ZHANG Chao 1 , GAO Zhengyan 2 , HUANG Jingyu 3 , LI Sheng 4 , NIU Yuming 1 , XU Chang 5 , HAN Fangfang 6
  • 1. Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, P.R.China;
  • 2. Department of Urology, The Six People’s Hospital of Yancheng, Yancheng, 224000, Hubei, P.R.China;
  • 3. Department of Thoracic Surgery, Hubei Cancer Clinical Study Center and Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, P.R.China;
  • 4. Center for Evidence-Based and Translation Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, P.R.China;
  • 5. Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
  • 6. Department of Ophthalmology, Yuyao No. 2 People's Hospital, Yuyao, 315470, Zhejiang, P.R.China;
HAN Fangfang, Email: hanff88@126.com
Dose-response meta-analysis; Two-stage approach; Random-effect model; Stata software
10.7507/1672-2531.201703042
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Dose-response meta-analysis is being increasingly applied in evidence production and clinical decision. The research method, synthesizing certain dose-specific effects across studies with the same target question by a certain types of weighting schedule to get a mean dose-response effect, is to reflect the dose-response relationship between certain exposure and outcome. Currently, the most popular method for dose-response meta-analysis is based on the classical "two-stage approach", with the advantage that it allows fixed- or random-effect model, according to the amount of heterogeneity in the model. There are two types of random-effect model available for dose-response meta-analysis, that is, the generally model and the coefficient-correlation-adjusted model. In this article, we briefly introduce two models and illustrate how they are applied in Stata software, which is expected to provide theoretical foundation for evidence-based practice.

Citation： ZHANG Chao, GAO Zhengyan, HUANG Jingyu, LI Sheng, NIU Yuming, XU Chang, HAN Fangfang. The application of two random-effect models for dose-response meta-analysis. Chinese Journal of Evidence-Based Medicine, 2017, 17(5): 616-620. doi: 10.7507/1672-2531.201703042 Copy

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