Program

POSTER SESSION B-04: Prediction and Prognostic Models

Tracks

POSTER AREA (M1 building upper floor)

Wednesday, July 24, 2024

9:00 AM - 5:00 PM

POSTER AREA (M1 building upper floor)

Presentations

Mr. Lasai Barreñada

KU Leuven, Belgium

P-B04-01 | Multicentre flexible calibration curves with binary outcomes using random effects meta-analysis

Ms. Mae Chester-Jones

DPhil Clinical Epidemiology And Biostatistics

University of Oxford, United Kingdom

P-B04-02 | Maternal Early Warning Scores for detecting deterioration in women during pregnancy: A systematic review

Mr. Gordon Forbes

King's College London, United Kingdom

P-B04-03 | Accounting for between-study heterogeneity when developing prediction models using IPD meta-analysis: A comparison of methods and recommendations for practice

Ms. Amy Magona

Biostatistician

Primary Care Clinical Trials Unit, University of Oxford, United Kingdom

P-B04-04 | Prediction performance effect of added-value biomarkers in multivariable heart failure prognostic models: A systematic review

Mr. Giuseppe Occhino

Strategic Regional Agency for Health and Social Care of Apulia (AReSS Puglia), Bari, Italy

P-B04-05 | Prognostic models for heart failure progression: Systematic review and meta-analysis

Ms. Jyoti Sehjal

PhD Student

University of Oxford, United Kingdom

P-B04-06 | Prediction models for prognostic outcomes in myelodysplastic syndromes and acute myeloid leukaemia: A systematic review

Prof. Gareth Ambler

University College London, United Kingdom

P-B04-07 | A sample size calculation for developing a risk prediction model for a binary outcome

Dr. Menelaos Pavlou

Associate Professor

University College London, United Kingdom

P-B04-08 | How to improve model stability when calculating the sample size and accuracy when estimating a prediction model

Prof. Donna Ankerst

Technical University of Munich, Germany

P-B04-09 | Selection and verification bias adjustments for clinical risk model validation

Mr. Frank Doornkamp

Leiden UMC, Netherlands

P-B04-10 | Integrating genomic markers for enhanced prediction: Uncertainty assessment of clinical usefulness in early breast cancer

Prof. Evangelos Kritsotakis

Associate Professor of Biostatistics

University of Crete, Heraklion, Greece

P-B04-11 | Statistical pitfalls and errors in external validation studies of clinical prediction models: A case study with the SAPS II and SAPS 3 prognostic models for critically ill patients

Mr. Yanakan Logeswaran

PhD Student

King's College London, United Kingdom

P-B04-12 | Examining model stability across modelling approaches to predicting poor functioning for individuals at-risk of psychosis

Dr. Ewan Carr

King’s College London, United Kingdom

P-B04-13 | Development and validation of a novel longitudinal prognostic model for depressive relapse using passively measured biomarkers

Dr. Ryo Emoto

Nagoya Graduate School of Medicine, Japan

P-B04-14 | Development and validation of treatment effect functions in observational studies: Application to predictive analysis of amiodarone in out-of-hospital cardiac arrest patients

Ms. Shan Gao

KU Leuven, Belgium

P-B04-15 | A comparison of regression models for static and dynamic prediction in electronic health records in the absence of censoring

Dr. Tommi Härkänen

Research Manager

Finnish Institute for Health and Welfare (THL), Helsinki, FInland

P-B04-16 | Diagnostic tool to assess projected occupation probabilities in illness-death model

Dr. Joyce (Yun-Ting) Huang

Research Associate

University of Manchester, United Kingdom

P-B04-17 | Quantifying the impact of underdiagnosis in outcome data when developing and validating a clinical prediction model: A simulation study

Dr. Seyed Amirhossein Jalali

University of Limerick, Ireland

P-B04-18 | Prognosis value of immunocyte ratios in neoadjuvant chemotherapy-treated breast cancer

Dr. David Jenkins

University of Manchester, United Kingdom

P-B04-19 | Use of statistical process control to monitor calibration-in-the-large of a clinical prediction model

Ms. Carolien Maas

Erasmus MC, Rotterdam, Netherlands

P-B04-20 | Predicting individualised treatment effects: A comparison of different modelling approaches and performance metrics

Mr. Ryunosuke Machida

National Cancer Center, Tokyo, Japan

P-B04-21 | Predicting study duration based on dynamic predictions using joint models in clinical trials with a time-to-event endpoint

Dr. Roberto Melotti

Eurac Research, Bolzano/Bozen, Italy

P-B04-22 | Tremor classification by digital spiral analysis in a large population sample

Mr. Asanao Shimokawa

Associate Professor

Tokyo University of Science, Japan

P-B04-23 | Construction of decision tree using prior information

Mr. Thomas Stojanov

University Hospital Basel, Switzerland

P-B04-24 | Development and validation of a model predicting post-operative shoulder stiffness by patients undergoing an arthroscopic rotator cuff repair in Switzerland

Dr. Cristian Tebe

Germans Trias I Pujol Research Institute and Hospital (IGTP), Barcelona, Spain

P-B04-25 | Can a clinical model be used to update a radiomics deep learning model for prediction of lung nodule malignancy?

