Sensitivity analysis method for unmeasured confounding interference in observational study
10.3760/cma.j.issn.0254-6450.2019.11.023
- VernacularTitle: 观察性研究中针对未测量混杂干扰的敏感性分析方法
- Author:
Danhua WANG
1
;
Dongfang YOU
1
,
2
;
Lihong HUANG
3
;
Yang ZHAO
1
,
4
,
5
,
6
Author Information
1. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
2. Key Laboratory of Modern Toxicology, Ministry of Education, Nanjing Medical University, Nanjing 211166, China
3. Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
4. Jiangsu Provincial Key Laboratory of Malignant Tumor Biomarkers and Prevention, Nanjing 211166, China
5. Collaborative Innovation Center for Individual Medicine in Cancer, Nanjing 211166, China
6. Key Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing 211166, China
- Publication Type:Journal Article
- Keywords:
Observational study;
Causal inference;
Unmeasured confounding factor;
Sensitivity analysis
- From:
Chinese Journal of Epidemiology
2019;40(11):1470-1475
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To introduce the methods for sensitivity analysis, discuss and compare the advantages and disadvantages of different methods.
Methods:The difference between confounding function method and bounding factor method in accuracy of identifying unmeasured confounding factors in observational studies through simulation trials and actual clinical data was compared.
Results:The results of simulation trials and actual clinical data showed that when there was unmeasured confounding between exposure (X) and outcome (Y), the results of confounding function and the bounding factor analysis were similar in terms of the effect of unmeasured confounding factor to lead to the complete change of the magnitude and direction of the observed effect value. However, the confounding function method needed smaller confounding effect to fully interpret the observed effect value than the bounding factor needed. In addition, the bounding factor method needed to analyze two confounding parameters, while only one parameter was needed in the confounding function method. The confounding function method was simpler and more sensitive than the bounding factor method.
Conclusion:For real-world observational data, the sensitivity analysis process is essential in analyzing the causal effects between exposure (X) and outcome (Y). In terms of the calculation process and result interpretation the sensitivity analysis method of confounding function is worth to recommend.
