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1. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease

Author

Bertens LCM, Reitsma JB, Moons KGM, van Mourik Y, Lammers JWJ, Broekhuizen BDL, Hoes AW, and Rutten FH

Subjects
Diseases of the respiratory system, RC705-779
Abstract

Loes CM Bertens,1 Johannes B Reitsma,1 Karel GM Moons,1 Yvonne van Mourik,1 Jan Willem J Lammers,2 Berna DL Broekhuizen,1 Arno W Hoes,1 Frans H Rutten1 1Julius Center for Health Sciences and Primary Care, 2Department of Pulmonology, Heart-Lung Center, University Medical Center Utrecht, Utrecht, the Netherlands Purpose: Prediction models for exacerbations in patients with chronic obstructive pulmonary disease (COPD) are scarce. Our aim was to develop and validate a new model to predict exacerbations in patients with COPD. Patients and methods: The derivation cohort consisted of patients aged 65 years or over, with a COPD diagnosis, who were followed up over 24 months. The external validation cohort consisted of another cohort of COPD patients, aged 50 years or over. Exacerbations of COPD were defined as symptomatic deterioration requiring pulsed oral steroid use or hospitalization. Logistic regression analysis including backward selection and shrinkage were used to develop the final model and to adjust for overfitting. The adjusted regression coefficients were applied in the validation cohort to assess calibration of the predictions and calculate changes in discrimination applying C-statistics. Results: The derivation and validation cohort consisted of 240 and 793 patients with COPD, of whom 29% and 28%, respectively, experienced an exacerbation during follow-up. The final model included four easily assessable variables: exacerbations in the previous year, pack years of smoking, level of obstruction, and history of vascular disease, with a C-statistic of 0.75 (95% confidence interval [CI]: 0.69–0.82). Predictions were well calibrated in the validation cohort, with a small loss in discrimination potential (C-statistic 0.66 [95% CI 0.61–0.71]). Conclusion: Our newly developed prediction model can help clinicians to predict the risk of future exacerbations in individual patients with COPD, including those with mild disease. Keywords: exacerbation of COPD, risk prediction, external validation, vascular disease

Published
2013

2. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Author

Hoogland, J, IntHout, J, Belias, M, Rovers, MM, Riley, RD, Harrell Jr, FE, Moons, KGM, Debray, TPA, and Reitsma, JB

Subjects
Statistics - Methodology
Abstract

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (i.e. varying with patient characteristics). In this paper we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrates the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data., Comment: 27 pages, 5 figures, 1 ancillary file

Published
2021
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4. External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

Author

Epi Methoden Team 2, Datascience, Infection & Immunity, Epi Methoden, JC onderzoeksprogramma Methodologie, Cancer, Epi Methoden Team 3, Circulatory Health, HAG Trombose, Zahra, Anum, van Smeden, Maarten, Moons, KGM, Luijken, Kim, Appelman, Brent, Abbink, Evertine J., Berg, Jesse M van den, Blom, Marieke T., van den Dries, Carline, Gussekloo, Jacobijn, Wouters, Fenne, Joling, Karlijn, Melis, René J.F., Mooijaart, Simon P., Peters, Jeannette, Polinder-Bos, Harmke, Raaij, Bas F M van, Teeuw, Hannah Marieke, Epi Methoden Team 2, Datascience, Infection & Immunity, Epi Methoden, JC onderzoeksprogramma Methodologie, Cancer, Epi Methoden Team 3, Circulatory Health, HAG Trombose, Zahra, Anum, van Smeden, Maarten, Moons, KGM, Luijken, Kim, Appelman, Brent, Abbink, Evertine J., Berg, Jesse M van den, Blom, Marieke T., van den Dries, Carline, Gussekloo, Jacobijn, Wouters, Fenne, Joling, Karlijn, Melis, René J.F., Mooijaart, Simon P., Peters, Jeannette, Polinder-Bos, Harmke, Raaij, Bas F M van, and Teeuw, Hannah Marieke

Published
2024

5. A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

Author

Epi Methoden Team 2, Datascience, Infection & Immunity, Epi Methoden Team 3, HAG Trombose, Epi Methoden, JC onderzoeksprogramma Methodology, Cancer, Zahra, Anum, van Smeden, Maarten, Luijken, Kim, Abbink, Evertine J., Blom, Marieke T., Elders, Petra J. M., Festen, Jan, Gussekloo, Jacobijn, Mooijaart, Simon P., Teeuw, Hannah Marieke, Polinder-Bos, Harmke, Moons, KGM, Berg, Jesse M van den, Joling, Karlijn, Melis, René J.F., Peters, Jeannette, Raaij, Bas F M van, Smorenberg, Annemieke, Epi Methoden Team 2, Datascience, Infection & Immunity, Epi Methoden Team 3, HAG Trombose, Epi Methoden, JC onderzoeksprogramma Methodology, Cancer, Zahra, Anum, van Smeden, Maarten, Luijken, Kim, Abbink, Evertine J., Blom, Marieke T., Elders, Petra J. M., Festen, Jan, Gussekloo, Jacobijn, Mooijaart, Simon P., Teeuw, Hannah Marieke, Polinder-Bos, Harmke, Moons, KGM, Berg, Jesse M van den, Joling, Karlijn, Melis, René J.F., Peters, Jeannette, Raaij, Bas F M van, and Smorenberg, Annemieke

Published
2023

6. UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure

Author

Clare J Taylor, Vazquez-Montes Mdla., Nicholas R Jones, Emmanuella Magriplis, Hugo Maruri-Aguilar, J Parissis, Hobbs Fdrr., Marialena Trivella, Kathryn Mary Taylor, Moons Kgm., Gary S. Collins, Rafael Perera, Debray Tpa., Kadoglou Npe., Benjamin Speich, and Nia Roberts

Subjects
medicine.medical_specialty, CHARMS, MEDLINE, Prediction rule, Covariate, Protocol, Medicine, Intensive care medicine, Protocol (science), lcsh:R5-920, Framingham Risk Score, business.industry, Acute heart failure, Decompensated heart failure, Prediction accuracy, Prognosis, PROBAST, Chronic heart failure, Critical appraisal, Systematic review, Data extraction, Biomarker (medicine), Risk score, business, lcsh:Medicine (General), Biomarkers
Abstract

BackgroundHeart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present.MethodsWe will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed.DiscussionThis project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews.Registration: PROSPERO registration number CRD42019086990

Published
2020
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7. Real-time imputation of missing predictor values in clinical practice

