J
James R. Carpenter
Researcher at University of London
Publications - 268
Citations - 33690
James R. Carpenter is an academic researcher from University of London. The author has contributed to research in topics: Missing data & Imputation (statistics). The author has an hindex of 58, co-authored 245 publications receiving 24983 citations. Previous affiliations of James R. Carpenter include University College London & University Medical Center Freiburg.
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Journal ArticleDOI
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions.
Jonathan A C Sterne,Miguel A. Hernán,Barnaby C Reeves,Jelena Savović,Jelena Savović,Nancy D. Berkman,Meera Viswanathan,David Henry,Douglas G. Altman,Mohammed T. Ansari,Isabelle Boutron,James R. Carpenter,An-Wen Chan,Rachel Churchill,Jonathan J Deeks,Asbjørn Hróbjartsson,Jamie J Kirkham,Peter Jüni,Yoon K. Loke,Theresa D Pigott,Craig R Ramsay,Deborah L. Regidor,Hannah R. Rothstein,Lakhbir Sandhu,Pasqualina Santaguida,Holger J. Schünemann,Beverly Shea,Ian Shrier,Peter Tugwell,Lucy Turner,Jeffrey C. Valentine,Hugh Waddington,Elizabeth Waters,George A. Wells,Penny Whiting,Julian P T Higgins +35 more
TL;DR: Risk of Bias In Non-randomised Studies - of Interventions is developed, a new tool for evaluating risk of bias in estimates of the comparative effectiveness of interventions from studies that did not use randomisation to allocate units or clusters of individuals to comparison groups.
Journal ArticleDOI
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
Jonathan A C Sterne,Ian R. White,John B. Carlin,Michael Spratt,Patrick Royston,Michael G. Kenward,Angela M. Wood,James R. Carpenter +7 more
TL;DR: The appropriate use and reporting of the multiple imputation approach to dealing with missing data is described by Jonathan Sterne and colleagues.
Journal ArticleDOI
Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials
Jonathan A C Sterne,Alex J. Sutton,John P. A. Ioannidis,Norma Terrin,David R. Jones,Joseph Lau,James R. Carpenter,Gerta Rücker,Roger M. Harbord,Christopher H. Schmid,Jennifer Tetzlaff,Jonathan J Deeks,Jaime Peters,Petra Macaskill,Guido Schwarzer,Sue Duval,Douglas G. Altman,David Moher,Julian P T Higgins +18 more
TL;DR: How to interpret funnel plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis model are described.
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Bootstrap confidence intervals : when, which, what? A practical guide for medical statisticians
TL;DR: This article reviews the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses.
Journal ArticleDOI
Undue reliance on I(2) in assessing heterogeneity may mislead.
TL;DR: As precision increases, while estimates of the heterogeneity variance τ2 remain unchanged on average, estimates of I2 increase rapidly to nearly 100%.