Richard D Riley
- Epidemiology top 0.5%
- Surgery top 0.5%
- Statistics, Probability and Uncertainty top 0.02%
- Pulmonary and Respiratory Medicine top 0.5%
- Cardiology and Cardiovascular Medicine top 0.5%
- Co-authors
- Karel G.M. MoonsGary S. CollinsJonathan J DeeksJulian P. T. HigginsJoie EnsorKym I E SnellJohannes B. ReitsmaPaul C. Lambert
- Topics
- Meta-analysis and systematic reviews (131 papers)Health Systems, Economic Evaluations, Quality of Life (61 papers)Statistical Methods in Clinical Trials (54 papers)
- Partner nations
- United KingdomNetherlandsUnited States
In The Last Decade
Richard D Riley
320 papers receiving 27.5k citations
Hit Papers
Peers
Comparison fields: 5 of 221
- Epidemiology 4.0k
- Surgery 4.0k
- Statistics, Probability and Uncertainty 3.6k
- Pulmonary and Respiratory Medicine 2.9k
- Cardiology and Cardiovascular Medicine 2.9k
Countries citing papers authored by Richard D Riley
This map shows the geographic impact of Richard D Riley'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 Richard D Riley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard D Riley more than expected).
Fields of papers citing papers by Richard D Riley
This network shows the impact of papers produced by Richard D Riley. 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 Richard D Riley. The network helps show where Richard D Riley may publish in the future.
Co-authorship network of co-authors of Richard D Riley
This figure shows the co-authorship network connecting the top 25 collaborators of Richard D Riley. A scholar is included among the top collaborators of Richard D Riley based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard D Riley. Richard D Riley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | 58 | |
| 9 | 2 | |
| 10 | 3 | |
| 11 | 88 | |
| 12 | 10 | |
| 13 | 8 | |
| 14 | 17 | |
| 15 | Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomesbreakdown → | 581 |
| 16 | 3 | |
| 17 | 29 | |
| 18 | 22 | |
| 19 | Quantifying the impact of between‐study heterogeneity in multivariate meta‐analysesbreakdown → | 482 |
| 20 | 75 |
About Richard D Riley
Richard D Riley is a scholar working on Statistics, Probability and Uncertainty, Health Informatics and Statistics and Probability, having authored 343 papers that have together received 28.0k indexed citations. Recurring topics across this work include Meta-analysis and systematic reviews (131 papers), Health Systems, Economic Evaluations, Quality of Life (61 papers) and Statistical Methods in Clinical Trials (54 papers). The work is most often cited by research in Health Informatics (1.1k citations), Statistics, Probability and Uncertainty (3.6k citations) and Statistics and Probability (2.4k citations). Richard D Riley has collaborated with scholars based in United Kingdom, Netherlands and United States. Frequent co-authors include Karel G.M. Moons, Gary S. Collins, Jonathan J Deeks, Julian P. T. Higgins, Joie Ensor, Kym I E Snell, Johannes B. Reitsma, Paul C. Lambert, Thomas P. A. Debray and Dan Jackson. Their work appears in journals such as JAMA, Nature Communications and Journal of Clinical Oncology.
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.