RE: Time-dependent Bias Existing in Their Statistical Analysis Must Be Properly Controlled/Avoided Before Drawing Any Conclusions
- PMID: 36808848
- DOI: 10.1097/TP.0000000000004470
RE: Time-dependent Bias Existing in Their Statistical Analysis Must Be Properly Controlled/Avoided Before Drawing Any Conclusions
Conflict of interest statement
The authors have no conflicts of interest to report.
Comment in
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RE: The Efficacy and Safety of SGLT2 Inhibitor in Kidney Transplant Recipients With Diabetes.Transplantation. 2023 Mar 1;107(3):e80-e81. doi: 10.1097/TP.0000000000004471. Epub 2023 Feb 21. Transplantation. 2023. PMID: 36808849 No abstract available.
Comment on
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The Efficacy and Safety of SGLT2 Inhibitor in Diabetic Kidney Transplant Recipients.Transplantation. 2022 Sep 1;106(9):e404-e412. doi: 10.1097/TP.0000000000004228. Epub 2022 Jun 30. Transplantation. 2022. PMID: 35768908
References
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- Lim JH, Kwon S, Jeon Y, et al. The efficacy and safety of SGLT2 inhibitor in diabetic kidney transplant recipients. Transplantation. 2022;106:e404–e412.
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- Beyersmann J, Gastmeier P, Wolkewitz M, et al. An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation. J Clin Epid. 2008;61:1261–1221.
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- Suissa S. Immortal time bias in pharmacoepidemiology. Am J Epid. 2008;167:492–499.
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- Shariff SZ, Cuerden MS, Jain AK, et al. The secret of immortal time bias in epidemiologic studies. J Am Soc Nephrol. 2008;19:841–843.
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- McGuire DK, Shih WJ, Cosentino F, et al. Association of SGLT2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes – a meta-analysis. JAMA Cardiol. 2021;6:148–158.
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