001, 22(4): 70-72. [Zhang Z, Zhan SY. Case crossover design[J]. Chinese Journal of Epidemiology, 2001, 22(4): 70-72.] DOI: 10.3760/j.issn:0254-6450.2001.04.022.

2.郑一男, 曹佩华, 欧春泉. N: M条件logistic回归分析在统计软件上的实现[J]. 中国卫生统计, 2011, 28(1): 93-94, 97. [Zheng YN, Cao PH, Ou CQ. Implementation of N: M conditional logistic regression analysis in statis-tical software[J]. Chinese Journal of Health Statistics, 2011, 28(1): 93-94, 97.] DOI: 10.3969/j.issn.1002-3674. 2011.01.034.

3.孙振球, 王勇勇. 医学统计学, 第4版[M]. 北京: 人民卫生出版社, 2014.

4.张业武. Cox比例风险模型对条件logistic回归参数估计原理和方法[J].中国卫生统计, 2002, 19(1): 23-25. [Zhang YW. Principle and method of conditional Logistic regression parameter estimation by Cox proportional risk model[J]. Chinese Journal of Health Statistics, 2002, 19(1): 23-25.] DOI: 10.3969/j.issn.1002-3674.2002.01.008.

5.孙中华, 王梅. Cox模型处理条件Logistic回归考察升主动脉压力波谷峰值与冠心病的相关性[J]. 数理医药学杂志, 2004, 17(1): 80-82. [Sun ZH, Wang M. Logistic Regression study about the correlation between prissure wave trough peak of ascending aorta and coronary heart disease in cox model[J]. Journal of Mathematical Medicine, 2004, 17(1): 80-82.] DOI: 10.3969/j.issn.1004- 4337.2004.01.041.

6.焦奎壮, 马煦晰, 马小茜, 等. 广义估计方程与混合线性模型在Python中的实现[J]. 医学新知, 2022, 32(5): 333-338. [Jiao KZ, Ma XX, Ma XQ, et al. Implementation of generalized estimating equation and mixed linear models in Python[J]. New Medicine, 2022, 32(5): 333-338.] DOI: 10.12173/j.issn.1004-5511.202203007.

7.平凯珂, 陈平雁. Python与R语言联合应用的实现[J].中国卫生统计, 2017, 34(2): 358-360. [Ping KK, Chen PY. Implementation of python and R language combined application[J]. Chinese Journal of Health Statistics, 2017, 34(2): 358-360.] DOI: CNKI:SUN:ZGWT.0.2017-02-054.

8.李天辉. 基于python的数据分析可视化研究与实现[J]. 电子测试, 2020, (20): 78-79. [Li TH. Research and im-plementation of visualized data analysis based on python[J]. Electronic Test, 2020, (20): 78-79.] DOI: 10.3969/j.issn.1000-8519.2020.20.030.