Julian P. T. Higgins

355.5k citations
344 papers · 205.2k · 60 hit papers · h-index 112

Impact in

Papers in

Julian P. T. Higgins

332 papers receiving 201.7k citations

Julian P. T. Higgins's Hit Papers

Using Risk of Bias 2 to assess results from randomised controlled trials: guidance from Cochrane 2023 · 129 citations
1290+3+6Years since publication10002.0k3.0k

Peers

Julian P. T. Higgins
Comparison fields: 5 of 231
  • Statistics, Probability and Uncertainty 8.6k
  • Psychiatry and Mental health 9.2k
  • Periodontics 2.7k
  • Applied Psychology 2.8k
  • Clinical Psychology 10.3k
Replace Matthias Egger with:
Matthias Egger Switzerland
John P. A. Ioannidis United States
Peter C Gøtzsche Denmark
Jennifer Tetzlaff Canada
George Davey Smith United Kingdom
David Moher Canada
Alessandro Liberati Italy
Gordon Guyatt Canada
Douglas G. Altman United Kingdom
Kenneth F. Schulz United States
Julian P. T. Higgins relative to Matthias Egger Switzerland Matthias Egger's profile →
Citations per field
00.5×1.5×1.9×
Matthias Egger · 1×
Citations per year

Countries citing papers authored by Julian P. T. Higgins

Since Specialization
Citations

This map shows the geographic impact of Julian P. T. Higgins's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julian P. T. Higgins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julian P. T. Higgins more than expected).

Fields of papers citing papers by Julian P. T. Higgins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Julian P. T. Higgins. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julian P. T. Higgins. The network helps show where Julian P. T. Higgins may publish in the future.

Co-authors

The 25 scholars most cited alongside Julian P. T. Higgins, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Julian P. T. Higgins Line = papers co-authored together Julian P. T. Higgins links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 344 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Measuring inconsistency in meta-analyses
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200350652
2
Quantifying heterogeneity in a meta‐analysis
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200229757
3
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
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201127115
4
Introduction to Meta‐Analysis
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200910964
5
Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials
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20115764
6
A basic introduction to fixed-effect and random-effects models for meta-analysis
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20105188
7
Risk‐of‐bias VISualization (robvis): An R package and Shiny web app for visualizing risk‐of‐bias assessments
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20203560
8
Chapter 8: Assessing risk of bias in included studies
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20083419
9
How should meta‐regression analyses be undertaken and interpreted?
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20022406
10
Chapter 9: Analysing Data and Undertaking Meta-Analyses
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20082404
11
Comparative Efficacy and Acceptability of 21 Antidepressant Drugs for the Acute Treatment of Adults With Major Depressive Disorder: A Systematic Review and Network Meta-Analysis
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20182256
12
Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization
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20212161
13
Interpretation of random effects meta-analyses
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20112138
14
Introduction to Meta‐Analysis
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20212077
15
Graphical Tools for Network Meta-Analysis in STATA
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20131966
16
A Re-Evaluation of Random-Effects Meta-Analysis
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20081890
17
GRADE guidelines: 7. Rating the quality of evidence—inconsistency
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20111773
18
Meta-analyses involving cross-over trials: methodological issues
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20021702
19
Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies
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20121516
20
ROBIS: A new tool to assess risk of bias in systematic reviews was developed
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20151463

About Julian P. T. Higgins

Julian P. T. Higgins is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability, Economics and Econometrics, Genetics and Molecular Biology, having authored 344 papers that have together received 205.2k indexed citations. Recurring topics across this work include Meta-analysis and systematic reviews (141 papers), Statistical Methods in Clinical Trials (42 papers), Statistical Methods and Bayesian Inference (26 papers), Health Systems, Economic Evaluations, Quality of Life (24 papers), Genetic Associations and Epidemiology (17 papers), Economic and Environmental Valuation (12 papers), Reliability and Agreement in Measurement (9 papers) and Advanced Causal Inference Techniques (9 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (8.6k citations), Psychiatry and Mental health (9.2k citations), Periodontics (2.7k citations), Applied Psychology (2.8k citations) and Clinical Psychology (10.3k citations). Julian P. T. Higgins has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Simon G. Thompson, Douglas G. Altman, Jonathan J Deeks, Simon G. Thompson, Michael Borenstein, Larry V. Hedges, Hannah R. Rothstein, Jonathan A C Sterne, Jelena Savović and David Moher. Their work appears in journals such as Research Synthesis Methods, Statistics in Medicine, Cochrane Database of Systematic Reviews, Systematic Reviews and International Journal of Epidemiology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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