Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Date: Thursday, 10/Dec/2020
2:00pm
-
3:05pm
Opening & Keynote David Kent
Chair: Ben Van Calster
 
2:00pm - 2:30pm

Using Group Data for Individual Patients: The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement

David M. Kent1, David van Klaveren1,2, Jessica K. Paulus1, Ewout Steyerberg3

1: Predictive Analytics and Comparative Effectiveness Center, Tufts Medical Center/Tufts University School of Medicine, United States of America; 2: Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands; 3: Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands



2:30pm - 2:40pm

Estimating heterogeneity of treatment effect by risk modeling

Ewout Steyerberg1,3, David Kent2, David van Klaveren2,3

1: LUMC, Leiden, The Netherlands; 2: Tufts University, Boston; 3: Erasmus MC - Rotterdam



2:40pm - 2:50pm

Application of the PATH Statement: Predicting Treatment Benefit of Heart Bypass Surgery versus Coronary Stenting

David van Klaveren1,2, Kuniaki Takahashi3, Ewout W Steyerberg1,4, David M Kent2, Patrick W Serruys5

1: Erasmus University Medical Center, Rotterdam, The Netherlands; 2: Tufts Medical Center, Boston, USA; 3: Academic Medical Center, University of Amsterdam, The Netherlands; 4: Leiden University Medical Center, Leiden, The Netherlands; 5: National University of Ireland, Galway, Ireland

3:05pm
-
3:35pm
Linked contributed talks on predicting treatment response
Chair: Ben Van Calster
 
3:05pm - 3:20pm

Individual participant data meta-analysis to examine treatment-covariate interactions: statistical recommendations for conduct and planning

Richard D Riley1, Thomas Debray2, David Fisher3, Miriam Hattle1, Nadine Marlin4, Jeroen Hoogland2, Francois Gueyffier5, Jan A Staessen6, Jiguang Wang7, Karel GM Moons2, Johannes B Reitsma2, Joie Ensor1

1: Keele University, UK; 2: University Medical Center Utrecht, Utrecht; 3: University College London, UK; 4: Queen Mary University of London, London, UK; 5: Inserm, France; 6: KU Leuven, Belgium; 7: Shanghai Jiaotong University, China



3:20pm - 3:35pm

Test and Treat Superiority Plot: estimating threshold performance for developers of tests for treatment response

Neil Hawkins1, Andrew Briggs2, Janet Bouttell1, Dmitry Pomonomarev3

1: University of Glasgow, United Kingdom; 2: London School of Hygiene and Tropical Medicine; 3: Meshalkin National Medical Research Centre, Novosibirsk, Russian FederationTest and Treat Superiority Plot: developing a simple tool to estimate required test performance for developers of tests for treatment response

3:35pm
-
3:45pm
Short break & Poster viewing
3:45pm
-
5:30pm
Contributed session on diagnostic tests
Chair: Nandini Dendukuri
 
3:45pm - 4:00pm

Unblinded sample size re-estimation for diagnostic accuracy studies

Antonia Zapf1, Annika Hoyer2

1: University Medical Center Hamburg-Eppendorf, Germany; 2: Ludwig-Maximilians-University Munich, Germany



4:00pm - 4:15pm

An alternative method for presenting risk of bias assessments in systematic review of accuracy studies

Yasaman Vali, Jenny Lee, Patrick M. Bossuyt, Mohammad Hadi Zafarmand

Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Center, Amsterdam, The Netherlands



4:15pm - 4:30pm

Major depression classification based on different diagnostic interviews: A synthesis of individual participant data meta-analyses

Yin Wu1,2,3, Brooke Levis1,2,4, John P. A. Ioannidis5, Andrea Benedetti2,6, Brett D Thombs1,2,3, DEPRESsion Screening Data (DEPRESSD) Collaboration7

1: Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; 2: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada; 3: Department of Psychiatry, McGill University, Montréal, Québec, Canada; 4: Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK; 5: Departments of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics, Stanford University, Stanford, California, USA; 6: Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montréal, Québec, Canada; 7: McGill University, Montréal, Québec, Canada