Author

Nijman, steven, Debray, T.P.A. (Thesis Advisor), Nijman SWJ, Hoogland J, Groenhof TKJ, Brandjes M, Jacobs JJL, Bots ML, Asselbergs FW, Moons KGM, Debray TPA, Nijman, steven, Debray, T.P.A. (Thesis Advisor), and Nijman SWJ, Hoogland J, Groenhof TKJ, Brandjes M, Jacobs JJL, Bots ML, Asselbergs FW, Moons KGM, Debray TPA

Abstract

The identification of individual patients at risk of disease has become an integral part of recent trends towards a more personalized healthcare system. A healthcare system that is personalized allows us to administer the most applicable treatment to an individual patient given their risk profile and, in turn, make our healthcare much more efficient. To that end, clinical prediction models are situated as prime candidates to assist clinicians with accurate risk estimates. By harnessing the information captured in various patient or disease related properties, these risk prediction models are able to chart a likely path that a disease might take (i.e., prognosis) or identify whether a specific disease is likely present in individual patients (i.e., diagnosis). Recent efforts to computerize the use of various clinical prediction models in clinical practice have provided clinical decision support systems (CDSS) that are already usable in clinical practice. These CDSS already allow clinicians to potentially inform their clinical decision making by providing individual risk probabilities. However, because currently available risk prediction models require complete information to generate predictions, these models are severely hampered whenever any patient or disease properties are missing. Luckily, the ample guidance that exists on the handling of missing data provides useful stepping stones to develop flexible or missing data handling techniques usable in real-time clinical practice. The development of several imputation methods for missing predictor values in real-time were presented previously. In a case-study with a real-world empirical data set for cardiovascular risk prediction, the accuracy of two common imputation methods which were adjusted for use in real time clinical practice were compared. These consisted of conditional modeling imputation (CMI, where for each predictor a separate multivariable imputation model is derived) and joint modeling imputation (J

Published
2022

8. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Author

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD, Moons, KGM, and Hooft, L

Subjects
Medicine (General), Blinding, Epidemiology, Health Informatics, Development, Machine learning, computer.software_genre, Machine Learning, R5-920, Prediction model, Diagnosis, Validation, TRIPOD, Humans, Medicine, Models, Statistical, Reporting adherence, business.industry, Research, Reporting guideline, External validation, Usability, Prognosis, R1, Regression, Checklist, Data extraction, Supervised Machine Learning, Artificial intelligence, Completeness (statistics), business, RA, computer, Predictive modelling
Abstract

ObjectiveWhile many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.Study design and settingWe included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields (PROSPERO, CRD42019161764). We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item.ResultsOur search identified 24 814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0-46.4) of TRIPOD items. No articles fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3).ConclusionSimilar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste.What is new?Key findings: Similar to prediction model studies developed using regression techniques, machine learning (ML)-based prediction model studies adhered poorly to the TRIPOD statement, the current standard reporting guideline.What this adds to what is known? In addition to efforts to improve the completeness of reporting in ML-based prediction model studies, an extension of TRIPOD for these type of studies is needed.What is the implication, what should change now? While TRIPOD-AI is under development, we urge authors to follow the recommendations of the TRIPOD statement to improve the completeness of reporting and reduce potential research waste of ML-based prediction model studies.

