The effect of mis-adjusting instrumental variables on the estimation of causal effect in Logistic regression analysis model

SU Ping; WANG Ting-ting; YU Yuan-yuan; SUN Xiao-ru; LI Hong-kai; XUE Fu-zhong

doi:10.16462/j.cnki.zhjbkz.2021.06.007

Volume 25 Issue 6

Jul. 2021

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SU Ping, WANG Ting-ting, YU Yuan-yuan, SUN Xiao-ru, LI Hong-kai, XUE Fu-zhong. The effect of mis-adjusting instrumental variables on the estimation of causal effect in Logistic regression analysis model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 656-662. doi: 10.16462/j.cnki.zhjbkz.2021.06.007

Citation:

SU Ping, WANG Ting-ting, YU Yuan-yuan, SUN Xiao-ru, LI Hong-kai, XUE Fu-zhong. The effect of mis-adjusting instrumental variables on the estimation of causal effect in Logistic regression analysis model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 656-662. doi: 10.16462/j.cnki.zhjbkz.2021.06.007

Citation:

SU Ping, WANG Ting-ting, YU Yuan-yuan, SUN Xiao-ru, LI Hong-kai, XUE Fu-zhong. The effect of mis-adjusting instrumental variables on the estimation of causal effect in Logistic regression analysis model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(6): 656-662. doi: 10.16462/j.cnki.zhjbkz.2021.06.007

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The effect of mis-adjusting instrumental variables on the estimation of causal effect in Logistic regression analysis model

doi: 10.16462/j.cnki.zhjbkz.2021.06.007

1.
Institute of Medical Sciences, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, China
2.
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Institute of Health and Medical Research, Shandong University, Jinan 250012, China

Funds:

National Key Research and Development Program 2020YFC2003500

National Natural Science Foundation of China 81773547

National Natural Science Foundation of China 82003557

National Natural Science Foundation of Shandong Province ZR2019ZD02

National Natural Science Foundation of Shandong Province ZR2019PH041

More Information

Corresponding author: LI Hong-kai, E-mail: lihongkaiyouxiang@163.com; XUE Fu-zhong, E-mail: xuefzh@sdu.edu.cn
Received Date: 2021-04-26
Rev Recd Date: 2021-05-18
Publish Date: 2021-06-10

Abstract

Abstract

Objective To explore the effects of adjusting for instrumental variables (Ⅳs) in a Logistic regression model through statistical simulation and real data analysis while there were unmeasured confounding factors. Methods Simulations were carried out by traversing the value of parameters in the Logistic regression model, and variables were all binomial distribution. Bias and standard error were used to evaluate the performance of estimators. As for the real data analysis, a longitudinal hypertension cohort was constructed based on the multi-center health management cohort of Shandong Province, and single nucleotide polymorphism (SNP) rs12149832 was selected as the Ⅳ. Logistic regression models with and without adjusting Ⅳ(rs12149832) were used to estimate the effect of body mass index (BMI) on hypertension. Results The statistical simulation results showed that adjusting for Ⅳs in a Logistic regression model would increase the confounding bias and the standard error of effect estimation, but these increases were generally small. As for the real data analysis, a total of 1 240 women were included in the Hypertension cohort. The baseline age was (37.7±10.5) years and the BMI was (22.1±3.1) kg/m². The estimated value with adjusting for Ⅳ (0.225, P < 0.001) was slightly less than the estimated value without adjusting for the Ⅳ (0.228, P < 0.001), which basically verified the statistical simulation results about adjusting Ⅳs. Conclusion In observational epidemiological studies, the mistaken inclusion of Ⅳs in the Logistic regression model has an impact on both the bias and standard error of the effect estimates.
- Instrumental variable,
- Causal inference,
- Confounding factor,
- Logistic regression model

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References(30)

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