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Robust integration of external control data in randomized trials

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arXiv 2024年

作者： Karlsson, Rickard Wang, Guanbo De Bartolomeis, Piersilvio Krijthe, Jesse H. Dahabreh, Issa J. Pattern Recognition Laboratory Delft University of Technology Delft Netherlands CAUSALab Harvard T.H. Chan School of Public Health BostonMA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Department of Computer Science ETH Zürich Zürich Switzerland Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

One approach for increasing the efficiency of randomized trials is the use of "external controls" – individuals who received the control treatment studied in the trial during routine practice or in prior experimental studies. Existing external control methods, however, can be biased if the populations underlying the trial and the external control data are not exchangeable. Here, we characterize a randomization-aware class of treatment effect estimators in the population underlying the trial that remain consistent and asymptotically normal when using external control data, even when exchangeability does not hold. We consider two members of this class of estimators: the well-known augmented inverse probability weighting trial-only estimator, which is the efficient estimator when only trial data are used;and a potentially more efficient member of the class when exchangeability holds and external control data are available, which we refer to as the optimized randomization-aware estimator. To achieve robust integration of external control data in trial analyses, we then propose a combined estimator based on the efficient trial-only estimator and the optimized randomization-aware estimator. We show that the combined estimator is consistent and no less efficient than the most efficient of the two component estimators, whether the exchangeability assumption holds or not. We examine the estimators’ performance in simulations and we illustrate their use with data from two trials of paliperidone extended-release for schizophrenia. © 2024, CC BY-NC-SA.

关键词： Data assimilation

Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

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Hybrid genetic-discretized algorithm to handle data uncertai...

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

When accurate prediction models yield harmful self-fulfilling prophecies

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arXiv 2023年

作者： van Amsterdam, Wouter A.C. van Geloven, Nan Krijthe, Jesse H. Ranganath, Rajesh Cinà, Giovanni Department of Data Science and Biostatistics Julius Center of Health Sciences Primary Care University Medical Center Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands University of Utrecht Heidelberglaan 100 Utrecht3584 CX Netherlands Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Pattern Recognition & Bioinformatics Delft University of Technology Delft Netherlands Courant Institute of Mathematical Science Department of Computer Science Center for Data Science New York University New York City United States Department of Medical Informatics Amsterdam University Medical Center Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands

Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisions, and are often hailed as the poster children for personalized, data-driven healthcare. We show however, that using prediction models for decision making can lead to harmful decisions, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients but the worse outcome of these patients does not invalidate the predictive power of the model. Our main result is a formal characterization of a set of such prediction models. Next we show that models that are well calibrated before and after deployment are useless for decision making as they made no change in the data distribution. These results point to the need to revise standard practices for validation, deployment and evaluation of prediction models that are used in medical decisions. © 2023, CC BY.

关键词： Prediction models

C-reactive protein-guided treatment in pneumonia: charting a personalised approach - Authors' reply

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The Lancet. Respiratory medicine 2025年第5期13卷 e25-e26页

作者： Jim M Smit Jesse H Krijthe Gianfranco U Meduri Pierre-François Dequin Harin Karunajeewa Antoni Torres Marcel J T Reinders Henrik Endeman Philip A Van Der Zee Department of Intensive Care Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Pattern Recognition & Bioinformatics group Delft University of Technology Delft Netherlands. Electronic address: j.smit@erasmusmc.nl. Pattern Recognition & Bioinformatics group Delft University of Technology Delft Netherlands. Department of Pharmaceutical Sciences University of Tennessee Health Science Center Memphis TN USA. Medecine intensive reanimation Chru Hôpitaux De Tours Hospital Bretonneau Tours France. Department of Medicine The Western Health Chronic Disease Alliance and the University of Melbourne Melbourne VIC Australia. Department of Pulmonology Hospital Clinic University of Barcelona Barcelona Spain. Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands. Department of Pulmonary Medicine Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands

Time-independent disease state identification defines distinct trajectories determined by localised vs systemic inflammation in patients with early rheumatoid arthritis

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Annals of the Rheumatic Diseases 2025年

作者： Steinz, Nils Maarseveen, Tjardo D. van den Akker, Erik B. Cope, Andrew P. Isaacs, John D. Winkler, Aaron R. Huizinga, Tom W. J. Abraham, Yann Knevel, Rachel Department of Rheumatology Leiden University Medical Centre Leiden Netherlands Leiden Computational Biology Centre Leiden University Medical Center Leiden Netherlands The Delft Bioinformatics Lab Pattern Recognition and Bioinformatics Delft University of Technology Delft Netherlands Centre for Rheumatic Diseases School of Immunology and Microbial Sciences Faculty of Life Sciences and Medicine King's College London London United Kingdom Musculoskeletal Unit Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle upon Tyne United Kingdom Inflammation and Immunology Research Unit Pfizer Inc Cambridge MA United States Janssen Research and Development Beerse Belgium Translational and Clinical Research Institute Newcastle University Newcastle upon Tyne United Kingdom

