Literature DB >> 32531265

How personalized are benefit and harm results of randomized trials? A systematic review.

Alice Yu1, Yaanu Jeyakumar2, Mei Wang3, Justin Lee4, Maura Marcucci3, Anne Holbrook5.   

Abstract

OBJECTIVES: This study aimed to review the degree of personalization of benefit and harm in the reporting of recent high-profile randomized controlled trials (RCTs) involving pharmacological interventions. STUDY DESIGN AND
SETTING: This study is a systematic review of RCTs published between 2012 and 2017 with at least one intervention evaluating drug therapy and meeting the "high-profile" threshold in a premier academic literature abstraction service. Our primary outcome was the proportion of trials reporting subgroup analyses of a combined benefit-harm outcome. Secondary outcomes included the proportion of trials reporting subgroup analyses or clinical prediction guide for benefits or harms. We assessed the quality of the subgroup analyses using a modified version of previously published credibility criteria.
RESULTS: Of 296 eligible RCTs, nine studies (3%) reported a combined benefit-harm endpoint. We found subgroup analyses of a combined benefit-harm endpoint in three studies (1%), a benefit endpoint in 167 studies (56.4%), and a harm endpoint in 18 studies (6.1%). The overall quality of the subgroup analyses was poor. Only one study reported a clinical prediction guide for an outcome.
CONCLUSION: Despite great interest in the personalization of therapies, it is rarely reported in high-profile trials. Lack of rigorous and widely accepted methods may be the major barrier.
Copyright © 2020 Elsevier Inc. All rights reserved.

Keywords:  Benefits and harms; Drug therapy; Personalization; Prediction guides; Subgroup analyses; Systematic review

Year:  2020        PMID: 32531265     DOI: 10.1016/j.jclinepi.2020.05.029

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  3 in total

1.  Identification of TNIK as a novel potential drug target in thyroid cancer based on protein druggability prediction.

Authors:  Yi-Fei Yang; Bin Yu; Xiu-Xia Zhang; Yun-Hua Zhu
Journal:  Medicine (Baltimore)       Date:  2021-04-23       Impact factor: 1.817

2.  Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants.

Authors:  Andreas D Meid; Lucas Wirbka; Andreas Groll; Walter E Haefeli
Journal:  Med Decis Making       Date:  2021-12-15       Impact factor: 2.749

3.  Coordination of Oral Anticoagulant Care at Hospital Discharge (COACHeD): protocol for a pilot randomised controlled trial.

Authors:  Anne M Holbrook; Kristina Vidug; Lindsay Yoo; Sue Troyan; Sam Schulman; James Douketis; Lehana Thabane; Stephen Giilck; Yousery Koubaesh; Sylvia Hyland; Karim Keshavjee; Joanne Ho; Jean-Eric Tarride; Amna Ahmed; Marianne Talman; Blair Leonard; Khursheed Ahmed; Mohammad Refaei; Deborah M Siegal
Journal:  Pilot Feasibility Stud       Date:  2022-08-02
  3 in total

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