Published
2022

9. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

Author

Achenbach, S, Aleksandrova, K, Amiano, P, San Sebastian, D, Amouyel, P, Andersson, J, Bakker, S, Da Providencia Costa, R, Beulens, J, Blaha, M, Bobak, M, Boer, J, Bonet, C, Bonnet, F, Boutron-Ruault, M, Braaten, T, Brenner, H, Brunner, F, Brunner, E, Brunström, M, Buring, J, Butterworth, A, Capkova, N, Cesana, G, Chrysohoou, C, Colorado-Yohar, S, Cook, N, Cooper, C, Dahm, C, Davidson, K, Dennison, E, Di Castelnuovo, A, Donfrancesco, C, Dörr, M, Doryńska, A, Eliasson, M, Engström, G, Ferrari, P, Ferrario, M, Ford, I, Fu, M, Gansevoort, R, Giampaoli, S, Gillum, R, Gómez de la Cámara, A, Grassi, G, Hansson, P, Huculeci, R, Hveem, K, Iacoviello, L, Ikram, M, Jørgensen, T, Joseph, B, Jousilahti, P, Wouter Jukema, J, Kaaks, R, Katzke, V, Kavousi, M, Kiechl, S, Klotsche, J, König, W, Kronmal, R, Kubinova, R, Kucharska-Newton, A, Läll, K, Lehmann, N, Leistner, D, Linneberg, A, Pablos, D, Lorenz, T, Lu, W, Luksiene, D, Lyngbakken, M, Magnussen, C, Malyutina, S, Ibañez, A, Masala, G, Mathiesen, E, Matsushita, K, Meade, T, Melander, O, Meyer, H, Moons, K, Moreno-Iribas, C, Muller, D, Münzel, T, Nikitin, Y, Nordestgaard, B, Omland, T, Onland, C, Overvad, K, Packard, C, Pająk, A, Palmieri, L, Panagiotakos, D, Panico, S, Perez-Cornago, A, Peters, A, Pietilä, A, Pikhart, H, Psaty, B, Quarti-Trevano, F, Garcia, J, Riboli, E, Ridker, P, Rodriguez, B, Rodriguez-Barranco, M, Rosengren, A, Roussel, R, Sacerdote, C, S, S, Sattar, N, Schiborn, C, Schmidt, B, Schöttker, B, Schulze, M, Schwartz, J, Selmer, R, Shea, S, Shipley, M, Sieri, S, Söderberg, S, Sofat, R, Tamosiunas, A, Thorand, B, Tillmann, T, Tjønneland, A, Tong, T, Trichopoulou, A, Tumino, R, Tunstall-Pedoe, H, Tybjaerg-Hansen, A, Tzoulaki, J, van der Heijden, A, van der Schouw, Y, Verschuren, W, Völzke, H, Waldeyer, C, Wareham, N, Weiderpass, E, Weidinger, F, Wild, P, Willeit, J, Willeit, P, Wilsgaard, T, Woodward, M, Zeller, T, Zhang, D, Zhou, B, Bakker, SJL, Da Providencia Costa, RB, Beulens, JWJ, Boer, JMA, Boutron-Ruault, MC, Brunner, EJ, Butterworth, AS, Cook, NR, Dahm, CC, Gansevoort, RT, Gillum, RF, Hansson, PO, Ikram, MK, Kronmal, RA, Pablos, DL, Malyutina, So, Ibañez, AM, Mathiesen, EB, Meade, TW, Meyer, HE, Moons, KGM, Nordestgaard, BG, Psaty, BM, Garcia, JRQ, Ridker, PM, C Sans, Schwartz, JE, Selmer, RM, Shipley, MJ, Tong, TYN, van der Schouw, YT, Verschuren, WMM, Wareham, NJ, Achenbach, S, Aleksandrova, K, Amiano, P, San Sebastian, D, Amouyel, P, Andersson, J, Bakker, S, Da Providencia Costa, R, Beulens, J, Blaha, M, Bobak, M, Boer, J, Bonet, C, Bonnet, F, Boutron-Ruault, M, Braaten, T, Brenner, H, Brunner, F, Brunner, E, Brunström, M, Buring, J, Butterworth, A, Capkova, N, Cesana, G, Chrysohoou, C, Colorado-Yohar, S, Cook, N, Cooper, C, Dahm, C, Davidson, K, Dennison, E, Di Castelnuovo, A, Donfrancesco, C, Dörr, M, Doryńska, A, Eliasson, M, Engström, G, Ferrari, P, Ferrario, M, Ford, I, Fu, M, Gansevoort, R, Giampaoli, S, Gillum, R, Gómez de la Cámara, A, Grassi, G, Hansson, P, Huculeci, R, Hveem, K, Iacoviello, L, Ikram, M, Jørgensen, T, Joseph, B, Jousilahti, P, Wouter Jukema, J, Kaaks, R, Katzke, V, Kavousi, M, Kiechl, S, Klotsche, J, König, W, Kronmal, R, Kubinova, R, Kucharska-Newton, A, Läll, K, Lehmann, N, Leistner, D, Linneberg, A, Pablos, D, Lorenz, T, Lu, W, Luksiene, D, Lyngbakken, M, Magnussen, C, Malyutina, S, Ibañez, A, Masala, G, Mathiesen, E, Matsushita, K, Meade, T, Melander, O, Meyer, H, Moons, K, Moreno-Iribas, C, Muller, D, Münzel, T, Nikitin, Y, Nordestgaard, B, Omland, T, Onland, C, Overvad, K, Packard, C, Pająk, A, Palmieri, L, Panagiotakos, D, Panico, S, Perez-Cornago, A, Peters, A, Pietilä, A, Pikhart, H, Psaty, B, Quarti-Trevano, F, Garcia, J, Riboli, E, Ridker, P, Rodriguez, B, Rodriguez-Barranco, M, Rosengren, A, Roussel, R, Sacerdote, C, S, S, Sattar, N, Schiborn, C, Schmidt, B, Schöttker, B, Schulze, M, Schwartz, J, Selmer, R, Shea, S, Shipley, M, Sieri, S, Söderberg, S, Sofat, R, Tamosiunas, A, Thorand, B, Tillmann, T, Tjønneland, A, Tong, T, Trichopoulou, A, Tumino, R, Tunstall-Pedoe, H, Tybjaerg-Hansen, A, Tzoulaki, J, van der Heijden, A, van der Schouw, Y, Verschuren, W, Völzke, H, Waldeyer, C, Wareham, N, Weiderpass, E, Weidinger, F, Wild, P, Willeit, J, Willeit, P, Wilsgaard, T, Woodward, M, Zeller, T, Zhang, D, Zhou, B, Bakker, SJL, Da Providencia Costa, RB, Beulens, JWJ, Boer, JMA, Boutron-Ruault, MC, Brunner, EJ, Butterworth, AS, Cook, NR, Dahm, CC, Gansevoort, RT, Gillum, RF, Hansson, PO, Ikram, MK, Kronmal, RA, Pablos, DL, Malyutina, So, Ibañez, AM, Mathiesen, EB, Meade, TW, Meyer, HE, Moons, KGM, Nordestgaard, BG, Psaty, BM, Garcia, JRQ, Ridker, PM, C Sans, Schwartz, JE, Selmer, RM, Shipley, MJ, Tong, TYN, van der Schouw, YT, Verschuren, WMM, and Wareham, NJ

Abstract

Aims: The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe. Methods and results : We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low-risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries. Conclusion : SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.

Published
2021

10. Diagnostic accuracy of rapid antigen tests in pre-/asymptomatic close contacts of individuals with a confirmed SARS-CoV-2 infection

Author

Schuit, E, primary, Veldhuijzen, IK, additional, Venekamp, RP, additional, van den Bijllaardt, W, additional, Pas, SD, additional, Lodder, EB, additional, Molenkamp, R, additional, GeurtsvanKessel, CH, additional, Velzing, J., additional, Huisman, RC, additional, Brouwer, L, additional, Boelsums, T, additional, Sips, GJ, additional, Benschop, KSM, additional, Hooft, L, additional, van de Wijgert, JHHM, additional, van den Hof, S, additional, and Moons, KGM, additional

Published
2021
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12. Fetal blood sampling in addition to intrapartum ST-analysis of the fetal electrocardiogram: evaluation of the recommendations in the Dutch STAN® trial

Author

Becker, J H, Westerhuis, M EMH, Sterrenburg, K, van den Akker, E SA, van Beek, E, Bolte, A C, van Dessel, T JHM, Drogtrop, AP, van Geijn, H P, Graziosi, G CM, van Lith, J MM, Mol, BWJ, Moons, KGM, Nijhuis, JG, Oei, SG, Oosterbaan, HP, Porath, MM, Rijnders, RJP, Schuitemaker, NWE, Wijnberger, LDE, Willekes, C, Visser, GHA, and Kwee, A

Published
2011
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16. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis.

Author

Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, IPPIC Collaborative Network, Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, and IPPIC Collaborative Network

Abstract

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions

Published
2020

19. Impact of preventive screening and lifestyle interventions in women with a history of preeclampsia: A micro-simulation study

Author

HEE, Arts-assistenten DV&B, HEE Team 1, Circulatory Health, JC onderzoeksprogramma Methodologie, Epi Methoden, Cancer, MS Verloskunde, Brain, Lagerweij, G R, Brouwers, L, De Wit, G A, Moons, Kgm, Benschop, L, Maas, Ahem, Franx, A, Wermer, Marieke J H, Roeters Van Lennep, Jeanine E., van Rijn, B B, Koffijberg, H, HEE, Arts-assistenten DV&B, HEE Team 1, Circulatory Health, JC onderzoeksprogramma Methodologie, Epi Methoden, Cancer, MS Verloskunde, Brain, Lagerweij, G R, Brouwers, L, De Wit, G A, Moons, Kgm, Benschop, L, Maas, Ahem, Franx, A, Wermer, Marieke J H, Roeters Van Lennep, Jeanine E., van Rijn, B B, and Koffijberg, H

Published
2020

20. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Author

Harry Hemingway, Rayid Ghani, Pall Jonsson, Franz J. Király, Moons Kgm., Christopher Holmes, Richard Branson, Sebastian J. Vollmer, McAllister Ksl., Gergo Bohner, Gary S. Collins, Bilal A. Mateen, A Jonas, Mark Birse, Ioannidis Jpa., Puja R. Myles, David Granger, and Sarah Cumbers

Subjects
Research groups, Best practice, MEDLINE, 030204 cardiovascular system & hematology, Machine learning, computer.software_genre, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Surveys and Questionnaires, Humans, 030212 general & internal medicine, Data collection, business.industry, Data Collection, Reproducibility of Results, General Medicine, Transparency (behavior), Patient benefit, Ethical concerns, Artificial intelligence, business, Psychology, computer, Algorithms
Abstract

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.