Dr. David Van Klaveren

Associate Professor

Erasmus MC, Rotterdam, Netherlands

P-B04-26 | Stronger penalties on treatment-covariate interactions improve the ability to predict treatment effect

Mr. Steven Wambua

University of Birmingham, United Kingdom

P-B04-27 | Development and validation of postpartum cardiovascular disease (CVD) risk prediction model in women with a history of pregnancy incorporating reproductive and pregnancy-related candidate predictors.

Dr. Junfeng Wang

Assistant Professor

Utrecht University, Netherlands

P-B04-28 | Comparison of different strategies in using Lasso in clinical prediction models for rare outcomes: A simulation study

Mr. Zhenwei Yang

Erasmus MC, Rotterdam, Netherlands

P-B04-29 | Predictive accuracy metrics in the context of interval censoring and competing risks

Ms. Lucinda Archer

University of Birmingham, United Kingdom

P-B04-30 | Stop before you start: A checklist for those thinking about developing a clinical prediction model

Ms. Lucinda Archer

University of Birmingham, United Kingdom

P-B04-31 LB | Uncertainty in clinical risk prediction: perspectives and approaches

Dr. Amardeep Legha

Research Fellow In Biostatistics

University of Birmingham, United Kingdom

POSTER SESSION B-04: Prediction and Prognostic Models

Presentations

P-B04-01 | Multicentre flexible calibration curves with binary outcomes using random effects meta-analysis

P-B04-02 | Maternal Early Warning Scores for detecting deterioration in women during pregnancy: A systematic review

P-B04-03 | Accounting for between-study heterogeneity when developing prediction models using IPD meta-analysis: A comparison of methods and recommendations for practice

P-B04-04 | Prediction performance effect of added-value biomarkers in multivariable heart failure prognostic models: A systematic review

P-B04-05 | Prognostic models for heart failure progression: Systematic review and meta-analysis

P-B04-06 | Prediction models for prognostic outcomes in myelodysplastic syndromes and acute myeloid leukaemia: A systematic review

P-B04-07 | A sample size calculation for developing a risk prediction model for a binary outcome

P-B04-08 | How to improve model stability when calculating the sample size and accuracy when estimating a prediction model

P-B04-09 | Selection and verification bias adjustments for clinical risk model validation

P-B04-10 | Integrating genomic markers for enhanced prediction: Uncertainty assessment of clinical usefulness in early breast cancer

P-B04-11 | Statistical pitfalls and errors in external validation studies of clinical prediction models: A case study with the SAPS II and SAPS 3 prognostic models for critically ill patients

P-B04-12 | Examining model stability across modelling approaches to predicting poor functioning for individuals at-risk of psychosis

P-B04-13 | Development and validation of a novel longitudinal prognostic model for depressive relapse using passively measured biomarkers

P-B04-14 | Development and validation of treatment effect functions in observational studies: Application to predictive analysis of amiodarone in out-of-hospital cardiac arrest patients

P-B04-15 | A comparison of regression models for static and dynamic prediction in electronic health records in the absence of censoring

P-B04-16 | Diagnostic tool to assess projected occupation probabilities in illness-death model

P-B04-17 | Quantifying the impact of underdiagnosis in outcome data when developing and validating a clinical prediction model: A simulation study

P-B04-18 | Prognosis value of immunocyte ratios in neoadjuvant chemotherapy-treated breast cancer

P-B04-19 | Use of statistical process control to monitor calibration-in-the-large of a clinical prediction model

P-B04-20 | Predicting individualised treatment effects: A comparison of different modelling approaches and performance metrics

P-B04-21 | Predicting study duration based on dynamic predictions using joint models in clinical trials with a time-to-event endpoint

P-B04-22 | Tremor classification by digital spiral analysis in a large population sample

P-B04-23 | Construction of decision tree using prior information

P-B04-24 | Development and validation of a model predicting post-operative shoulder stiffness by patients undergoing an arthroscopic rotator cuff repair in Switzerland

P-B04-25 | Can a clinical model be used to update a radiomics deep learning model for prediction of lung nodule malignancy?

P-B04-26 | Stronger penalties on treatment-covariate interactions improve the ability to predict treatment effect

P-B04-27 | Development and validation of postpartum cardiovascular disease (CVD) risk prediction model in women with a history of pregnancy incorporating reproductive and pregnancy-related candidate predictors.

P-B04-28 | Comparison of different strategies in using Lasso in clinical prediction models for rare outcomes: A simulation study

P-B04-29 | Predictive accuracy metrics in the context of interval censoring and competing risks

P-B04-30 | Stop before you start: A checklist for those thinking about developing a clinical prediction model

P-B04-31 LB | Uncertainty in clinical risk prediction: perspectives and approaches

P-B04-32 LB | Uncertainty-based sequential sample size calculations for developing, validating and updating clinical prediction models