4:30pm - 4:45pm

What makes a good cancer biomarker? Developing a consensus

Katerina-Vanessa Savva

Imperial College London, United Kingdom



4:45pm - 5:00pm

Developing Target Product Profiles for medical tests: a methodology review

Paola Cocco1, Anam Ayaz-Shah2, Michael Paul Messenger3,4, Robert Michael West5, Bethany Shinkins1,4

1: Test Evaluation Group, Academic Unit of Health Economics, University of Leeds, Leeds, UK; 2: Academic Unit of Primary Care, Leeds Institute for Health Sciences, University of Leeds, Leeds, UK; 3: Centre for Personalised Health and Medicine, University of Leeds, Leeds, UK; 4: NIHR Leeds In Vitro Diagnostic (IVD) Co-operative, Leeds, UK; 5: Leeds Institute for Health Sciences, University of Leeds, Leeds, UK



5:00pm - 5:15pm

Nonparametric Limits of Agreement for small to moderate sample sizes - a simulation study

Maria Elisabeth Frey1, Hans Christian Petersen2, Oke Gerke2,3

1: Charles River Laboratories Copenhagen A/S, Lille Skensved, Denmark; 2: University of Southern Denmark, Odense, Denmark; 3: Odense University Hospital, Odense, Denmark



5:15pm - 5:30pm

QUADAS-C: a tool for assessing risk of bias in comparative diagnostic accuracy studies

Bada Yang1, Penny Whiting2, Clare Davenport3,4, Jonathan Deeks3,4, Christopher Hyde5, Susan Mallett6, Yemisi Takwoingi3,4, Mariska Leeflang1

1: Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, The Netherlands; 2: Population Health Sciences, Bristol Medical School, UK; 3: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, UK; 4: NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, UK; 5: Exeter Test Group, Institute of Health Research, College of Medicine and Health, University of Exeter, UK; 6: UCL Centre for Medical Imaging, University College London, UK

Contributed session on prediction models
Chair: Laure Wynants
 
3:45pm - 4:00pm

Minimum sample size for external validation of a clinical prediction model with a continuous outcome

Lucinda Archer1, Kym Snell1, Joie Ensor1, Mohammed Hudda2, Gary Collins3, Richard Riley1

1: Centre for Prognosis Research, School of Medicine. Keele University, Staffordshire. ST5 5BG; 2: Population Health Research Institute, St George’s, University of London, London, UK. SW17 0RE; 3: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK. OX3 7LD



4:00pm - 4:15pm

A systematic review of clinical prediction models developed using machine learning methods in Oncology

Paula Dhiman1, Jie Ma1, Benjamin Speich1, Garrett Bullock2, Constanza Andaur-Navarro3, Shona Kirtley1, Ben Van Calster4, Richard Riley5, Karel Moons3, Gary Collins1

1: Centre for Statistics in Medicine, University of Oxford, Oxford, UK; 2: Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 3: Julius Center, UMC Utrecht, Utrecht University, Utrecht, The Netherlands; 4: Department of Development and Regeneration, KU Leuven, Leuven, Belgium; 5: School of Primary, Community and Social Care, University of Keele, Keele, UK



4:15pm - 4:30pm

Causal interpretation of clinical prediction models: When, why and how

Matthew Sperrin, Lijing Lin, David Jenkins, Niels Peek

Health e-Research Centre, Division of Informatics, Imaging and Data Science, University of Manchester



4:30pm - 4:45pm

Risk prediction with discrete ordinal outcomes; calibration and the impact of the proportional odds assumption

Michael Edlinger1,2, Maarten van Smeden3,4, Hannes F Alber5,6, Ewout W Steyerberg4, Ben Van Calster1,4

1: KU Leuven, Belgium; 2: Medical University Innsbruck, Austria; 3: University Medical Centre Utrecht, the Netherlands; 4: Leiden University Medical Centre, the Netherlands; 5: Klinikum Klagenfurt, Austria; 6: Rehabilitation Centre Münster, Austria



4:45pm - 5:00pm

Recovering the full equation of an incompletely reported logistic regression model

Toshihiko Takada, Chris van Lieshout, Jeroen Hoogland, Ewoud Schuit, Johanes B Reitsma

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht



5:00pm - 5:15pm

AI phone apps for skin cancer: Reviewing the evidence, regulations, marketing, plus what happened next