Published
2020
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22. Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: A comparison of new and existing tests

Author

Debray, TPA, Moons, KGM, and Riley, RD

Subjects
education, H1, Q1
Abstract

Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In this paper, we demonstrate that the application of these tests may be less appropriate in meta-analysis of survival data, where censoring influences statistical significance of the hazard ratio. We subsequently propose 2 new tests that are based on the total number of observed events and adopt a multiplicative variance component. We compare the performance of the various funnel plot asymmetry tests in an extensive simulation study where we varied the true hazard ratio (0.5 to 1), the number of published trials (N=10 to 100), the degree of censoring within trials (0% to 90%), and the mechanism leading to participant dropout (noninformative versus informative). Results demonstrate that previous well-known tests for detecting funnel plot asymmetry suffer from low power or excessive type-I error rates in meta-analysis of survival data, particularly when trials are affected by participant dropout. Because our novel test (adopting estimates of the asymptotic precision as study weights) yields reasonable power and maintains appropriate type-I error rates, we recommend its use to evaluate funnel plot asymmetry in meta-analysis of survival data. The use of funnel plot asymmetry tests should, however, be avoided when there are few trials available for any meta-analysis.

Published
2018

23. Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies

Author

Pennells, L, Kaptoge, S, Wood, A, Sweeting, M, Zhao, X, White, I, Burgess, S, Willeit, P, Bolton, T, Moons, KGM, Van Der Schouw, YT, Selmer, R, Khaw, KT, Gudnason, V, Assmann, G, Amouyel, P, Salomaa, V, Kivimaki, M, Nordestgaard, BG, Blaha, MJ, Kuller, LH, Brenner, H, Gillum, RF, Meisinger, C, Ford, I, Knuiman, MW, Rosengren, A, Lawlor, DA, Völzke, H, Cooper, C, Marín Ibañez, A, Casiglia, E, Kauhanen, J, Cooper, JA, Rodriguez, B, Sundström, J ; https://orcid.org/0000-0003-2247-8454, Barrett-Connor, E, Dankner, R, Nietert, PJ, Davidson, KW, Wallace, RB, Blazer, DG, Björkelund, C, Donfrancesco, C, Krumholz, HM, Nissinen, A, Davis, BR, Coady, S, Whincup, PH, Jørgensen, T, Ducimetiere, P, Trevisan, M, Engström, G, Crespo, CJ, Meade, TW, Visser, M, Kromhout, D, Kiechl, S, Daimon, M, Price, JF, Gómez De La Cámara, A, Wouter Jukema, J, Lamarche, B, Onat, A, Simons, LA ; https://orcid.org/0000-0001-8082-6416, Kavousi, M, Ben-Shlomo, Y, Gallacher, J, Dekker, JM, Arima, H, Shara, N, Tipping, RW, Roussel, R, Brunner, EJ, Koenig, W, Sakurai, M, Pavlovic, J, Gansevoort, RT, Nagel, D, Goldbourt, U, Barr, ELM, Palmieri, L, Njølstad, I, Sato, S, Monique Verschuren, WM, Varghese, CV, Graham, I, Onuma, O, Greenland, P, Woodward, M ; https://orcid.org/0000-0001-9800-5296, Ezzati, M, Psaty, BM, Sattar, N, Jackson, R, Ridker, PM, Cook, NR, D'Agostino, RB, Thompson, SG, Danesh, J, Di Angelantonio, E, Pennells, L, Kaptoge, S, Wood, A, Sweeting, M, Zhao, X, White, I, Burgess, S, Willeit, P, Bolton, T, Moons, KGM, Van Der Schouw, YT, Selmer, R, Khaw, KT, Gudnason, V, Assmann, G, Amouyel, P, Salomaa, V, Kivimaki, M, Nordestgaard, BG, Blaha, MJ, Kuller, LH, Brenner, H, Gillum, RF, Meisinger, C, Ford, I, Knuiman, MW, Rosengren, A, Lawlor, DA, Völzke, H, Cooper, C, Marín Ibañez, A, Casiglia, E, Kauhanen, J, Cooper, JA, Rodriguez, B, Sundström, J ; https://orcid.org/0000-0003-2247-8454, Barrett-Connor, E, Dankner, R, Nietert, PJ, Davidson, KW, Wallace, RB, Blazer, DG, Björkelund, C, Donfrancesco, C, Krumholz, HM, Nissinen, A, Davis, BR, Coady, S, Whincup, PH, Jørgensen, T, Ducimetiere, P, Trevisan, M, Engström, G, Crespo, CJ, Meade, TW, Visser, M, Kromhout, D, Kiechl, S, Daimon, M, Price, JF, Gómez De La Cámara, A, Wouter Jukema, J, Lamarche, B, Onat, A, Simons, LA ; https://orcid.org/0000-0001-8082-6416, Kavousi, M, Ben-Shlomo, Y, Gallacher, J, Dekker, JM, Arima, H, Shara, N, Tipping, RW, Roussel, R, Brunner, EJ, Koenig, W, Sakurai, M, Pavlovic, J, Gansevoort, RT, Nagel, D, Goldbourt, U, Barr, ELM, Palmieri, L, Njølstad, I, Sato, S, Monique Verschuren, WM, Varghese, CV, Graham, I, Onuma, O, Greenland, P, Woodward, M ; https://orcid.org/0000-0001-9800-5296, Ezzati, M, Psaty, BM, Sattar, N, Jackson, R, Ridker, PM, Cook, NR, D'Agostino, RB, Thompson, SG, Danesh, J, and Di Angelantonio, E

Abstract

Aims There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods and results Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. Conclusion Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

