Adjusting for covariates in clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

HUANG Lihong ¹ ,  CHEN Feng ²

1. Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai, 200032, P.R.China;
2. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, P.R.China;

Corresponding author：

CHEN Feng, Email: fengchen@njmu.edu.cn

Keywords：

Covariate; Analysis of covariance; Generalized linear model

DOI：

10.7507/1672-2531.201905066

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Covariates are factors which have significant impacts on the primary analysis prior to the subjects being treated. Covariates adjustment should be considered in the design and analysis stages of the clinical trial. Through controlling in the design stage is the optimal resolution; randomization, stratified randomization and restricted covariant values could be used to balance the covariates between comparative treatments. During data analysis stage, analysis of covariance, stratified analysis, linear or generalized linear model can be conducted for covariate adjustment according to different types of outcome and covariate. For confirmatory clinical trial, covariates should be defined in advance in the protocol and statistical analysis plan with the main statistical model.

Citation： HUANG Lihong, CHEN Feng. Adjusting for covariates in clinical trials. Chinese Journal of Evidence-Based Medicine, 2019, 19(12): 1498-1502. doi: 10.7507/1672-2531.201905066 Copy

1.	FDA. Adjusting for covariates in randomized clinical trials for drugs and biologics with continuous outcomes. Washington DC: FDA, 2019.
2.	ICH. E9: statistical principles for clinical trials. Available at: https://wenku.baidu.com/view/9785232a647d27284b735117.html.
3.	EMA. Guideline on adjustment for baseline covariates in clinical trials. London: EMA, 2015.
4.	陈峰, 夏结来, 主编. 临床试验统计学. 北京: 人民卫生出版社, 2018: 202-210.
5.	Altman DG, Doré CJ. Randomisation and baseline comparisons in clinical trials. Lancet, 1990, 335(8682): 149-153.
6.	Begg CB. Significance tests of covariate imbalance in clinical trials. Control Clin Trials, 1990, 11(4): 223-225.
7.	Ciolino JD, Martin RH, Zhao W, et al. Measuring continuous baseline covariate imbalances in clinical trial data. Stat Methods Med Res, 2015, 24(2): 255-272.
8.	Törnqvist L, Vartia P, Vartia Y. How should relative changes be measured? Am Stat, 1985, 39(1): 43-46.
9.	Burgess DC, Gebski VJ, Keech AC. Baseline data in clinical trials. Med J Aust, 2003, 179(2): 105-107.
10.	Pocock SJ, Assmann SE, Enos LE, et al. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med, 2002, 21(19): 2917-2930.
11.	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
12.	赵厚宇, 詹思延. 疾病风险评分在药物流行病学研究中的应用. 中华流行病学杂志, 2017, 38(2): 261-266.
13.	Chen Y, Briesacher BA. Use of instrumental variable in prescription drug research with observational data: a systematic review. J Clin Epidemiol, 2011, 64(6): 687-700.
14.	沈敏学, 胡明, 曾娜, 等. 双重差分模型在医学研究中的应用. 中国卫生统计, 2015, 32(3): 528-531.
15.	Yu M, Xie D, Wang X, et al. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies. Pharmacoepidemiol Drug Saf, 2012, 21(Suppl 2): 60-68.

1. FDA. Adjusting for covariates in randomized clinical trials for drugs and biologics with continuous outcomes. Washington DC: FDA, 2019.
2. ICH. E9: statistical principles for clinical trials. Available at: https://wenku.baidu.com/view/9785232a647d27284b735117.html.
3. EMA. Guideline on adjustment for baseline covariates in clinical trials. London: EMA, 2015.
4. 陈峰, 夏结来, 主编. 临床试验统计学. 北京: 人民卫生出版社, 2018: 202-210.
5. Altman DG, Doré CJ. Randomisation and baseline comparisons in clinical trials. Lancet, 1990, 335(8682): 149-153.
6. Begg CB. Significance tests of covariate imbalance in clinical trials. Control Clin Trials, 1990, 11(4): 223-225.
7. Ciolino JD, Martin RH, Zhao W, et al. Measuring continuous baseline covariate imbalances in clinical trial data. Stat Methods Med Res, 2015, 24(2): 255-272.
8. Törnqvist L, Vartia P, Vartia Y. How should relative changes be measured? Am Stat, 1985, 39(1): 43-46.
9. Burgess DC, Gebski VJ, Keech AC. Baseline data in clinical trials. Med J Aust, 2003, 179(2): 105-107.
10. Pocock SJ, Assmann SE, Enos LE, et al. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med, 2002, 21(19): 2917-2930.
11. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
12. 赵厚宇, 詹思延. 疾病风险评分在药物流行病学研究中的应用. 中华流行病学杂志, 2017, 38(2): 261-266.
13. Chen Y, Briesacher BA. Use of instrumental variable in prescription drug research with observational data: a systematic review. J Clin Epidemiol, 2011, 64(6): 687-700.
14. 沈敏学, 胡明, 曾娜, 等. 双重差分模型在医学研究中的应用. 中国卫生统计, 2015, 32(3): 528-531.
15. Yu M, Xie D, Wang X, et al. Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies. Pharmacoepidemiol Drug Saf, 2012, 21(Suppl 2): 60-68.

Previous Article
The comparison of research and reporting guideline for core outcome sets and the significance for research in traditional Chinese medicine

Chinese Journal of Evidence-Based Medicine

Adjusting for covariates in clinical trials

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Format

Content