Jon Deeks1,2, Jac Dinnes1,2, Karoline Freeman1,3, Naomi Chuchu1,4, Sue Bayliss1, Rubeta Matin5, Abhilash Jain6,7, Yemisi Takwongi1,2, Fiona Walter8, Hywel Williams9

1: Test Evaluation Research Group, University of Birmingham, United Kingdom; 2: NIHR Biomedical Research Centre, University Hospitals NHS Foundation Trust and University of Birmingham, Birmingham UK; 3: Warwick Medical School, University of Warwick, Coventry, UK; 4: London School of Hygiene and Tropical Medicine, London UK; 5: Department of Dermatology, Churchill Hospital, Oxford UK; 6: Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford UK; 7: Department of Plastic and Reconstructive Surgery, Imperial College Healthcare NHS Trust, St Mary's Hospital, London UK; 8: Department of Public Health and Primary Care, University of Cambridge, Cambridge UK; 9: Centre for Evidence Based Dermatology, University of Nottingham, Nottingham UK.



5:15pm - 5:30pm

TRIPOD-CLUSTER: reporting of prediction model studies in IPD-MA, EHR and other clustered datasets

Thomas Debray1, Gary Collins4, Richard Riley2, Kym Snell2, Ben van Calster3, Doug Altman4, Johannes Reitsma1, Karel Moons1

1: University Medical Center Utrecht, the Netherlands; 2: Keele University, Keele, the United Kingdom; 3: K U Leuven, Leuven, Belgium; 4: University of Oxford, Oxford, the United Kingdom

Poster viewing
 

Predicting pre-eclampsia in nulliparous women using routinely-collected maternal characteristics: A model development and validation study

Ziad TA Al-Rubaie1, H Malcolm Hudson2, Gregory Jenkins3, Imad Mahmoud4, Joel G Ray5, Lisa M Askie2, Sarah J Lord1,2

1: School of Medicine, The University of Notre Dame Australia, Australia; 2: NHMRC Clinical Trial Centre, University of Sydney, Australia; 3: Department of Obstetrics, Westmead Hospital, Australia; 4: Department of Obstetrics, Auburn and Mount-Druitt and Blacktown Hospitals, Australia; 5: Department of Medicine, St. Michael’s Hospital, Canada



Why are Machine Learning-based prediction models still unpopular in clinical practice?

Constanza L Andaur Navarro1,2, Johanna Damen1,2, Toshihiko Takada1,2, Paula Dihman3, Jie Ma3, Gary S Collins3, Ram Bajpai4, Richard D Riley4, Lotty Hooft1,2, Karel G Moons1,2

1: Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, The Netherlands; 2: Cochrane Netherlands, UMC Utrecht, Utrecht University, The Netherlands; 3: Center for Statistics in Medicine, University of Oxford, United Kingdom; 4: School of Primary, Community and Social Care, Keele University, United Kingdom



Development of prediction models using competing risk models in big healthcare databases

Constantinos Koshiaris1, Richard Stevens1, Richard Riley2, Sarah Lay-Flurrie1, Kym Snell2, Lucinda Archer2, James Sheppard1

1: University of Oxford, United Kingdom; 2: University of Keele, United Kingdom



Weighted variogram analyses for estimating within-patient variance components using routine data from biomarker monitoring programmes

Simon Baldwin1,2, Alice Sitch1,2, Yemisi Takwoingi1,2, Jonathan Deeks1,2

1: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; 2: National Institute for Health Research Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, UK



Bayesian Hierarchical Models for Personalized Health Care

Nicolas Banholzer, Stefan Feuerriegel

ETH Zurich, Switzerland



Dealing with multiple thresholds in diagnostic test accuracy meta-analysis: application of two modelling strategies

Hanne Ann Boon1, Thomas Struyf1, Dominique Bullens2,3, Ann Van den Bruel1,4, Jan Y Verbakel1,4

1: Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium; 2: Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium; 3: Clinical division of pediatrics, UZ Leuven, Leuven, Belgium; 4: Nuffield Department of Primary Care Health Sciences, University of Oxford, UK



Harnessing repeated measurements of predictor variables: A review of existing methods for clinical risk prediction

Lucy M. Bull1,2, Mark Lunt1, Glen P. Martin3, Kimme Hyrich4, Jamie C. Sergeant1,2

