A guidance was developed to identify participants with missing outcome data in randomized controlled trials.

BACKGROUND In order for authors of systematic reviews to address missing data in randomized controlled trials (RCTs), they need to first identify the number of trial participants with missing data. OBJECTIVE To provide guidance for authors of systematic reviews on how to identify participants with missing outcome data in reports of RCTs. METHODS Guidance statements were informed by a review of studies addressing the topic of missing data and an iterative process of feedback and refinement, through meetings involving experts in health research methodology and authors of systematic reviews. RESULTS The proposed guidance includes: (1) definitions of key terms, (2) 19 categories of participants described in RCT reports and who might have missing data, and (3) a flowchart on how to judge the outcome data missingness for each category. The judgment of missingness relies on how trial authors report on the categories and handle them in their analyses. Practically, for their primary analysis, systematic reviewer authors should choose how to identify participants with missing outcome data (i.e., use either 'definitely missing data' or 'total possible missing data'), then select a method for handling missing data in meta-analysis. Sensitivity analyses should be undertaken to explore consistency with competing options for classifying patients as having missing data. CONCLUSION Adopting the proposed guidance will help promote transparency and consistency regarding how missing data is managed in systematic reviews.

[1]  Lara A. Kahale,et al.  Potentially missing data are considerably more frequent than definitely missing data: a methodological survey of 638 randomized controlled trials. , 2019, Journal of clinical epidemiology.

[2]  J. Sterne,et al.  Assessing risk of bias in a randomized trial , 2019, Cochrane Handbook for Systematic Reviews of Interventions.

[3]  Gordon H Guyatt,et al.  Addressing continuous data measured with different instruments for participants excluded from trial analysis: a guide for systematic reviewers. , 2014, Journal of clinical epidemiology.

[4]  R. Little,et al.  The prevention and treatment of missing data in clinical trials. , 2012, The New England journal of medicine.

[5]  Carmine Zoccali,et al.  When do we need competing risks methods for survival analysis in nephrology? , 2013, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[6]  A. E. Ades,et al.  A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta‐analysis , 2015, Statistics in medicine.

[7]  Lara A. Kahale,et al.  GRADE guidelines 17: assessing the risk of bias associated with missing participant outcome data in a body of evidence. , 2017, Journal of clinical epidemiology.

[8]  Lara A. Kahale,et al.  Systematic reviews do not adequately report or address missing outcome data in their analyses: a methodological survey. , 2018, Journal of clinical epidemiology.

[9]  Fiona Godlee,et al.  Data Sharing Statements for Clinical Trials: A Requirement of the International Committee of Medical Journal Editors , 2017, Annals of Internal Medicine.

[10]  I. White,et al.  A general method for handling missing binary outcome data in randomized controlled trials , 2014, Addiction.

[11]  Julian Pt Higgins,et al.  Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis , 2013, Clinical trials.

[12]  Robert West,et al.  Outcome criteria in smoking cessation trials: proposal for a common standard. , 2005, Addiction.

[13]  Lara A Kahale,et al.  Three challenges described for identifying participants with missing data in trials reports, and potential solutions suggested to systematic reviewers. , 2016, Journal of clinical epidemiology.

[14]  C. Feyerabend,et al.  Transdermal nicotine patches with low-intensity support to aid smoking cessation in outpatients in a general hospital. A placebo-controlled trial. , 1993, Archives of family medicine.

[15]  Dimitris Mavridis,et al.  Addressing missing outcome data in meta-analysis , 2014, Evidence-Based Mental Health.

[16]  Lara A Kahale,et al.  Reporting missing participant data in randomised trials: systematic survey of the methodological literature and a proposed guide , 2015, BMJ Open.

[17]  R. Little,et al.  The design and conduct of clinical trials to limit missing data , 2012, Statistics in medicine.

[18]  Lori A. Post,et al.  Strategies for Dealing with Missing Data in Clinical Trials: From Design to Analysis , 2013, The Yale journal of biology and medicine.

[19]  David Moher,et al.  Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine , 2018, British Medical Journal.

[20]  Matthias Briel,et al.  Addressing Dichotomous Data for Participants Excluded from Trial Analysis: A Guide for Systematic Reviewers , 2013, PloS one.

[21]  Douglas G Altman,et al.  Missing outcomes in randomized trials: addressing the dilemma , 2009, Open medicine : a peer-reviewed, independent, open-access journal.

[22]  Ian R White,et al.  Allowing for uncertainty due to missing data in meta‐analysis—Part 1: Two‐stage methods , 2008, Statistics in medicine.

[23]  G. Guyatt,et al.  Reporting, handling and assessing the risk of bias associated with missing participant data in systematic reviews: a methodological survey , 2022 .

[24]  L. Spineli.  Missing binary data extraction challenges from Cochrane reviews in mental health and Campbell reviews with implications for empirical research , 2017, Research synthesis methods.

[25]  D. Moher,et al.  CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials , 2010, BMJ : British Medical Journal.

[26]  Lara A. Kahale,et al.  A systematic survey on reporting and methods for handling missing participant data for continuous outcomes in randomized controlled trials. , 2017, Journal of clinical epidemiology.

[27]  Dimitris Mavridis,et al.  Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta‐analysis , 2015, Statistics in medicine.

[28]  G. Guyatt,et al.  Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis , 2002, BMJ : British Medical Journal.

[29]  G. Rait,et al.  Strategies to improve retention in randomised trials , 2013, The Cochrane database of systematic reviews.

[30]  Lara A Kahale,et al.  A systematic survey of the methods literature on the reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials. , 2017, Journal of clinical epidemiology.

[31]  Xin Sun,et al.  Addressing continuous data for participants excluded from trial analysis: a guide for systematic reviewers. , 2013, Journal of clinical epidemiology.

[32]  Per Winkel,et al.  When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts , 2017, BMC Medical Research Methodology.

[33]  Nicky J Welton,et al.  Allowing for uncertainty due to missing data in meta‐analysis—Part 2: Hierarchical models , 2008, Statistics in medicine.

[34]  M. Kenward,et al.  Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls , 2009, BMJ : British Medical Journal.

[35]  V. Serebruany,et al.  Drug Discontinuation and Follow-up Rates in Oral Antithrombotic Trials. , 2016, JAMA internal medicine.

[36]  Dimitris Mavridis,et al.  Dealing with missing outcome data in meta‐analysis , 2019, Research synthesis methods.

[37]  Thomas Agoritsas,et al.  Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches , 2015, Systematic Reviews.

[38]  Gordon H Guyatt,et al.  Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review , 2012, BMJ : British Medical Journal.

[39]  R. West,et al.  Commentary on Smolkowski et al. (2010): why is it important to assume that non-responders in tobacco cessation trials have relapsed? , 2010, Addiction.

[40]  J. Carpenter,et al.  Missing data in clinical research: an integrated approach , 2017, The British journal of dermatology.

[41]  Ian R White,et al.  Imputation methods for missing outcome data in meta-analysis of clinical trials , 2008, Clinical trials.