Didier Renard

2.4k total citations
42 papers, 1.7k citations indexed

About

Didier Renard is a scholar working on Statistics and Probability, Pulmonary and Respiratory Medicine and Management Science and Operations Research. According to data from OpenAlex, Didier Renard has authored 42 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Statistics and Probability, 7 papers in Pulmonary and Respiratory Medicine and 6 papers in Management Science and Operations Research. Recurrent topics in Didier Renard's work include Statistical Methods in Clinical Trials (19 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (7 papers). Didier Renard is often cited by papers focused on Statistical Methods in Clinical Trials (19 papers), Advanced Causal Inference Techniques (8 papers) and Statistical Methods and Bayesian Inference (7 papers). Didier Renard collaborates with scholars based in Belgium, United States and Switzerland. Didier Renard's co-authors include Geert Molenberghs, Helena Geys, Tomasz Burzykowski, Marc Buyse, Philip J. Lowe, Tony Vangeneugden, Annouschka Laenen, Paul Van Royen, Samuel Coenen and B. Michiels and has published in prestigious journals such as Journal of the American College of Cardiology, Biometrics and CHEST Journal.

In The Last Decade

Didier Renard

40 papers receiving 1.7k citations

Peers

Didier Renard
Comparison fields: 5 of 153
  • Statistics and Probability 633
  • Economics and Econometrics 336
  • Pulmonary and Respiratory Medicine 310
  • Oncology 177
  • Physiology 168
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Citations per field, relative to Didier Renard
Didier Renard · 1×
Citations per year, relative to Didier Renard
Didier Renard · 1×

Countries citing papers authored by Didier Renard

Since Specialization
Citations

This map shows the geographic impact of Didier Renard'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 Didier Renard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Didier Renard more than expected).

Fields of papers citing papers by Didier Renard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Didier Renard. 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 Didier Renard. The network helps show where Didier Renard may publish in the future.

Co-authorship network of co-authors of Didier Renard

This figure shows the co-authorship network connecting the top 25 collaborators of Didier Renard. A scholar is included among the top collaborators of Didier Renard 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 Didier Renard. Didier Renard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 40
2
'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME Models [R package ggPMX version 1.1.2]
1
3 3
4 26
5 30
6 6
7 35
8 28
9 3
10 8
11 93
12 4
13 60
14 46
15 10
16 79
17 150
18 8
19 428
20 35

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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