1: Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, UK; 2: Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PL, UK; 3: Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester M13 9PL, UK; 4: National Institute for Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Oxford Road, Manchester M13 9PL, UK



Adaptive sample size determination for the development of clinical prediction models

Evangelia Christodoulou1, Dirk Timmerman1,2, Maarten Van Smeden3, Ewout Steyerberg4, Ben Van Calster1,4

1: Department of Development & Regeneration, KU Leuven, Leuven, Belgium; 2: University Hospitals Leuven, Leuven, Belgium; 3: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands; 4: Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands



Propensity-based standardization to enhance the interpretation of predictive performance in individual participant data meta-analysis

Valentijn M.T. de Jong1, Jeroen Hoogland1, Karel G.M. Moons1,2, Tri-Long Nguyen1,3,4, Thomas P.A. Debray1,2

1: Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, The Netherlands; 2: Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands; 3: Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; 4: Department of Pharmacy, University Hospital Centre of Nîmes and University of Montpellier, Nîmes, France



Exploring test accuracy of faecal calprotectin for IBD using primary care electronic health records

Karoline Freeman1, Ronan Ryan2, Sian Taylor-Phillips1,3, Brian H. Willis3, Aileen Clarke1

1: University of Warwick, United Kingdom; 2: Devon, United Kingdom; 3: University of Birmingham, United Kingdom



Multiple screening tools, multiple thresholds, multiple clinical cohorts: Evaluating screening tools for obstructive sleep apnoea

Suzanne Freeman1, Emer Brady2, Helena Polmann3, Jessica Reus3, Graziela De Luca Canto3, Noelle Robertson4, Iain Squire5, Lizelle Bernhardt5,6

1: Department of Health Sciences, University of Leicester, Leicester, UK; 2: Diabetes Centre, University Hospitals of Leicester NHS Trust, Leicester, UK; 3: Brazilian Centre for Evidence-based Research/Centro Brasileiro de Pesquisas Baseadas em Evidências, Federal University of Santa Catarina, Florianopólis, Brazil; 4: Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK; 5: Department of Cardiovascular Sciences, University of Leicester, Leicester, UK; 6: Community Health Services, Leicestershire Partnership NHS Trust, Leicester, UK



Only fools rush in! – initial data analysis is required for developing and validating prediction models

Georg Heinze1, Mark Baillie2, Marianne Huebner3

1: Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems; Medical University of Vienna, Vienna, Austria; 2: Biostatistical Sciences and Pharmacometrics; Novartis Pharma AG, Basel, Switzerland; 3: Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA



Pertussis in Belgium - The challenge of using historical serial serological survey data

Sereina Annik Herzog1, Steven Abrams2,3, Amber Litzroth4, Isabelle Desombere5, Heidi Theeten1, Niel Hens1,2,3

1: Vaccine and Infectious Disease Institute, University of Antwerp, Belgium; 2: Department of Epidemiology and Social Medicine, University of Antwerp, Belgium; 3: Data Science Institute, Hasselt University, Belgium; 4: SD Epidemiology and Public Health, Sciensano, Brussels, Belgium; 5: SD Infectious Diseases in Humans, Sciensano, Brussels, Belgium



Diagnostic accuracy of C-reactive protein for appendicitis in primary care

G.A. Holtman1, G. Blok1, E. Nikkels1, J. van der Lei2, M.Y. Berger1

1: Department of General Practice and Elderly Care Medicine, University Medical Center Groningen, University of Groningen, PO Box 196, 9700 AD Groningen, the Netherlands; 2: Department of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands



Developing and validating a warfarin dose prediction model for patients in sub-Saharan Africa

Andrea L Jorgensen, Innocent G Asiimwe, Munir Pirmohamed

University of Liverpool, United Kingdom



Collinearity in prognostic models for dysphagia

Artuur Leeuwenberg, Ewoud Schuit, Johannes B. Reitsma, Karel G. Moons

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht



Evaluating risk of bias and applicability in meta-analyses of individual participant data

Brooke Levis1, Kym IE Snell1, Yemisi Takwoingi2, Gary S Collins3, Karel G Moons4, Johannes B Reitsma4, Lotty Hooft4, Richard D Riley1

1: Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK; 2: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; 3: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, UK; 4: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands



