World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
66
Citations
53935
World Ranking
346
National Ranking
20

Overview

James R. Carpenter is affiliated with University College London in the United Kingdom. Their research primarily spans the fields of mathematics and medicine, with significant contributions to subfields including statistics and probability, economics and econometrics, general health professions, public health, environmental and occupational health, and infectious diseases.

The scientist's work focuses on advanced causal inference techniques, statistical methods and Bayesian inference, statistical methods in clinical trials, health systems, economic evaluations, and quality of life, as well as meta-analysis and systematic reviews. Their research topics also cover broader areas related to statistical methods and inference and health, environment, and cognitive aging.

Among recent publications authored or co-authored by James R. Carpenter are:

  • Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework (2021, Journal of Clinical Epidemiology)
  • Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide (2020, Statistics in Medicine)
  • Missing data: A statistical framework for practice (2021, Biometrical Journal)
  • A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation (2022, Sociological Methods & Research)
  • Access to routinely collected health data for clinical trials - review of successful data requests to UK registries (2020, Trials)

Frequent co-authors include Suzie Cro, Tim P. Morris, Elizabeth Williamson, Rosie Cornish, and Kate Tilling. These collaborators reflect ongoing partnerships that have produced numerous studies.

James R. Carpenter has published extensively in venues such as Statistics in Medicine, bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), BMJ Open, and Trials, indicating active engagement with both clinical and methodological research communities.

Best Publications

  • ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions.

    Jonathan A. C. Sterne;Miguel A Hernan;Barnaby C Reeves;Jelena Savovic;Jelena Savovic

  • 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

  • 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

  • Bootstrap confidence intervals : when, which, what? A practical guide for medical statisticians

    James Carpenter;John Bithell

  • Undue reliance on I(2) in assessing heterogeneity may mislead.

    Gerta Rücker;Guido Schwarzer;James R Carpenter;James R Carpenter;Martin Schumacher

  • Strategy for intention to treat analysis in randomised trials with missing outcome data

    Ian R White;Nicholas J Horton;James Carpenter;Stuart J Pocock

  • Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

    Anoop D. Shah;Jonathan W. Bartlett;James Carpenter;Owen Nicholas

  • Multiple imputation and its application

    J Carpenter;M Kenward

  • Multiple imputation: current perspectives.

    Michael G Kenward;James Carpenter

  • Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis

    R. H. H. Groenwold;I. R. White;A. R. T. Donders;J. R. Carpenter

  • REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types

    James R. Carpenter;Harvey Goldstein;Michael G. Kenward

  • Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model:

    Jonathan W Bartlett;Shaun R Seaman;Ian R White;James R Carpenter

  • Strategies for Multiple Imputation in Longitudinal Studies

    Michael Spratt;James Carpenter;Jonathan A C Sterne;John B Carlin

  • Arcsine test for publication bias in meta-analyses with binary outcomes.

    Gerta Rücker;Gerta Rücker;Guido Schwarzer;James Carpenter;James Carpenter

  • Meta-Analysis with R

    Guido Schwarzer;Gerta Rücker;James R Carpenter

  • Including all individuals is not enough: lessons for intention-to-treat analysis

    Ian R White;James Carpenter;Nicholas J Horton

  • Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation

    James R. Carpenter;James H. Roger;Michael G. Kenward

  • Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.

    Katherine J Lee;Kate M Tilling;Rosie P Cornish;Roderick J A Little

  • Effects of training on quality of peer review: randomised controlled trial

    Sara Schroter;Nick Black;Stephen Evans;James Carpenter

  • Sensitivity analysis after multiple imputation under missing at random: a weighting approach.

    James R. Carpenter;Michael G. Kenward;Ian R. White

  • Multiple imputation of covariates by fully conditional specification: accommodating the substantive model

    Jonathan W. Bartlett;Shaun R. Seaman;Ian R. White;James R. Carpenter

Frequent Co-Authors

Michael G. Kenward
Michael G. Kenward London School of Hygiene & Tropical Medicine
Ian R. White
Ian R. White University College London
Elizabeth A. Williamson
Elizabeth A. Williamson London School of Hygiene & Tropical Medicine
Richard W Morris
Richard W Morris University of Bristol
Harvey Goldstein
Harvey Goldstein University of Bristol
Kate Tilling
Kate Tilling University of Bristol
Irwin Nazareth
Irwin Nazareth University College London
Stephen Evans
Stephen Evans University of London
Jonathan A C Sterne
Jonathan A C Sterne University of Bristol
Matthew R. Sydes
Matthew R. Sydes University College London

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