Published
2019

24. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

Author

Kaptoge, S, Pennells, L, De Bacquer, D, Cooney, MT, Kavousi, M, Stevens, G, Riley, LM, Savin, S, Khan, T, Altay, S, Amouyel, P, Assmann, G, Bell, S, Ben-Shlomo, Y, Berkman, L, Beulens, JW, Björkelund, C, Blaha, M, Blazer, DG, Bolton, T, Bonita Beaglehole, R, Brenner, H, Brunner, EJ, Casiglia, E, Chamnan, P, Choi, YH, Chowdry, R, Coady, S, Crespo, CJ, Cushman, M, Dagenais, GR, D'Agostino, RB, Daimon, M, Davidson, KW, Engström, G, Ford, I, Gallacher, J, Gansevoort, RT, Gaziano, TA, Giampaoli, S, Grandits, G, Grimsgaard, S, Grobbee, DE, Gudnason, V, Guo, Q, Tolonen, H, Humphries, S, Iso, H, Jukema, JW, Kauhanen, J, Kengne, AP, Khalili, D, Koenig, W, Kromhout, D, Krumholz, H, Lam, TH, Laughlin, G, Marín Ibañez, A, Meade, TW, Moons, KGM, Nietert, PJ, Ninomiya, T, Nordestgaard, BG, O'Donnell, C, Palmieri, L, Patel, A ; https://orcid.org/0000-0003-3825-4092, Perel, P, Price, JF, Providencia, R, Ridker, PM, Rodriguez, B, Rosengren, A, Roussel, R, Sakurai, M, Salomaa, V, Sato, S, Schöttker, B, Shara, N, Shaw, JE, Shin, HC, Simons, LA ; https://orcid.org/0000-0001-8082-6416, Sofianopoulou, E, Sundström, J ; https://orcid.org/0000-0003-2247-8454, Völzke, H, Wallace, RB, Wareham, NJ, Willeit, P, Wood, D, Wood, A, Zhao, D, Woodward, M ; https://orcid.org/0000-0001-9800-5296, Danaei, G, Roth, G, Mendis, S, Onuma, O, Varghese, C, Ezzati, M, Graham, I, Jackson, R, Danesh, J, Kaptoge, S, Pennells, L, De Bacquer, D, Cooney, MT, Kavousi, M, Stevens, G, Riley, LM, Savin, S, Khan, T, Altay, S, Amouyel, P, Assmann, G, Bell, S, Ben-Shlomo, Y, Berkman, L, Beulens, JW, Björkelund, C, Blaha, M, Blazer, DG, Bolton, T, Bonita Beaglehole, R, Brenner, H, Brunner, EJ, Casiglia, E, Chamnan, P, Choi, YH, Chowdry, R, Coady, S, Crespo, CJ, Cushman, M, Dagenais, GR, D'Agostino, RB, Daimon, M, Davidson, KW, Engström, G, Ford, I, Gallacher, J, Gansevoort, RT, Gaziano, TA, Giampaoli, S, Grandits, G, Grimsgaard, S, Grobbee, DE, Gudnason, V, Guo, Q, Tolonen, H, Humphries, S, Iso, H, Jukema, JW, Kauhanen, J, Kengne, AP, Khalili, D, Koenig, W, Kromhout, D, Krumholz, H, Lam, TH, Laughlin, G, Marín Ibañez, A, Meade, TW, Moons, KGM, Nietert, PJ, Ninomiya, T, Nordestgaard, BG, O'Donnell, C, Palmieri, L, Patel, A ; https://orcid.org/0000-0003-3825-4092, Perel, P, Price, JF, Providencia, R, Ridker, PM, Rodriguez, B, Rosengren, A, Roussel, R, Sakurai, M, Salomaa, V, Sato, S, Schöttker, B, Shara, N, Shaw, JE, Shin, HC, Simons, LA ; https://orcid.org/0000-0001-8082-6416, Sofianopoulou, E, Sundström, J ; https://orcid.org/0000-0003-2247-8454, Völzke, H, Wallace, RB, Wareham, NJ, Willeit, P, Wood, D, Wood, A, Zhao, D, Woodward, M ; https://orcid.org/0000-0001-9800-5296, Danaei, G, Roth, G, Mendis, S, Onuma, O, Varghese, C, Ezzati, M, Graham, I, Jackson, R, and Danesh, J

Abstract

Background: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a

Published
2019

25. External validation of prognostic models for preeclampsia in a Dutch multicenter prospective cohort

Author

de Ruiter, M., Kwee, A. (Anneke), Naaktgeboren, CA, Louhanepessy, R.D., Groot, I.B. (Ingrid) de, Evers, IM, Groenendaal, F. (Floris), Hering, YR, Huisjes, A.J.M. (Anjoke), Kirpestein, C, Monincx, WM, Schielen, P., van t Zelfde, A., Van Oirschot, C.M. (Charlotte M.), Vankan-Buitelaar, SA, Vonk, M. (Marco), Wiegers, TA, Zwart, J.J. (Joost), Moons, KGM, Franx, A. (Arie), Koster, M.P.H., de Ruiter, M., Kwee, A. (Anneke), Naaktgeboren, CA, Louhanepessy, R.D., Groot, I.B. (Ingrid) de, Evers, IM, Groenendaal, F. (Floris), Hering, YR, Huisjes, A.J.M. (Anjoke), Kirpestein, C, Monincx, WM, Schielen, P., van t Zelfde, A., Van Oirschot, C.M. (Charlotte M.), Vankan-Buitelaar, SA, Vonk, M. (Marco), Wiegers, TA, Zwart, J.J. (Joost), Moons, KGM, Franx, A. (Arie), and Koster, M.P.H.

Abstract

Objective: To perform an external validation of all published prognostic models for first-trimester prediction of the risk of developing preeclampsia (PE). Methods: Women <14 weeks of pregnancy were recruited in the Netherlands. All systematically identified prognostic models for PE that contained predictors commonly available were eligible for external validation. Results: 3,736 women were included; 87 (2.3%) developed PE. Calibration was poor due to overestimation. Discrimination of 9 models for LO-PE ranged from 0.58 to 0.71 and of 9 models for all PE from 0.55 to 0.75. Conclusion: Only a few easily applicable prognostic models for all PE showed discrimination above 0.70, which is considered an acceptable performance.