Selective Cutoff Reporting in Diagnostic Accuracy Studies of the PHQ-9 and EPDS Depression Screening Tools

Dipika Neupane1,2, Brooke Levis1,2,3, Parash Mani Bhandari1,2, Brett D Thombs1,2, Andrea Benedetti2,4, DEPRESsion Screening Data {DEPRESSD} Collaboration5

1: Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; 2: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada; 3: Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK; 4: Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montréal, Québec, Canada; 5: N/A



Estimating the prevalence of misdiagnosis of giant cell arteritis: using a genetic test as umpire

Charikleia Chatzigeorgiou1, Jennifer H Barrett1, Javier Martin2, Ann Wendy Morgan1, Sarah Louise Mackie1

1: University of Leeds, United Kingdom; 2: 3. Instituo di Parasitologia y Biomedicina, Consejo Superior de Investigaciones Cientificas (Spanish National Research Council), Madrid, Spain



Bayesian latent class analysis versus composite reference standards for estimating tuberculosis meningitis diagnostic test accuracy

Emily MacLean1,2, Mikashmi Kohli2, Nandini Dendukuri1,2

1: McGill University, Canada; 2: McGill International TB Centre



Measuring the impact of diagnostic tests on patient management decisions within three clinical trials

Sue Mallett, Stuart Taylor, Gauraang Bhatnagar, COLON STREAMLINE Investigators, LUNG STREAMLINE Investigators, METRIC Investigators

UCL Centre for Medical Imaging, University College London, Charles Bell House, 43-45 Foley Street, W1W 7TS



Ambiguous baseline definitions affect automated AKI diagnosis at emergency department

Michael S.A. Niemantsverdriet1,2, Meriem Khairoun3, Ayman El Idrissi3, Romy R Koopsen3, Imo E Hofer1, Wouter W Van Solinge1, Jan Willem Uffen3, Wouter M Tiel-Groenestege1, Karin A.H. Kaasjager3, Saskia Haitjema1

1: Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; 2: SkylineDx, Lichtenauerlaan 40, 3062 ME Rotterdam, The Netherlands; 3: Department of Internal Medicine, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands



Real-time handling of Missing Predictor Values when implementing and using prediction models in daily practice

Steven W J Nijman1, T Katrien J Groenhof1, Jeroen Hoogland1, Michiel L Bots1, Menno Brandjes2, John JL Jacobs2, Folkert W Asselbergs3,4,5, Karel GM Moons1, Thomas PA Debray1,4

1: Julius Center for Health Sciences and Primary Care, Utrecht University, Utrecht, The Netherlands; 2: LogiqCare, Ortec B.V. Zoetermeer, The Netherlands; 3: Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; 4: Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom; 5: Health Data Research UK, Institute of Health Informatics, University College London, London, United Kingdom



intelligent Liver Function Testing (iLFT): an algorithm-based pathway to increase diagnosis of liver disease

Jennifer Nobes1, Iain Macpherson1,2, Ellie Dow1, Ian Kennedy1, Michael Miller1, Elizabeth Furrie1, John Dillon2

1: NHS Tayside, United Kingdom; 2: University of Dundee, United Kingdom



Recommended labels for approaches to evaluate diagnostic accuracy: the STARD ReLabel project

Maria Olsen1, Bada Yang1, Patrick M. Bossuyt1, Chris Hyde2

1: Dept. of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, The Netherlands; 2: Exeter Test Group, Institute of Health and Research, College of Medicine and Health, University of Exeter, UK



Frequencies and patterns of microbiology test requests from general practice

Jose M. Ordonez-Mena1,2, Thomas R. Fanshawe1, Dona Foster3, Sarah Walker2, Gail Hayward1

1: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; 2: NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; 3: Nuffield Department of Medicine, University of Oxford, Oxford, UK



Patient and public involvement in methodological research: a case study

Laura Quinn1,2, Alice Sitch1,2, Jon Deeks1,2

1: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom; 2: NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, UK



A standardized framework for risk-based assessment of heterogeneity of treatment effect

Alexandros Rekkas1,2, Peter R. Rijnbeek1, David M. Kent3, Ewout W. Steyerberg1,2, David van Klaveren1,3

