Joie Ensor
- Health Informatics top 0.5%
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- Meta-analysis and systematic reviews 31
- Internal Medicine top 2%
- Venous Thromboembolism Diagnosis and Management 6
- Statistics and Probability top 1%
- Statistical Methods in Clinical Trials 11
- Statistical Methods and Bayesian Inference 8
- Geriatrics and Gerontology top 2%
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- Health Systems, Economic Evaluations, Quality of Life 17
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- Machine Learning in Healthcare 7
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- Acute Myocardial Infarction Research 7
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- Sepsis Diagnosis and Treatment 5
- Co-authors
- Richard D RileyKym I E SnellGary S. CollinsKarel G.M. MoonsFrank E. HarrellThomas P. A. DebrayDanielle BurkeJohannes B. Reitsma
- Partner nations
- United KingdomNetherlandsUnited States
In The Last Decade
Joie Ensor
61 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 175
- Health Informatics 191
- Statistics, Probability and Uncertainty 465
- Internal Medicine 180
- Statistics and Probability 388
- Geriatrics and Gerontology 182
Countries citing papers authored by Joie Ensor
This map shows the geographic impact of Joie Ensor'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 Joie Ensor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joie Ensor more than expected).
Fields of papers citing papers by Joie Ensor
This network shows the impact of papers produced by Joie Ensor. 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 Joie Ensor. The network helps show where Joie Ensor may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Joie Ensor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 5 | |
| 3 | 2025 | 2 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 0 | |
| 6 | Evaluation of clinical prediction models (part 2): how to undertake an external validation studybreakdown → | 2024 | 102 |
| 7 | 2024 | 0 | |
| 8 | 2023 | 46 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 26 | |
| 11 | 2023 | 1 | |
| 12 | 2021 | 56 | |
| 13 | 2021 | 65 | |
| 14 | 2021 | 34 | |
| 15 | 2020 | 87 | |
| 16 | Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomesbreakdown → | 2018 | 581 |
| 17 | 2017 | 15 | |
| 18 | A guide to systematic review and meta-analysis of prediction model performancebreakdown → | 2017 | 402 |
| 19 | 2015 | 45 | |
| 20 | 2014 | 18 |
About Joie Ensor
Joie Ensor is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability and Internal Medicine, having authored 66 papers that have together received 5.1k indexed citations. Recurring topics across this work include Meta-analysis and systematic reviews (31 papers), Health Systems, Economic Evaluations, Quality of Life (17 papers), Statistical Methods in Clinical Trials (11 papers), Statistical Methods and Bayesian Inference (8 papers), Machine Learning in Healthcare (7 papers), Acute Myocardial Infarction Research (7 papers), Venous Thromboembolism Diagnosis and Management (6 papers) and Sepsis Diagnosis and Treatment (5 papers). The work is most often cited by research in Health Informatics (191 citations), Statistics, Probability and Uncertainty (465 citations) and Internal Medicine (180 citations). Joie Ensor has collaborated with scholars based in United Kingdom, Netherlands and United States. Frequent co-authors include Richard D Riley, Kym I E Snell, Gary S. Collins, Karel G.M. Moons, Frank E. Harrell, Thomas P. A. Debray, Danielle Burke, Johannes B. Reitsma, Maarten van Smeden and Glen P. Martin. Their work appears in journals such as Statistics in Medicine, BMJ, Research Synthesis Methods, Journal of Clinical Epidemiology and Statistical Methods in Medical Research.
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.