A5067519537- Health Systems, Economic Evaluations, Quality of Life
- Advanced Causal Inference Techniques
- Meta-analysis and systematic reviews
- Statistical Methods in Clinical Trials
- Statistical Methods and Bayesian Inference
- Machine Learning in Healthcare
- COVID-19 Clinical Research Studies
- COVID-19 and healthcare impacts
- Statistical Methods and Inference
- Artificial Intelligence in Healthcare and Education
- Sepsis Diagnosis and Treatment
- Venous Thromboembolism Diagnosis and Management
- Frailty in Older Adults
- Sports injuries and prevention
- Artificial Intelligence in Healthcare
- Reliability and Agreement in Measurement
- Chronic Disease Management Strategies
- Long-Term Effects of COVID-19
- COVID-19 diagnosis using AI
- Pneumonia and Respiratory Infections
- Statistical Methods in Epidemiology
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Colorectal Cancer Screening and Detection
- Acute Ischemic Stroke Management
- Emergency and Acute Care Studies
University Medical Center Utrecht
2016-2025
Utrecht University
2013-2025
Leiden University Medical Center
2018-2024
Heidelberg University
2016-2024
University Hospital Heidelberg
2016-2024
Oklahoma State University Center for Health Sciences
2018-2024
University of California, San Francisco
2024
University College London
2021
Nuffield Orthopaedic Centre
2020
University of Oxford
2020
Clinical prediction models aim to predict outcomes in individuals, inform diagnosis or prognosis healthcare. Hundreds of are published the medical literature each year, yet many developed using a dataset that is too small for total number participants outcome events. This leads inaccurate predictions and consequently incorrect healthcare decisions some individuals. In this article, authors provide guidance on how calculate sample size required develop clinical model.
Abstract Background The assessment of calibration performance risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated can be misleading and potentially harmful for clinical decision-making. We summarize how avoid poor at algorithm development assess validation, emphasizing balance between model complexity the available sample size. At external...
The Transparent Reporting of a multivariable prediction model Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction Risk Of Bias ASsessment Tool (PROBAST) were both published to improve reporting critical appraisal studies for diagnosis prognosis. This paper describes processes methods that will be used develop an extension TRIPOD (TRIPOD-artificial intelligence, AI) PROBAST (PROBAST-AI) tool applied machine learning techniques.
Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine minimal sample size required and maximum number candidate predictors that can be examined. We present extensive simulation study in which we studied influence EPV, events fraction, predictors, correlations distributions predictor variables, area under ROC...
Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this EPV only one supports the use of minimum 10 EPV. In paper, we examine reasons substantial differences between these extensive studies. The current study uses Monte Carlo simulations to evaluate small bias, coverage confidence intervals and mean square error logit coefficients. Logistic models fitted by...
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations studies developing or evaluating performance model. Methodological advances field have since included widespread use artificial intelligence (AI) powered by machine learning methods develop models. An update is thus needed. TRIPOD+AI provides harmonised guidance studies, irrespective whether regression...
In prediction model research, external validation is needed to examine an existing model's performance using data independent that for development. Current studies often suffer from small sample sizes and consequently imprecise predictive estimates. To address this, we propose how determine the minimum size a new study of binary outcome. Our calculations aim precisely estimate calibration (Observed/Expected slope), discrimination (C‐statistic), clinical utility (net benefit). For each...
Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses therapies. If successful, this strategy would represent revolution in clinical research practice. However, although the vision individually tailored is alluring, there need distinguish genuine potential from hype. We argue that goal medical care faces serious challenges, many which cannot be addressed...
The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it unknown whether any are suitable widespread clinical implementation. We independently externally validated the performance candidate models, identified through a living systematic review, among consecutive adults admitted to hospital with final diagnosis COVID-19. reconstructed as per original descriptions and evaluated their intended outcomes using predictors measured at time...
Abstract Objective Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models. We examined effect correction on performance logistic regression Material Prediction models were developed using standard penalized (ridge) under 4 methods address imbalance: no correction, random undersampling, oversampling, SMOTE. Model was evaluated in terms discrimination, calibration, classification....
Abstract Objective To review and critically appraise published preprint reports of models that aim to predict either (i) presence existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) identify at high risk for the general population. Design Rapid systematic critical appraisal prediction diagnosis prognosis infection. Data sources PubMed, EMBASE via Ovid, Arxiv, medRxiv bioRxiv until 24 th March 2020. Study selection Studies developed...
Abstract Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external sufficient to claim that a model works well the intended context? Main body We argue contrary because (1) patient populations vary, (2) measurement procedures and (3) measurements change over time. Hence, we have expect heterogeneity between locations settings, across It follows are never truly validated. This does...
Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in populations and settings intended for use. In this article, first three part series, Collins colleagues describe importance meaningful evaluation using internal, internal-external, external validation, as well exploring heterogeneity, fairness, generalisability performance.
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With introduction such AI-based model tools and software cardiovascular patient care, researcher healthcare professional are challenged to understand opportunities as well limitations predictions. In this article, we present 12 critical questions for health professionals ask when confronted with an model. We aim support distinguish models that can add value care from AI does not.
Risk prediction models need thorough validation to assess their performance. Validation of for survival outcomes poses challenges due the censoring observations and varying time horizon at which predictions can be made. This article describes measures evaluate potential improvement in decision making from based on Cox proportional hazards regression. As a motivating case study, authors consider composite outcome recurrence or death (the "event") patients with breast cancer after surgery....
Aims Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such model, researchers should ensure number participants ([Formula: see text]) is appropriate relative events and predictor parameters for each category k. We propose three criteria determine minimum n required in light existing developed binary outcomes. Proposed The first criterion aims minimise model overfitting. second difference between observed...
An external validation study evaluates the performance of a prediction model in new data, but many these studies are too small to provide reliable answers. In third article their series on evaluation, Riley and colleagues describe how calculate sample size required for studies, propose avoid rules thumb by tailoring calculations setting at hand.
To determine the frequency and pattern of cardiac complications in patients hospitalised with coronavirus disease (COVID-19).
Abstract Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and instrument or procedural errors. If the effect of is misjudged, analyses hampered validity study’s inferences may be affected. In this paper, we describe five myths that contribute misjudgments about error, regarding expected structure, impact solutions mitigate problems resulting from mismeasurements. The aim clarify...