1: Erasmus Medical Center, Netherlands; 2: Leiden University Medical Center, Netherlands; 3: Tufts Medical Center, USA



A prognostic model for overall survival in sporadic Creutzfeldt-Jakob disease

Nicole Rübsamen1, Franc Llorens2,3,4, Peter Hermann4, Matthias Schmitz4, Anna Villar-Piqué2,3,4, Stefan Goebel4, André Karch1, Inga Zerr4,5

1: Institute for Epidemiology and Social Medicine, University of Münster, Domagkstraße 3, 48149 Münster, Germany; 2: Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Institute Carlos III, Campus Bellvitge, Feixa LLarga s/n, 08907 L’Hospitalet de Llobregat, Barcelona, Spain; 3: Bellvitge Biomedical Research Institute (IDIBELL), Avinguda de la Granvia de l’Hospitalet, 199, 08908 L’Hospitalet de Llobregat, Barcelona, Spain; 4: Department of Neurology, University Medical School, Robert-Koch-Straße 40, 37075 Göttingen, Germany; 5: German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Straße 3A, 37075 Göttingen, Germany



Novel method for assessing the effect of case-mix and model calibration on the ROC curve

Mohsen Sadatsafavi1, Paramita Saha Chaudhuri2, John Petkau3

1: Faculty of Pharmaceutical Sciences, The University of British Columbia, Canada; 2: Department of Epidemiology and Biostatistics, McGill University, Canada; 3: Department of Statistics, The University of British Columbia, Canada



A Permutation Test Approach to Provide Exact Inference for Incremental Gain from Nested Regression Models

Paramita Saha-Chaudhuri1, Li Cheung2, Hormuzd Katki2

1: University of Vermont, Burlington, USA; 2: National Cancer Institute, USA



Probabilistic data standardisation of big heterogeneous datasets in biomedicine

Alexia Sampri, Nophar Geifman, Philip Couch, Niels Peek

Division of Informatics, Imaging and Data Sciences University of Manchester, Manchester, UK



Predicting Biomarker success:a new toolkit

Katerina-Vanessa Savva

Imperial College London, United Kingdom



Assessing the impact of test measurement uncertainty on clinical and health-economic outcomes: a case study

Alison F Smith1,2, Michael P Messenger2,3, Claire T Hulme4, Peter S Hall5, Bethany Shinkins1,2

1: Test Evaluation Group, Academic Unit of Health Economics, University of Leeds, Leeds, UK; 2: NIHR Leeds In Vitro Diagnostic (IVD) Co-operative, Leeds, UK; 3: Leeds Centre for Personalised Medicine and Health, University of Leeds, Leeds, UK; 4: Health Economics Group, University of Exeter, Exeter, UK; 5: Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK



Beyond the laboratory: methods to assess the impact of test measurement uncertainty on outcomes

Alison F Smith1,2, Michael P Messenger2,3, Claire T Hulme4, Peter S Hall5, Bethany Shinkins1,2

1: Test Evaluation Group, Academic Unit of Health Economics, University of Leeds, Leeds, UK; 2: NIHR Leeds In Vitro Diagnostic (IVD) Co-operative, Leeds, UK; 3: Leeds Centre for Personalised Medicine and Health, University of Leeds, Leeds, UK; 4: Health Economics Group, University of Exeter, Exeter, UK; 5: Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK



Impact of prediction models in obstetric care: the Expect study

Pim van Montfort1, Hubertina Scheepers2, Carmen Dirksen3, Ivo van Dooren4, Linda Meertens1, Sander van Kuijk3, Ella Wijnen5, Maartje Zelis6, Iris Zwaan7, Marc Spaanderman2, Luc Smits1

1: Department of Epidemiology, Maastricht University, The Netherlands; 2: Department of Obstetrics and Gynecology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Centre, The Netherlands; 3: Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Centre, The Netherlands; 4: Department of Obstetrics and Gynecology, Sint Jans Gasthuis Weert, The Netherlands.; 5: Department of Obstetrics and Gynecology, VieCuri Medical Centre, Venlo, The Netherlands.; 6: Department of Obstetrics and Gynecology, Zuyderland Medical Centre, Heerlen, The Netherlands.; 7: Department of Obstetrics and Gynecology, Laurentius Hospital, Roermond, The Netherlands.



