Statistical methods for relative risk estimation and applications in case-cohort study.
10.3760/cma.j.cn112338-20210812-00638
- Author:
Jia Yi TUO
1
;
Jing Hao BI
1
;
Zhuo Ying LI
2
;
Qiu Ming SHEN
1
;
Yu Ting TAN
2
;
Hong Lan LI
2
;
Hui Yun YUAN
3
;
Yong Bing XIANG
4
Author Information
1. School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China State Key Laboratory of Oncogene and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, China.
2. State Key Laboratory of Oncogene and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, China.
3. Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
4. School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China State Key Laboratory of Oncogene and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, China Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
- Publication Type:Journal Article
- MeSH:
China/epidemiology*;
Cohort Studies;
Female;
Humans;
Proportional Hazards Models;
Risk;
Sample Size
- From:
Chinese Journal of Epidemiology
2022;43(3):392-396
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To systematically introduce the design of case-cohort study and the statistical methods of relative risk estimation and their application in the design. Methods: First, we introduced the basic principles of case-cohort study design. Secondly, Prentice's method, Self-Prentice method and Barlow method were described in the weighted Cox proportional hazard regression models in detail, finally, the data from the Shanghai Women's Health Study were used as an example to analyze the association between obesity and liver cancer incidence in the full cohort and case-cohort sample, and the results of parameters from each method were compared. Results: Significant association was observed between obesity and risk for liver cancer incidence in women in both the full cohort and the case-cohort sample. In the Cox proportional hazard regression model, the partial regression coefficients of the full cohort and the case-cohort sample fluctuated with the adjustment of confounding factors, but the hazard ratio estimates of them were close. There was a difference in the standard error of the partial regression coefficient between the full cohort and the case-cohort sample. The standard error of the partial regression coefficient of the case-cohort sample was larger than that of the full cohort, resulting in a wider 95% confidence interval of the relative risk. In the weighted Cox proportional hazard regression model, the standard error of the partial regression coefficient of Prentice's method was closer to the parameter estimates from full cohort than Self-Prentice method and Barlow method, and the 95% confidence interval of hazard ratio was closer to that of the full cohort. Conclusions: Case-cohort design could yield parameter results closer to the full cohort by collecting and analyzing data from sub-cohort members and patients with the disease, and reduce sample size and improve research efficiency. The results suggested that Prentice's method would be preferred in case-cohort design.