Published
2019
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26. Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

Author

Smith, T, Muller, DC, Moons, KGM, Cross, AJ, Johansson, M, Ferrari, P, Fagherazzi, G, Peeters, PHM, Severi, G, Huesing, A, Kaaks, R, Tjonneland, A, Olsen, A, Overvad, K, Bonet, C, Rodriguez-Barranco, M, Huerta, JM, Gurrea, AB, Bradbury, KE, Trichopoulou, A, Bamia, C, Orfanos, P, Palli, D, Pala, V, Vineis, P, Bueno-de-Mesquita, B, Ohlsson, B, Harlid, S, Van Guelpen, B, Skeie, G, Weiderpass, E, Jenab, M, Murphy, N, Riboli, E, Gunter, MJ, Aleksandrova, KJ, Tzoulaki, I, Smith, T, Muller, DC, Moons, KGM, Cross, AJ, Johansson, M, Ferrari, P, Fagherazzi, G, Peeters, PHM, Severi, G, Huesing, A, Kaaks, R, Tjonneland, A, Olsen, A, Overvad, K, Bonet, C, Rodriguez-Barranco, M, Huerta, JM, Gurrea, AB, Bradbury, KE, Trichopoulou, A, Bamia, C, Orfanos, P, Palli, D, Pala, V, Vineis, P, Bueno-de-Mesquita, B, Ohlsson, B, Harlid, S, Van Guelpen, B, Skeie, G, Weiderpass, E, Jenab, M, Murphy, N, Riboli, E, Gunter, MJ, Aleksandrova, KJ, and Tzoulaki, I

Abstract

OBJECTIVE: To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts. DESIGN: Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability). RESULTS: The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC. CONCLUSION: Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

Published
2019

27. Evaluation of prediction models and diagnostic tests

Author

Epi Methoden Team 5, Moons, KGM, Naaktgeboren, Christa, Hooft, Lotty, Jenniskens, Kevin, Epi Methoden Team 5, Moons, KGM, Naaktgeboren, Christa, Hooft, Lotty, and Jenniskens, Kevin

Published
2019

29. External validation of prognostic models for preeclampsia in a Dutch multicenter prospective cohort

Author

Ruiter, Margina, Kwee, A, Naaktgeboren, CA, Louhanepessy, RD, Groot, IB, Evers, IM, Groenendaal, F, Hering, YR, Huisjes, AJM, Kirpestein, C, Monincx, WM, Schielen, P, Zelfde, A, van Oirschot, CM, Vankan-Buitelaar, SA, Vonk, M, Wiegers, TA, Zwart, JJ, Moons, KGM, Franx, A, Koster, Wendy, Ruiter, Margina, Kwee, A, Naaktgeboren, CA, Louhanepessy, RD, Groot, IB, Evers, IM, Groenendaal, F, Hering, YR, Huisjes, AJM, Kirpestein, C, Monincx, WM, Schielen, P, Zelfde, A, van Oirschot, CM, Vankan-Buitelaar, SA, Vonk, M, Wiegers, TA, Zwart, JJ, Moons, KGM, Franx, A, and Koster, Wendy

Published
2019

30. Erratum to:Methods for evaluating medical tests and biomarkers

Author

Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, De Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, Di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, De Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, De Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, Van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, Van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, De Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, De Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, De Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, Van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, De Bono, J, CTC-STOP protocol development group, and National Institute for Health Research

Subjects
medicine.medical_specialty, Astrophysics::High Energy Astrophysical Phenomena, MEDLINE, 030204 cardiovascular system & hematology, BTC (Bristol Trials Centre), MASTERMIND consortium, 03 medical and health sciences, 0302 clinical medicine, medicine, 030212 general & internal medicine, Intensive care medicine, CTC-STOP protocol development group, lcsh:R5-920, business.industry, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Published Erratum, STREAMLINE COLON Investigators, 3. Good health, STREAMLINE LUNG Investigators, Centre for Surgical Research, Family medicine, METRIC Investigators, High Energy Physics::Experiment, Erratum, business, lcsh:Medicine (General)
Abstract

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

Published
2017
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31. Prognostic research in treated populations

Author

Epi Methoden Team 3, Other research (not in main researchprogram), Moons, KGM, Slingerland-Peelen, Linda M., Groenwold, RHH, Pajouheshnia, Romin, Epi Methoden Team 3, Other research (not in main researchprogram), Moons, KGM, Slingerland-Peelen, Linda M., Groenwold, RHH, and Pajouheshnia, Romin

Published
2018

34. Methods for Evaluating Medical Tests and Biomarkers

Author

Pajouheshnia, R, Groenwold, RHH, Moons, KGM, Reitsma, JB, and Peelen, LM

Abstract

Background and objectives: Prognostic models are, among other things, used to provide risk predictions for individuals who are not receiving a certain treatment, in order to assess the natural course of a disease and in turn guide treatment decisions. As treatment availability and use changes over time, researchers may be faced with assessing the validity of existing models in partially treated populations, and may account for treatment use in different ways. We aimed to investigate how treatment use contributes to the poor performance commonly observed in external validation studies, and to explore methods to address the issue. Methods: The effect of treatment use on the observed performance of a model was evaluated analytically. Development data sets representing untreated individuals were simulated using a logistic model and “optimal” models were developed using those sets. Validation sets drawn from the same theoretical population were simulated to receive an effective (binary) treatment. The prevalence and effectiveness of treatment were varied, with and without being dependent on true risk. Model performance in the validation sets was expressed in terms of calibration slope, observed:expected ratio (O:E) and C statistic. We examined the results of i) ignoring treatment, ii) restricting validation to untreated patients, and iii) adjusting the observed event rates to account for treatment effects. This was expressed through the difference (Δ) between each performance measure after applying a method and the value observed in the untreated set. Results: Validation of a model derived in untreated individuals in a treated validation set resulted in poorer model performance than that observed in the same population, if left untreated. Treatment of 50% of patients with a highly effective treatment (higher risk patients had a higher probability of receiving treatment; treatment effect odds ratio: 0.5), resulted in a decrease in the O:E from 1.0 to 0.7, and a decrease in the C statistic from 0.67 to 0.62 when compared to the observed statistics in an untreated set. This trend was observed across settings with different mechanisms for treatment allocation and different population risk distributions. As treatment prevalence and effectiveness increased, the observed model performance almost invariably decreased. Restricting the validation to only untreated individuals resulted in performance measures closer to those observed in the full untreated validation set, at the cost of precision (Δ O:E = 0.0; C statistic = 0.03). When treatment allocation was completely based on risk (I.e. according to a risk threshold), the restriction approach was less effective (ΔO:E = 0.0; ΔC statistic = 0.07). Increasing the observed event rates to account for treatment effects improved the observed model calibration, but this was highly sensitive to incorrect assumptions about treatment use and effectiveness. Conclusions: Validating a model designed to make predictions of the “natural course” of an individual’s health in a validation data set containing treated individuals may result in an underestimation of the performance of the model in untreated individuals. Current methods are not sufficient to account for the effects of treatment, and findings from such studies should be interpreted with caution.

