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My research investigates approaches for deriving and validating prediction models when multiple sources of evidence are available. Particularly, we propose methods to aggregate previously published prediction models and associations with new data, methods to combine multiple datasets, methods to quantify heterogeneity and interpret external validity.

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Scientific papers 2018

Scientific papers 2017

Kockelkoren R, Jairam PM, Murchison JT, Debray TPA, Mirsadraee S, van der Graaf Y, de Jong PA, van Beek EWR. Validation of an imaging based cardiovascular risk score in a Scottish population. Eur J Radiol 98 (pp. 143-149).

van Hoorn R, Tummers M, Booth A, Gerhardus A, Rehfuess E, Hind D, Bossuyt PM, Welch V, Debray TP, Underwood M, Cuijpers P, Kraemer H, van der Wilt GJ, Kievit W. The development of CHAMP: a checklist for the appraisal of moderators and predictors. BMC Med Res Methodol 173 (pp. ).

Debray TP, Moons KG, Riley RD. Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: a comparison of new and existing tests. Res Synth Methods 0 (pp. ).

Makady A, Stegenga H, Ciaglia A, Debray TPA, Lees M, Happich M, Ryll B, Abrams K, Thwaites R, Jonsson P, Goettsch W on behalf of GetReal Work Packages 1 & 4. Practical Implications of Using Real-World Evidence in Comparative Effectiveness Research: Learnings from IMI-GetReal. J Comp Eff Res 6 (pp. 485-490).

Snell KI, Ensor J, Debray TP, Moons KG, Riley RD. Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?. Stat Methods Med Res 0 (pp. ).

Rietbergen C, Debray TPA, Klugkist I, Janssen KJM, Moons KG. Reporting of Bayesian analysis in epidemiologic research should become more transparent. J Clin Epidemiol 86 (pp. 51-58).

van Doorn S, Debray TPA, Kaasenbrood F, Hoes AW, Rutten FH, Moons KGM, Geersing GJ. Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis. J Thromb Haemost 15 (pp. 1-13).

Efthimiou O, Mavridis D, Debray TP, Samara M, Belger M, Siontis GC, Leucht S, Salanti G, on behalf of GetReal Work Package 4. Combining randomized and non-randomized evidence in network meta-analysis. Stat Med 36 (pp. 1210-1226).

Debray TPA, Damen JAAG, Snell, KIE, Ensor J, Hooft L, Reitsma JB, Riley RD, Moons KGM. A guide to systematic review and meta-analysis of prediction model performance. BMJ 356 (pp. i6460).

Hummel N, Debray TPA, Didden E-M, Efthimiou O, Egger M, Fletcher C, Moons KG, Reitsma JB, Ruffieux Y, Salanti G, van Valkenhoef G, on behalf of WP4. Methodological guidance, recommendations and illustrative case studies for (network) meta-analysis and modelling to predict real-world effectiveness using individual participant and/or aggregate data. 0 (pp. ).

Makady A, Stegenga H, Ciaglia A, Debray T, Lees M, Ryll B, Abrams K, Thwaites R, Garner S, Jonsson P, Goettsch W. Practicalities Of Using Real-World Evidence (RWE) In Comparative Effectiveness Research (CER). Value in Health 9 (pp. A692).

Scientific papers 2016

Vergouwe Y, Nieboer D, Oostenbrink R, Debray TPA, Murray G, Kattan M, Koffijberg H, Moons KGM, Steyerberg EW. A closed testing procedure to select an appropriate method for updating prediction models. Stat Med 36 (pp. 4529-4539).

Panayidou K, Gsteiger S, Egger M, Kilcher G, Carreras M, Efthimiou O, Debray TP, Trelle S, Hummel N, on behalf of the GetReal methods review group. GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world. Res Synth Methods 7 (pp. 264-77).

Debray TP, Schuit E, Efthimiou O, Reitsma JB, Ioannidis JP, Salanti G, Moons KG, on behalf of GetReal Workpackage 4. An overview of methods for network meta-analysis using individual participant data: when do benefits arise?. Stat Methods Med Res 0 (pp. ).

Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 353 (pp. i3140).

Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 353 (pp. i2416).

