Thomas P. A. Debray

15.1k total citations · 9 hit papers
108 papers, 6.4k citations indexed

About

Thomas P. A. Debray is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Economics and Econometrics. According to data from OpenAlex, Thomas P. A. Debray has authored 108 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Statistics and Probability, 41 papers in Statistics, Probability and Uncertainty and 39 papers in Economics and Econometrics. Recurrent topics in Thomas P. A. Debray's work include Meta-analysis and systematic reviews (40 papers), Health Systems, Economic Evaluations, Quality of Life (37 papers) and Advanced Causal Inference Techniques (24 papers). Thomas P. A. Debray is often cited by papers focused on Meta-analysis and systematic reviews (40 papers), Health Systems, Economic Evaluations, Quality of Life (37 papers) and Advanced Causal Inference Techniques (24 papers). Thomas P. A. Debray collaborates with scholars based in Netherlands, United Kingdom and United States. Thomas P. A. Debray's co-authors include Karel G.M. Moons, Richard D Riley, Kym I E Snell, Gary S. Collins, Johannes B. Reitsma, Joie Ensor, Lotty Hooft, Hendrik Koffijberg, Johanna AAG Damen and Ewout W. Steyerberg and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Thomas P. A. Debray

101 papers receiving 6.3k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prediction models for cardiovascular disease risk in the general population: systematic review

2016 588 citations Johanna AAG Damen, Lotty Hooft et al. BMJ profile →
A guide to systematic review and meta-analysis of prognostic factor studies

2019 521 citations Richard D Riley, Karel G.M. Moons et al. BMJ profile →
A guide to systematic review and meta-analysis of prediction model performance

2017 402 citations Thomas P. A. Debray, Johanna AAG Damen et al. BMJ profile →
A new framework to enhance the interpretation of external validation studies of clinical prediction models

2014 371 citations Thomas P. A. Debray, Karel G.M. Moons et al. Journal of Clinical Epidemiology profile →
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

2016 348 citations Richard D Riley, Joie Ensor et al. BMJ profile →
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

2022 279 citations Lotty Hooft, Thomas P. A. Debray et al. profile →
Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 186 citations Richard D Riley, Thomas P. A. Debray et al. Statistics in Medicine profile →
Developing clinical prediction models: a step-by-step guide

2024 73 citations Orestis Efthimiou, Michael Seo et al. BMJ profile →
Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study

2024 68 citations Richard D Riley, Joie Ensor et al. BMJ profile →

Peers

Countries citing papers authored by Thomas P. A. Debray

Since Specialization

Citations

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

Fields of papers citing papers by Thomas P. A. Debray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas P. A. Debray. 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 Thomas P. A. Debray. The network helps show where Thomas P. A. Debray may publish in the future.