Published
2017

35. Reporting of clinical prediction model studies in journal and conference abstracts: TRIPOD for Abstracts

Author

Heus, P, Hooft, L, Reitsma, JB, Scholten, RJ, Altman, Douglas G, Collins, Gary S., and Moons, KGM

Abstract

Background: Informative titles and abstracts are important for the identification of potentially relevant studies and communication of research results. Many readers and reviewers base their decision to read the full text of a publication on clarity and detail presented in the title and abstract. Clear and informative reporting in title and abstract is therefore essential. The TRIPOD Statement, published in 2015, is a guideline for Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis. TRIPOD provides general recommendations for the reporting of title and abstracts, however, more detailed guidance is desirable. Objectives: To develop specific guidance for informative reporting of diagnostic or prognostic prediction model studies in both journal and conference abstracts. Methods: We conducted a literature review on the reporting of prediction model studies and established a list of potentially relevant items to report in abstracts. This list served as the basis for a modified Delphi procedure. In the first round a panel of 110 experts in the field of prediction modeling studies were asked to rate to what extent each candidate item is essential. A maximum of two Delphi rounds will be carried out to reach consensus whether to include an item and to provide insight into potential wording. Results: Preliminary analyses from our literature review showed that objectives, setting, participants, sample size, outcome and conclusions were reported in over 75% of 134 abstracts. Candidate predictors, internal validation technique and results for calibration were addressed in fewer than 25% of abstracts. The modified Delphi procedure is currently being carried out. We will present the results of this procedure and the guidance resulting from it. Conclusions: We present the development of a specific checklist and corresponding guidance for the reporting of diagnostic or prognostic prediction model studies in both journal and conference abstracts: TRIPOD for Abstracts. The guidance will be applicable to abstracts of publications that describe development or external validation of a prediction model.

Published
2016

37. More than half of the TRIPOD items are inadequately reported in prediction modelling studies

Author

Heus, P, Damen, JAAG, Pajouheshnia, R, Scholten, RJ, Reitsma, JB, Collins, Gary S., Altman, Douglas G, Moons, KGM, and Hooft, Lotty

Abstract

Background: Prediction models, both diagnostic and prognostic, are developed with the aim to guide clinical decision making. To validate, evaluate their impact and eventually use these models in clinical practice, clear and comprehensive reporting of prediction modelling studies is required. To improve the reporting of prediction models, a guideline for Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was launched in January 2015. The TRIPOD statement is a checklist of 22 main items considered essential for informative reporting of studies developing or validating multivariable prediction models. Objectives: To assess the quality of reporting of prediction modelling studies that were published before the launch of TRIPOD in 2015. Methods: We selected the 10 journals with the highest impact factors within 37 clinical domains. A PubMed search was performed to identify prediction models published in May 2014. Publications that described the development and/or validation of a diagnostic or prognostic prediction model were considered eligible. We also included studies evaluating the incremental value of adding a predictor to a model. TRIPOD items were translated into a data extraction form, which was piloted extensively. Three reviewers extracted data. If they disagreed on when to consider an item “adhered”, it was discussed in consensus meetings with the other co-authors. Results: Our search identified 4871 references, of which 347 potentially eligible references were assessed in full text. Eventually 148 references (within 28 clinical domains) met our eligibility criteria. Of these, 17% described diagnostic and 83% prognostic prediction models. Model development was described in 43% of the publications, validation of an existing prediction model in 26%, incremental value of adding a predictor to a model in 19% and a combination of development and validation of a model was described in 12%. The analysis showed that overall a mean of 48.4% of the TRIPOD items (on publication level) was adhered (range 20.7%-72.4%). The mean adherence was 46.5%, 51.4%, 47.1% and 48.5% in publications about development, validation, incremental value and combination of development and validation, respectively. There was incomplete reporting of TRIPOD items concerning title and abstract, blinding, model building procedures, final model and performance measures. Source of data, eligibility criteria, study limitations and overall interpretation were adequately reported in the majority of publications. Conclusions: There is room for improvement in the reporting of multivariable prediction models: more than half of the TRIPOD items are currently not or inadequately reported. Our study could serve as a baseline measurement for future research evaluating the impact of the introduction of the TRIPOD statement.

Published
2016

38. ROBIS : A new tool to assess risk of bias in systematic reviews was developed

Author

Whiting, Penny, Savović, Jelena, Higgins, Julian P T, Caldwell, Deborah M, Reeves, Barnaby C, Shea, Beverley, Davies, Philippa, Kleijnen, Jos, Churchill, Rachel, ROBIS group, and Moons, KGM

Subjects
Meta-analysis, Research Support, Non-U.S. Gov't, Systematic review, Journal Article, Tool, Quality, Risk of bias, Evidence
Abstract

OBJECTIVE: To develop ROBIS, a new tool for assessing the risk of bias in systematic reviews (rather than in primary studies). STUDY DESIGN AND SETTING: We used four-stage approach to develop ROBIS: define the scope, review the evidence base, hold a face-to-face meeting, and refine the tool through piloting. RESULTS: ROBIS is currently aimed at four broad categories of reviews mainly within health care settings: interventions, diagnosis, prognosis, and etiology. The target audience of ROBIS is primarily guideline developers, authors of overviews of systematic reviews ("reviews of reviews"), and review authors who might want to assess or avoid risk of bias in their reviews. The tool is completed in three phases: (1) assess relevance (optional), (2) identify concerns with the review process, and (3) judge risk of bias. Phase 2 covers four domains through which bias may be introduced into a systematic review: study eligibility criteria; identification and selection of studies; data collection and study appraisal; and synthesis and findings. Phase 3 assesses the overall risk of bias in the interpretation of review findings and whether this considered limitations identified in any of the phase 2 domains. Signaling questions are included to help judge concerns with the review process (phase 2) and the overall risk of bias in the review (phase 3); these questions flag aspects of review design related to the potential for bias and aim to help assessors judge risk of bias in the review process, results, and conclusions. CONCLUSIONS: ROBIS is the first rigorously developed tool designed specifically to assess the risk of bias in systematic reviews.

