Potentially missing data are considerably more frequent than definitely missing data: a methodological survey of 638 randomized controlled trials.

[1]  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.

[2]  Eric J Tchetgen Tchetgen,et al.  Multiple Imputation for Incomplete Data in Epidemiologic Studies , 2018, American journal of epidemiology.

[3]  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.

[4]  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.

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

[6]  Fiona Godlee,et al.  Data Sharing Statements for Clinical Trials: A Requirement of the International Committee of Medical Journal Editors , 2017, Ethiopian journal of health sciences.

[7]  David C. Currow,et al.  Quality of missing data reporting and handling in palliative care trials demonstrates that further development of the CONSORT statement is required: a systematic review , 2017, Journal of clinical epidemiology.

[8]  Lara A. Kahale,et al.  Reporting and methods for handling missing participant data for continuous outcomes in randomized controlled trials : a systematic survey , 2017 .

[9]  C. Ramsay,et al.  Reporting and dealing with missing quality of life data in RCTs: has the picture changed in the last decade? , 2016, Quality of Life Research.

[10]  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.

[11]  Kevin A. Hallgren,et al.  Missing Data in Alcohol Clinical Trials with Binary Outcomes. , 2016, Alcoholism, clinical and experimental research.

[12]  B. Tom,et al.  A systematic review of randomised controlled trials in rheumatoid arthritis: the reporting and handling of missing data in composite outcomes. , 2016, Trials.

[13]  Miriam J. Johnson,et al.  Missing data in randomised controlled trials evaluating palliative interventions: a systematic review and meta-analysis , 2016, The Lancet.

[14]  Eyal Oren,et al.  Statistical analysis and handling of missing data in cluster randomized trials: a systematic review , 2016, Trials.

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

[16]  Oliver Rivero-Arias,et al.  The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review of the current literature , 2016, Quality of Life Research.

[17]  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.

[18]  Non-compliance with randomised allocation and missing outcome data in randomised controlled trials evaluating surgical interventions: a systematic review , 2015, BMC Research Notes.

[19]  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.

[20]  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.

[21]  Katya L. Masconi,et al.  Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review , 2015, EPMA Journal.

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

[23]  Lara A. Kahale,et al.  Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study , 2014, Systematic Reviews.

[24]  Jørn Wetterslev,et al.  Comparison of Results from Different Imputation Techniques for Missing Data from an Anti-Obesity Drug Trial , 2014, PloS one.

[25]  Melanie L Bell,et al.  Handling missing data in RCTs; a review of the top medical journals , 2014, BMC Medical Research Methodology.

[26]  Karel G M Moons,et al.  Randomized trials with missing outcome data: how to analyze and what to report , 2014, Canadian Medical Association Journal.

[27]  Melanie L Bell,et al.  Practical and statistical issues in missing data for longitudinal patient-reported outcomes , 2014, Statistical methods in medical research.

[28]  J. Farrar,et al.  Reporting of missing data and methods used to accommodate them in recent analgesic clinical trials: ACTTION systematic review and recommendations , 2014, PAIN®.

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

[30]  Paula Williamson,et al.  A review of the handling of missing longitudinal outcome data in clinical trials , 2014, Trials.

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

[32]  M. Kenward,et al.  Differential dropout and bias in randomised controlled trials: when it matters and when it may not , 2013, BMJ.

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

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

[35]  Joseph G Ibrahim,et al.  Missing data in clinical studies: issues and methods. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  I. White,et al.  Including all individuals is not enough: Lessons for intention-to-treat analysis , 2012, Clinical trials.

[37]  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.

[38]  Ian R White,et al.  Strategy for intention to treat analysis in randomised trials with missing outcome data , 2011, BMJ : British Medical Journal.

[39]  P. Fayers,et al.  Predicting missing quality of life data that were later recovered: an empirical comparison of approaches , 2010, Clinical trials.

[40]  H. Hemilä.  Citation bias in the CONSORT comments on blinding , 2010 .

[41]  M. Rapoff,et al.  Attrition in randomized controlled trials for pediatric chronic conditions. , 2009, Journal of pediatric psychology.

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

[43]  P. Fayers,et al.  Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches , 2009, Health and quality of life outcomes.

[44]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[45]  C. Ramsay,et al.  A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes , 2008, Trials.

[46]  Craig H Mallinckrodt,et al.  The impact of missing data and how it is handled on the rate of false‐positive results in drug development , 2008, Pharmaceutical statistics.

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

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

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

[50]  H. Heinzl,et al.  Statistical problems caused by missing data resulting from neodymium:YAG laser capsulotomies in long‐term posterior capsule opacification studies: Problem identification and possible solutions , 2008, Journal of cataract and refractive surgery.

[51]  J Zhang,et al.  Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study , 2006, Pharmaceutical statistics.

[52]  Ian R White,et al.  Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals , 2004, Clinical trials.

[53]  S. Leucht,et al.  Dropout rates in randomised antipsychotic drug trials , 2001, Psychopharmacology.