Co-authorship network of co-authors of Thomas P. A. Debray

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Risk prediction models for cancer therapy related cardiac dysfunction in patients with cancer and cancer survivors: systematic review and meta-analysis 2025 ·BMJ ·(unknown), (unknown), Thomas P. A. Debray, Arjan Malekzadeh, Folkert W. Asselbergs, Marijke Linschoten	1
2	POS0578 REAL-WORLD EFFECTIVENESS OF FILGOTINIB IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED FOR UP TO 2 YEARS IN CLINICAL PRACTICE: INTERIM RESULTS FROM FILOSOPHY AND PARROTFISH 2025 ·Annals of the Rheumatic Diseases ·Patrick Verschueren, (unknown), G. R. Burmester, Alejandro Escudero‐Contreras, (unknown), Thomas P. A. Debray, Carole Van der Donckt, Neil D McKay, (unknown), Roberto Caporali	0
3	Developing clinical prediction models: a step-by-step guide breakdown → 2024 ·BMJ ·Orestis Efthimiou, Michael Seo, Konstantina Chalkou, Thomas P. A. Debray, Matthias Egger, Georgia Salanti	73
4	Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods 2023 ·American Journal of Epidemiology ·Tri‐Long Nguyen, Thomas P. A. Debray, (unknown), Gabrielle Simoneau, Gary S. Collins	4
5	Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population 2023 ·Statistics in Medicine ·Valentijn M. T. de Jong, Jeroen Hoogland, Karel G.M. Moons, Richard D Riley, Tri‐Long Nguyen, Thomas P. A. Debray	1
6	Measuring the performance of prediction models to personalize treatment choice 2023 ·Statistics in Medicine ·Orestis Efthimiou, Jeroen Hoogland, Thomas P. A. Debray, Michael Seo, Toshi A. Furukawa, Matthias Egger, Ian R. White	18
7	Multiple imputation of incomplete multilevel data using Heckman selection models 2023 ·Statistics in Medicine ·(unknown), Orestis Efthimiou, Vincent Audigier, Valentijn M. T. de Jong, Thomas P. A. Debray	1
8	Implementing clinical trial data sharing requires training a new generation of biomedical researchers 2023 ·Nature Medicine ·Ulrich Mansmann, Clara Locher, Fabian Praßer, Tracey L. Weissgerber, Ulrich Sax, Martin Posch, Évelyne Decullier, Ioana A. Cristea, Thomas P. A. Debray, Leonhard Held, David Moher, John P. A. Ioannidis, Joseph S. Ross, Christian Ohmann, Florian Naudet	14
9	Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions 2022 ·Statistical Methods in Medical Research ·Michael Seo, Thomas P. A. Debray, Yann Ruffieux, Sandro Gsteiger, Sylwia Bujkiewicz, Axel Finckh, Matthias Egger, Orestis Efthimiou	4
10	Predicting personalised absolute treatment effects in individual participant data meta‐analysis: An introduction to splines 2022 ·Research Synthesis Methods ·(unknown), Maroeska M. Rovers, Jeroen Hoogland, Johannes B. Reitsma, Thomas P. A. Debray, Joanna IntHout	6
11	Systematic Review Reveals Lack of Causal Methodology Applied to Pooled Longitudinal Observational Infectious Disease Studies 2022 ·Journal of Clinical Epidemiology ·(unknown), (unknown), Alexander M. Danzer, (unknown), Valentijn M. T. de Jong, Harlan Campbell, Paul Gustafson, Thomas P. A. Debray, Thomas Jaenisch, Lauren Maxwell, Ellicott C. Matthay, Till Bärnighausen	2
12	A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint 2021 ·Statistics in Medicine ·Jeroen Hoogland, Joanna IntHout, (unknown), Maroeska M. Rovers, Richard D Riley, Frank E. Harrell, Karel G.M. Moons, Thomas P. A. Debray, Johannes B. Reitsma	36
13	Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies—A protocol for a methodological systematic review 2021 ·PLoS ONE ·(unknown), Ellicott C. Matthay, (unknown), Valentijn M. T. de Jong, Harlan Campbell, Paul Gustafson, Thomas P. A. Debray, Thomas Jaenisch, Lauren Maxwell, Till Bärnighausen	3
14	Current trends in the application of causal inference methods to pooled longitudinal non-randomised data: a protocol for a methodological systematic review 2021 ·BMJ Open ·(unknown), (unknown), (unknown), Sinclair Carr, Ellicott C. Matthay, Lauren Maxwell, (unknown), Thomas P. A. Debray, Valentijn M. T. de Jong, Harlan Campbell, Paul Gustafson, Thomas Jänisch, Till Bärnighausen	3
15	Strategies to reduce antibiotic use in women with uncomplicated urinary tract infection in primary care: protocol of a systematic review and meta-analysis including individual patient data 2020 ·BMJ Open ·Judith Heinz, Christian Röver, (unknown), Yvonne Kaußner, Eva Hummers, Thomas P. A. Debray, Alastair D Hay, Stefan Heytens, (unknown), Paul Little, Michael Moore, Beth Stuart, Florian Wagenlehner, Andreas Kronenberg, Sven Ferry, Tor Monsen, Morten Lindbæk, Tim Friede, Ildikó Gágyor	6
16	Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning 2020 ·Statistics in Medicine ·Richard D Riley, Thomas P. A. Debray, David J. Fisher, Miriam Hattle, Nadine Marlin, Jeroen Hoogland, François Gueyffier, Jan A. Staessen, Ji‐Guang Wang, Karel G.M. Moons, Johannes B. Reitsma, Joie Ensor	87
17	Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling 2019 ·Multiple Sclerosis Journal ·Fabio Pellegrini, Massimiliano Copetti, Maria Pia Sormani, Francesca Bovis, Carl de Moor, Thomas P. A. Debray, Bernd C. Kieseier	15
18	Empirical evidence of the impact of study characteristics on the performance of prediction models: a meta-epidemiological study 2019 ·BMJ Open ·Johanna AAG Damen, Thomas P. A. Debray, Romin Pajouheshnia, Johannes B. Reitsma, Rob Scholten, Karel G.M. Moons, Lotty Hooft	18
19	Cardiovascular risk prediction models for women in the general population: A systematic review 2019 ·PLoS ONE ·Sara J. Baart, Veerle Dam, Luuk J.J. Scheres, Johanna AAG Damen, René Spijker, Ewoud Schuit, Thomas P. A. Debray, Bart C.J.M. Fauser, Eric Boersma, Karel G.M. Moons, Yvonne T. van der Schouw	37
20	Combining randomized and non‐randomized evidence in network meta‐analysis 2017 ·Statistics in Medicine ·Orestis Efthimiou, Dimitris Mavridis, Thomas P. A. Debray, Myrto Samara, Mark Belger, George C.M. Siontis, Stefan Leucht, Georgia Salanti, (unknown)	130

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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