Published
2016

39. Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP) : a prospective cohort study

Author

Thangaratinam, Shakila, Allotey, John, Marlin, Nadine, Mol, Ben W., von Dadelszen, Peter, Ganzevoort, Wessel, Akkermans, Joost, Ahmed, Asif, Daniels, Jane, Deeks, Jon, Ismail, Khaled, Barnard, Ann Marie, Dodds, Julie, Kerry, Sally, Riley, Richard D., Khan, Khalid S, Moons, KGM, Thangaratinam, Shakila, Allotey, John, Marlin, Nadine, Mol, Ben W., von Dadelszen, Peter, Ganzevoort, Wessel, Akkermans, Joost, Ahmed, Asif, Daniels, Jane, Deeks, Jon, Ismail, Khaled, Barnard, Ann Marie, Dodds, Julie, Kerry, Sally, Riley, Richard D., Khan, Khalid S, and Moons, KGM

Published
2017

42. Erratum to: Methods for evaluating medical tests and biomarkers.

Author

Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, de Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Di Ruffano, LF, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, de Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, de Bono, J, CTC-STOP protocol development group, Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, de Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Di Ruffano, LF, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, de Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, de Bono, J, and CTC-STOP protocol development group

Abstract

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

Published
2017

44. Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study

Author

Epi Methoden, Child Health, Cancer, Circulatory Health, JC onderzoeksprogramma Methodologie, Thangaratinam, Shakila, Allotey, John, Marlin, Nadine, Mol, Ben W., von Dadelszen, Peter, Ganzevoort, Wessel, Akkermans, Joost, Ahmed, Asif, Daniels, Jane, Deeks, Jon, Ismail, Khaled, Barnard, Ann Marie, Dodds, Julie, Kerry, Sally, Riley, Richard D., Khan, Khalid S, Moons, KGM, Epi Methoden, Child Health, Cancer, Circulatory Health, JC onderzoeksprogramma Methodologie, Thangaratinam, Shakila, Allotey, John, Marlin, Nadine, Mol, Ben W., von Dadelszen, Peter, Ganzevoort, Wessel, Akkermans, Joost, Ahmed, Asif, Daniels, Jane, Deeks, Jon, Ismail, Khaled, Barnard, Ann Marie, Dodds, Julie, Kerry, Sally, Riley, Richard D., Khan, Khalid S, and Moons, KGM

Published
2017

45. Prediction models for the risk of gestational diabetes: a systematic review

Author

Eerstelijnsverloskundigen, Circulatory Health, MS Verloskunde, Other research (not in main researchprogram), Epi Methoden Team 1, JC onderzoeksprogramma Methodologie, DV&B-MT-Medisch, Brain, Child Health, Epi Methoden, Cancer, Lamain-de Ruiter, Marije, Kwee, Anneke, Naaktgeboren, Christiana, Franx, A, Moons, KGM, Koster, Maria, Eerstelijnsverloskundigen, Circulatory Health, MS Verloskunde, Other research (not in main researchprogram), Epi Methoden Team 1, JC onderzoeksprogramma Methodologie, DV&B-MT-Medisch, Brain, Child Health, Epi Methoden, Cancer, Lamain-de Ruiter, Marije, Kwee, Anneke, Naaktgeboren, Christiana, Franx, A, Moons, KGM, and Koster, Maria

Published
2017

46. The effects of misclassification in routine healthcare databases on the accuracy of prognostic prediction models: a case study of the CHA2DS2-VASc score in atrial fibrillation

Author

HAG Hart- Vaatziekten, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Epi Methoden Team 1, Epi Methoden, JC onderzoeksprogramma Methodologie, Epidemiology & Health Economics, van Doorn, S, Brakenhoff, TB, Moons, KGM, Rutten, Frans H, Hoes, Arno W., Groenwold, RHH, Geersing, Geert Jan, HAG Hart- Vaatziekten, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Epi Methoden Team 1, Epi Methoden, JC onderzoeksprogramma Methodologie, Epidemiology & Health Economics, van Doorn, S, Brakenhoff, TB, Moons, KGM, Rutten, Frans H, Hoes, Arno W., Groenwold, RHH, and Geersing, Geert Jan

Published
2017

47. What to expect when you're expecting?

Author

Eerstelijnsverloskundigen, Circulatory Health, Franx, Arie, Moons, KGM, Koster, M.P.H. (Wendy), Kwee, Anneke, Lamain-de Ruiter, Marije, Eerstelijnsverloskundigen, Circulatory Health, Franx, Arie, Moons, KGM, Koster, M.P.H. (Wendy), Kwee, Anneke, and Lamain-de Ruiter, Marije

Published
2017

48. Methods for Evaluating Medical Tests and Biomarkers

Author

Epi Methoden Team 3, Epi Methoden Team 1, Epi Methoden, Epi Methoden Team 4, Pajouheshnia, R, Groenwold, RHH, Moons, KGM, Reitsma, JB, Peelen, LM, Epi Methoden Team 3, Epi Methoden Team 1, Epi Methoden, Epi Methoden Team 4, Pajouheshnia, R, Groenwold, RHH, Moons, KGM, Reitsma, JB, and Peelen, LM

Published
2017

49. A generic nomogram for multinomial prediction models: theory and guidance for construction

Author

Epi Methoden Team 2, Circulatory Health, Biostatistiek Onderzoek, Epi Methoden, Cancer, Child Health, JC onderzoeksprogramma Methodologie, Epi Methoden Team 4, van Smeden, M, de Groot, JAH, Nikolakopoulos, S, Bertens, LCM, Moons, KGM, Reitsma, JB, Epi Methoden Team 2, Circulatory Health, Biostatistiek Onderzoek, Epi Methoden, Cancer, Child Health, JC onderzoeksprogramma Methodologie, Epi Methoden Team 4, van Smeden, M, de Groot, JAH, Nikolakopoulos, S, Bertens, LCM, Moons, KGM, and Reitsma, JB

Published
2017

50. Cardiovascular RiskprofilE - IMaging and gender-specific disOrders (CREw-IMAGO): rationale and design of a multicenter cohort study

Author

Zoet, GA, Meun, Cindy, Benschop, L, Boersma, Eric, Budde, Ricardo, Fauser, B, de Groot, CJM, van der Lugt, Aad, Maas, AIR, Moons, KGM, Roeters van Lennep, Jeanine, Roos - Hesselink, Jolien, Steegers, Eric, Rijn, BB, Laven, Joop, Franx, A (Arie), Velthuis, BK, Zoet, GA, Meun, Cindy, Benschop, L, Boersma, Eric, Budde, Ricardo, Fauser, B, de Groot, CJM, van der Lugt, Aad, Maas, AIR, Moons, KGM, Roeters van Lennep, Jeanine, Roos - Hesselink, Jolien, Steegers, Eric, Rijn, BB, Laven, Joop, Franx, A (Arie), and Velthuis, BK

Published
2017

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