Development and validation of prediction models for pre-eclampsia: An Individual Participant Data Meta-analysis

John Allotey1, Kym Snell2, Richard Riley2, Shakila Thangaratinam1,3

1: Barts Research Centre for Women’s Health, Queen Mary University of London, UK; 2: Centre for Prognosis Research, Keele University, UK; 3: Institute of Metabolism and Systems Research, University of Birmingham, UK



External validation of prognostic models to predict pre-eclampsia: An Individual Participant Data Meta-analysis

John Allotey1, Kym Snell2, Richard Riley2, Shakila Thangaratinam1,3

1: Barts Research Centre for Women’s Health, Queen Mary University of London, UK; 2: Centre for Prognosis Research, Keele University, UK; 3: Institute of Metabolism and Systems Research, University of Birmingham, UK



Simulation-based sample size calculations for studies externally validating a prediction model

Kym Snell1, Lucinda Archer1, Joie Ensor1, Laura Bonnett2, Bob Phillips3, Gary Collins4, Richard Riley1

1: Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK; 2: Department of Biostatistics, University of Liverpool, Liverpool, UK; 3: Centre for Reviews and Dissemination, University of York, York, UK; 4: Centre for Statistics in Medicine, University of Oxford, Oxford, UK



TRIPOD-SR: An extension to reporting guidelines for systematic reviews of prediction model studies

Kym Snell1, Brooke Levis1, Thomas Debray2, Lotty Hooft2, Paula Dhiman3, Johannes Reitsma2, Karel Moons2, Gary Collins3, Richard Riley1

1: Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, Staffordshire, UK; 2: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; 3: Centre for Statistics in Medicine, University of Oxford, Oxford, UK



Use and misuse of the "calibration" slope

Richard Stevens1, Katrina Poppe2

1: University of Oxford, United Kingdom; 2: University of Auckland, New Zealand



Statistical methods for estimating sources of variability in count biomarkers

Kostas Tryposkiadis1,2, Alice Sitch1,2, Malcolm Price1,2, Jon Deeks1,2

1: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; 2: NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK



Statistical methods for the meta-analysis of reliability estimates reported in biological variability studies

Kostas Tryposkiadis1,2, Jac Dinnes1,2, Alice Sitch1,2, Malcolm Price1,2, Jon Deeks1,2

1: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom; 2: NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, UK



Pre-analytical error for three point of care venous blood testing platforms in acute ambulatory care

Thomas R Fanshawe1, Margaret Glogowska1, George Edwards1, Philip J Turner1, Ian Smith2, Rosie Steele2, Caroline Croxson1, Jordan ST Bowen2, Gail N Hayward1

1: Nuffield Department of Primary Care Health Sciences, University of Oxford; 2: Oxford University Hospitals NHS Foundation Trust



Development of a model to predict the likelihood of a genetic variant causing familial hypercholesterolaemia

Rachel A O'Leary1,2, Samuel G Urwin2,3, Clare Lendrem2,3, Ahai Luvai4, R Dermot G Neely2, A Joy Allen2

1: Northern Medical Physics and Clinical Engineering, The Newcastle-upon-Tyne Hospitals NHS Foundation Trust, UK; 2: The National Institute for Health Research Newcastle In Vitro Diagnostics Co-operative, Newcastle-upon-Tyne, UK; 3: Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, UK; 4: Laboratory Medicine, The Newcastle-upon-Tyne Hospitals NHS Foundation Trust, UK



Development of an application to support the identification of patients with familial hypercholesterolaemia

Rachel A O'Leary1,2, Samuel G Urwin2,3, Clare Lendrem2,3, Ahai Luvai4, R Dermot G Neely2, A Joy Allen2

1: Northern Medical Physics and Clinical Engineering, The Newcastle-upon-Tyne Hospitals NHS Foundation Trust, UK; 2: The National Institute for Health Research Newcastle In Vitro Diagnostics Co-operative, Newcastle-upon-Tyne, UK; 3: Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, UK; 4: Laboratory Medicine, The Newcastle-upon-Tyne Hospitals NHS Foundation Trust, UK



The potential of ‘conceptually oriented’ pre-processing of covariates to improve prognostic models. A case study.