Groenwold RH, Moons KG, Pajouheshnia R, Altman DG, Collins GS, Debray TP, et al. Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings. J Clin Epidemiol 78 (pp. 90-100).

Efthimiou O, Debray TP, van Valkenhoef G, Trelle S, Panayidou K, Moons KG, Reitsma JB, Shang A, Salanti G, on behalf of the GetReal methods review group. GetReal in network meta-analysis: a review of the methodology. Res Synth Methods 7 (pp. 236-63).

Snell KI, Hua H, Debray TP, Ensor J, Look MP, Moons KG, Riley RD. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol 69 (pp. 40-50).

Scientific papers 2015

Ayele HT, Mourik MS, Debray TP, Bonten MJ. Isoniazid Prophylactic Therapy for the Prevention of Tuberculosis in HIV Infected Adults: A Systematic Review and Meta-Analysis of Randomized Trials. PLoS One 10 (pp. e0142290).

Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG, on behalf of the Cochrane IPD Meta-analysis Methods group. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med 12 (pp. e1001886).

Debray TP, Moons KG, van Valkenhoef G, Efthimiou O, Hummel N, Groenwold RH, Reitsma JB, on behalf of the GetReal methods review group. Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods 6 (pp. 293-309).

Riley RD, Ahmed I, Debray TP, Willis BH, Noordzij JP, Higgins JP, Deeks JJ. Summarising and validating test accuracy results across multiple studies for use in clinical practice. Stat Med 34 (pp. 2081-103).

Debray TP, Jolani S, Koffijberg H, van Buuren S, Moons KG. Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Stat Med 34 (pp. 1841-63).

Nell S, Kist JW, Debray TP, de Keizer B, van Oostenbrugge TJ, Borel Rinkes IH, Valk GD, Vriens MR. Qualitative elastography can replace thyroid nodule fine-needle aspiration in patients with soft thyroid nodules. A systematic review and meta-analysis. Eur J Radiol 84 (pp. 652-61).

Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 68 (pp. 279-89).

Scientific papers 2014

Debray TP, Koffijberg H, Nieboer D, Vergouwe Y, Steyerberg EW, Moons KG. Meta-analysis and aggregation of multiple published prediction models. Stat Med 33 (pp. 2341-62).

Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol 14 (pp. 3).

Scientific papers 2013

Onland W, Debray TP, Laughon MM, Miedema M, Cools F, Askie LM, et al. Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study. BMC Pediatr 13 (pp. 207).

Abo-Zaid G, Guo B, Deeks JJ, Debray TP, Steyerberg EW, Moons KG, Riley RD. Individual participant data meta-analyses should not ignore clustering. J Clin Epidemiol 66 (pp. 865-873).

Debray TP, Moons KG, Abo-Zaid GM, Koffijberg H, Riley RD. Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?. PLoS One 8 (pp. e60650).

Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med 32 (pp. 3158-80).

Scientific papers 2012

Debray TP, Koffijberg H, Lu D, Vergouwe Y, Steyerberg EW, Moons KG. Incorporating published univariable associations in diagnostic and prognostic modeling. BMC Med Res Methodol 12 (pp. 121).

Debray TPA, Koffijberg H, Vergouwe Y, Moons KGM, Steyerberg EW. Aggregating published prediction models with individual patient data: a comparison of different approaches. Stat Med 31 (pp. 2697-712).

Janssen KJ, Siccama I, Vergouwe Y, Koffijberg H, Debray TP, Keijzer M, Grobbee DE, Moons KG. Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming. J Clin Epidemiol 65 (pp. 404-12).

Book chapters

Steyerberg EW, Nieboer D, Debray TPA, van Houwelingen H (2018; expected). Chapter 24: Meta-analysis of prognostic models. In C. Smith, T. Stijnen, I. White (Ed.), Handbook of Meta-Analysis (pp. xxx–xxx).



Debray TPA. Meta-analysis of clinical prediction models. PhD thesis (2013). Julius Center for Health Sciences and Primary Care. Utrecht University , Utrecht, The Netherlands. [Full Text]

Debray TPA. Classification in Imbalanced Datasets. MSc thesis (2009). Department of Knowledge Engineering. Maastricht University , Maastricht, The Netherlands. [Full Text]