Comment on “Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy”

Pharmacotherapy: The Journal of Human Pharmacology and Drug TherapyVolume 43, Issue 4 p. 349-350 LETTER TO THE EDITOR Comment on “Tacrolimus in the treatment of childhood nephrotic syndrome: Machine learning detects novel biomarkers and predicts efficacy” William L. Baker, Corresponding Author William L. Baker [email protected] orcid.org/0000-0003-2172-0931 Department of Pharmacy Practice, University of Connecticut School of Pharmacy, Storrs, Connecticut, USA Correspondence William L. Baker, Department of Pharmacy Practice, University of Connecticut School of Pharmacy, 69 N Eagleville Rd, Storrs, CT 06269, USA. Email: [email protected]Search for more papers by this author William L. Baker, Corresponding Author William L. Baker [email protected] orcid.org/0000-0003-2172-0931 Department of Pharmacy Practice, University of Connecticut School of Pharmacy, Storrs, Connecticut, USA Correspondence William L. Baker, Department of Pharmacy Practice, University of Connecticut School of Pharmacy, 69 N Eagleville Rd, Storrs, CT 06269, USA. Email: [email protected]Search for more papers by this author First published: 19 April 2023 https://doi.org/10.1002/phar.2785Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1Mo X, Chen X, Zeng H, et al. Tacrolimus in the treatment of childhood nephrotic syndrome: machine learning detects novel biomarkers and predicts efficacy. Pharmacotherapy. 2022; 43: 43-52. doi:10.1002/phar.2749 2Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015; 162: 55-63. 3Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015; 162: W1-W73. 4Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021; 11:e048008. 5Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020; 368:m441. 6Riley RD, Debray TPA, Collins GS, et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med. 2021; 40: 4230-4251. 7Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009; 338:b2393. 8Groenwold RH, White IR, Donders AT, et al. Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. CMAJ. 2012; 184: 1265-1269. 9Lee KJ, Tilling KM, Cornish RP, et al. Framework for the treatment and reporting of missing data in observational studies: the treatment and reporting of missing data in observational studies (STRATOS) framework. J Clin Epidemiol. 2021; 134: 79-88. 10Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol. 1996; 49: 907-916. 11Heinze G, Wallisch C, Dunkler D. Variable selection – a review and recommendations for the practicing statistician. Biom J. 2018; 60: 431-449. 12Van Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019; 17: 230. 13Andaur Navarro CL, Damen JA, et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol. 2022; 22: 12. Volume43, Issue4April 2023Pages 349-350 ReferencesRelatedInformation