Anne Molgaard Nielsen1, Adrian Binding2, Casey Ahlbrandt-Rains3, Martin Boeker3, Stefan Feuerriegel2, Werner Vach4,5

1: Department of Sports Science and Clinical Biomechanics, University of Southern Denmark; 2: Department of Management, Technology, and Economics, ETH Zurich; 3: Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg; 4: Basel Academy for Quality and Rsearch in Medicine; 5: Nordic Institute of Chiropractic and Clinical Biomechanics



Visualizing the results of a diagnostic accuracy study using comparison regions

Maren Eckert, Werner Vach

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany



Using harmonised results of different tests for a single biomarker in test accuracy meta-analysis

Yasaman Vali1, Jenny Lee1, Patrick M. Bossuyt1, Jerome Boursier2,3, Mohammad Hadi Zafarmand1

1: Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Center, Amsterdam, The Netherlands; 2: Hepato-Gastroenterology Department, Angers University Hospital, Angers, France; 3: HIFIH Laboratory, UPRES EA3859, Angers University, Angers France



Large-scale validation of the Prediction model Risk Of Bias ASsessment Tool(PROBAST) using a short form

Esmee Venema1, Benjamin S Wessler2, Jessica K Paulus2, Rehab Salah3, Gowri Raman2, Lester Y Leung2, Benjamin C Koethe2, Jason Nelson2, Jinny G Park2, David van Klaveren1,2, Ewout W Steyerberg1,4, David M Kent2

1: Erasmus MC University Medical Center, Rotterdam, The Netherlands; 2: Tufts Medical Center, Boston, MA, USA; 3: Benha Faculty of Medicine, Benha, Egypt; 4: Leiden University Medical Center, Leiden, The Netherlands



Network meta-analysis methods for ranking the accuracy of multiple diagnostic tests

Areti Angeliki Veroniki1,2,3, Sofia Tsokani1, Yemisi Takwoingi4,5, Dimitris Mavridis1,6

1: Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece; 2: Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada; 3: Institute of Reproductive and Developmental Biology, Department of Surgery & Cancer, Faculty of Medicine, Imperial College, London, United Kingdom; 4: Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, UK; 5: NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK; 6: Paris Descartes University, Sorbonne Paris Cité, Faculté de Médecine, Paris, France



Clinical Prediction Models for Patients with Acute Coronary Syndromes: Results from Independent External Validations

Benjamin S Wessler1,2, Jason Nelson1, Jinny Park1, Hannah McGinnis1, Jenica Upshaw1,2, Ben Van Calster3, David van Klaveren1,4, Ewout Steyerberg4, David Kent1

1: Predictive Analytics and Comparative Effectiveness (PACE), Tufts Medical Center, United States of America; 2: Division of Cardiology, Tufts Medical Center, Boston, MA; 3: KU Leuven, Department of Development and Regeneration, Leuven, Belgium; 4: Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands



Survey for elucidating potential roles for sepsis diagnostics in the UK NHS

Amanda Winter1,2, William Stephen Jones4, Anthony Rostron4,5, A. Joy Allen4, Raffaele Filieri3, D. Ashley Price2, Sara Graziadio1,2

1: National Institute for Heath Research Newcastle In vitro Diagnostics Co-operative, Newcastle University, Medical School, Framlington Place, Newcastle-upon-Tyne, UK; 2: The Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Royal Victoria Infirmary, Queen Victoria Road, Newcastle-upon-Tyne, UK; 3: Department of Marketing, Audencia Business School, 8 Route de la Jonelière, B.P. 31222 44312 Nantes, Cedex 3, France; 4: Translational and Clinical Research Institute, Newcastle University, Medical School, Framlington Place, Newcastle-upon-Tyne, UK; 5: South Tyneside and Sunderland NHS Foundation Trust, Kayll Road, Sunderland, UK

5:30pm
-
5:40pm
Short break & Poster viewing
5:40pm
-
6:20pm
Invited talk Rudi Pauwels
Chair: Ann Van den Bruel
 
5:40pm - 6:20pm

High Impact Pandemics: From Crisis to Preparedness

Rudi Pauwels

Praesens Foundation, Belgium


 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: MEMTAB 2020
Conference Software - ConfTool Pro 2.6.136
© 2001 - 2021 by Dr. H. Weinreich, Hamburg, Germany