Literature DB >> 28583378

A systematic survey on reporting and methods for handling missing participant data for continuous outcomes in randomized controlled trials.

Yuqing Zhang1, Ivan D Flórez2, Luis E Colunga Lozano3, Fazila Abu Bakar Aloweni4, Sean Alexander Kennedy5, Aihua Li6, Samantha Craigie7, Shiyuan Zhang8, Arnav Agarwal9, Luciane C Lopes10, Tahira Devji6, Wojtek Wiercioch6, John J Riva11, Mengxiao Wang6, Xuejing Jin6, Yutong Fei12, Paul Alexander6, Gian Paolo Morgano6, Yuan Zhang6, Alonso Carrasco-Labra13, Lara A Kahale14, Elie A Akl15, Holger J Schünemann16, Lehana Thabane6, Gordon H Guyatt17.   

Abstract

OBJECTIVE: To assess analytic approaches randomized controlled trial (RCT) authors use to address missing participant data (MPD) for patient-important continuous outcomes. STUDY DESIGN AND
SETTING: We conducted a systematic survey of RCTs published in 2014 in the core clinical journals that reported at least one patient-important outcome analyzed as a continuous variable.
RESULTS: Among 200 studies, 187 (93.5%) trials explicitly reported whether MPD occurred. In the 163 (81.5%) trials that reported the occurrence of MPD, the median and interquartile ranges of the percentage of participants with MPD were 11.4% (2.5%-22.6%).Among the 147 trials in which authors made clear their analytical approach to MPD, the approaches chosen included available data only (109, 67%); mixed-effect models (10, 6.1%); multiple imputation (9, 4.5%); and last observation carried forward (9, 4.5). Of the 163 studies reporting MPD, 16 (9.8%) conducted sensitivity analyses examining the impact of the MPD and (18, 11.1%) discussed the risk of bias associated with MPD.
CONCLUSION: RCTs reporting continuous outcomes typically have over 10% of participant data missing. Most RCTs failed to use optimal analytic methods, and very few conducted sensitivity analyses addressing the possible impact of MPD or commented on how MPD might influence risk of bias.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Analytic approaches; Continuous outcome; Lost to follow-up; MPD; Missing participant data; Randomized controlled trials

Mesh:

Year:  2017        PMID: 28583378     DOI: 10.1016/j.jclinepi.2017.05.017

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


  7 in total

1.  Clinical and Molecular Findings after Autologous Stem Cell Transplantation or Cyclophosphamide for Scleroderma: Handling Missing Longitudinal Data.

Authors:  Lynette Keyes-Elstein; Ashley Pinckney; Ellen Goldmuntz; Beverly Welch; Jennifer M Franks; Viktor Martyanov; Tammara A Wood; Leslie Crofford; Maureen Mayes; Peter McSweeney; Richard Nash; George Georges; M E Csuka; Robert Simms; Daniel Furst; Dinesh Khanna; E William St Clair; Michael L Whitfield; Keith M Sullivan
Journal:  Arthritis Care Res (Hoboken)       Date:  2021-09-17       Impact factor: 4.794

2.  Mapping the nomenclature, methodology, and reporting of studies that review methods: a pilot methodological review.

Authors:  Daeria O Lawson; Alvin Leenus; Lawrence Mbuagbaw
Journal:  Pilot Feasibility Stud       Date:  2020-01-30

3.  Alpha7 nAChR Agonists for Cognitive Deficit and Negative Symptoms in Schizophrenia: A Meta-analysis of Randomized Double-blind Controlled Trials.

Authors:  Ye Jin; Qi Wang; Yan Wang; Mengxi Liu; Anji Sun; Zhongli Geng; Yiwei Lin; Xiaobai Li
Journal:  Shanghai Arch Psychiatry       Date:  2017-08-25

4.  The proportion of missing data should not be used to guide decisions on multiple imputation.

Authors:  Paul Madley-Dowd; Rachael Hughes; Kate Tilling; Jon Heron
Journal:  J Clin Epidemiol       Date:  2019-03-13       Impact factor: 6.437

5.  Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.

Authors:  Jonathan Steif; Rollin Brant; Rama Syamala Sreepada; Nicholas West; Srinivas Murthy; Matthias Görges
Journal:  Pediatr Crit Care Med       Date:  2022-01-01       Impact factor: 3.971

6.  Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator.

Authors:  Audinga-Dea Hazewinkel; Jack Bowden; Kaitlin H Wade; Tom Palmer; Nicola J Wiles; Kate Tilling
Journal:  Stat Med       Date:  2022-01-31       Impact factor: 2.497

Review 7.  A tutorial on methodological studies: the what, when, how and why.

Authors:  Lawrence Mbuagbaw; Daeria O Lawson; Livia Puljak; David B Allison; Lehana Thabane
Journal:  BMC Med Res Methodol       Date:  2020-09-07       Impact factor: 4.615

  7 in total

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