Objectives: Patients with rheumatoid arthritis (RA) display different trajectories towards improvement of disease. We aimed to disentangle the heterogeneity of RA disease trajectories from the first clinical visit onwards using graph-based pseudotime analysis. Methods: We studied early patients with RA over 1.5 years in 2 data sets: Leiden (Netherlands), n = 1237, with 5017 visits, and Towards a Cure for Early Rheumatoid Arthritis (TACERA) (United Kingdom), n = 243, with 750 visits. We created a pipeline for time-independent clustering of clinical and haematologic features to identify disease states. Sequence analyses of these states defined the trajectories. We studied the predictability of the trajectories with baseline features. Results: Clustering identified 8 disease states with localised inflammation (joints) and systemic inflammation (erythrocyte sedimentation rate [ESR] or leucocytes) as the main discriminating factors. The disease state sequences consisted of 4 trajectories, which we independently replicated in TACERA: A, high ESR;B, rapid progression from many inflamed joints towards remission;C, high leucocytes;and D, many inflamed joints with poor prognosis. Systemic vs local inflammation patterns showed moderate predictability at baseline (sensitivity of 71% and precision of 0.73 for trajectory A, although lower precision of 0.52 for trajectory B), while other trajectories were less predictable. Trajectories C and D had strong resemblance with B at baseline but deteriorated into less favourable trajectories. Patients in trajectory A were more often female and on average older. The trajectories were not explained by time till disease-modifying antirheumatic drug, baseline disease activity, or symptom duration. The suboptimal trajectories coincided with worse patient-reported outcomes, even when the inflammation was mainly systemic. Conclusions: We identified 4 distinct trajectories in early RA, differentiating RA into localised vs systemic inflammation.

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The INTERSPEECH 2021 computational paralinguistics challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

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arXiv 2021年

作者： Schuller, Björn W. Batliner, Anton Bergler, Christian Mascolo, Cecilia Han, Jing Lefter, Iulia Kaya, Heysem Amiriparian, Shahin Baird, Alice Stappen, Lukas Ottl, Sandra Gerczuk, Maurice Tzirakis, Panagiotis Brown, Chloë Chauhan, Jagmohan Grammenos, Andreas Hasthanasombat, Apinan Spathis, Dimitris Xia, Tong Cicuta, Pietro Rothkrantz, Leon J.M. Zwerts, Joeri Treep, Jelle Kaandorp, Casper GLAM – Group on Language Audio & Music Imperial College London United Kingdom EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Pattern Recognition Lab FAU Erlangen-Nuremberg Germany University of Cambridge United Kingdom Delft University of Technology Netherlands Faculty of Science Utrecht University Netherlands

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech;in the Escalation Sub-Challenge, a three-way assessment of the level of escalation in a dialogue is featured;and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the ‘usual’ COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit;in addition, we add deep end-to-end sequential modelling, and partially linguistic *** Codes 68 © 2021, CC BY-NC-ND.

关键词： Mammals

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ZygosityPredictor

bioinformatics Advances 2024年第1期4卷 vbae017页

作者： Rheinnecker, Marco Fröhlich, Martina Rübsam, Marc Paramasivam, Nagarajan Heilig, Christoph E. Fröhling, Stefan Schlenk, Richard F. Hutter, Barbara Hübschmann, Daniel Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg German Cancer Research Center (DKFZ) Heidelberg 69120 Germany German Cancer Consortium (DKTK) Heidelberg 69120 Germany Division of Translational Medical Oncology NCT Heidelberg and DKFZ Heidelberg 69120 Germany Department of Medical Oncology NCT Heidelberg Heidelberg University Hospital Heidelberg 69120 Germany Department of Hematology Oncology and Rheumatology Heidelberg University Hospital Heidelberg 69120 Germany NCT Trial Center NCT Heidelberg Heidelberg University Hospital DKFZ Heidelberg 69120 Germany Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) Pattern Recognition and Digital Medicine Group Heidelberg 69120 Germany Innovation and Service Unit for Bioinformatics and Precision Medicine German Cancer Research Center (DKFZ) Heidelberg 69120 Germany

ZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. © The Author(s) 2024.

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Risk-based decision making: estimands for sequential prediction under interventions

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

A review of domain adaptation without target labels

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arXiv 2019年

作者： Kouw, Wouter M. Loog, Marco Datalogisk Institut University of Copenhagen at Universitetsparken 1 CopenhagenDK-2100 Denmark Pattern Recognition Laboratory Delft University of Technology Delft Netherlands Datalogisk Institut University of Copenhagen Copenhagen Denmark

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research. Copyright © 2019, The Authors. All rights reserved.

关键词： pattern recognition