A5086736480- Health Systems, Economic Evaluations, Quality of Life
- Machine Learning in Healthcare
- Meta-analysis and systematic reviews
- Cancer, Hypoxia, and Metabolism
- RNA modifications and cancer
- Sepsis Diagnosis and Treatment
- Cancer-related molecular mechanisms research
- Hernia repair and management
- Musculoskeletal pain and rehabilitation
- Blood Pressure and Hypertension Studies
- Mental Health Treatment and Access
- Statistical Methods in Clinical Trials
- Workplace Health and Well-being
- Pregnancy and preeclampsia studies
- Statistical Methods and Inference
- Occupational Health and Safety Research
- Artificial Intelligence in Healthcare and Education
- Gestational Diabetes Research and Management
- Artificial Intelligence in Healthcare
- Pelvic and Acetabular Injuries
- Birth, Development, and Health
- Chronic Disease Management Strategies
- Health, psychology, and well-being
- Intestinal and Peritoneal Adhesions
- Academic Publishing and Open Access
University of Birmingham
2018-2025
NIHR Birmingham Biomedical Research Centre
2023-2025
Keele University
2020-2024
Primary Health Care
2023
University College London
2023
University of Cambridge
2023
University of Bristol
2023
University of Oxford
2023
Henry Morrison Flagler Museum
2021
Allergan (Ireland)
2021
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...
Clinical prediction models provide individualized outcome predictions to inform patient counseling and clinical decision making. External validation is the process of examining a model's performance in data independent that used for model development. Current external studies often suffer from small sample sizes, subsequently imprecise estimates predictive performance. To address this, we propose how determine minimum size needed with continuous outcome. Four criteria are proposed, target...
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.
External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on evaluation, Riley and colleagues explain what external study entails describe key steps involved, from establishing high quality dataset to evaluating model's predictive performance clinical usefulness.
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.
Intratumoral hypoxia and immunity have been correlated with patient outcome in various tumor settings. However, these factors are not currently considered for treatment selection head neck cancer (HNC) due to lack of validated biomarkers. Here we sought develop a hypoxia-immune classifier potential application prognostication prediction response targeted therapy.
ObjectivesWhen developing a clinical prediction model, penalization techniques are recommended to address overfitting, as they shrink predictor effect estimates toward the null and reduce mean-square error in new individuals. However, shrinkage penalty terms ('tuning parameters') estimated with uncertainty from development data set. We examined magnitude of this subsequent impact on model performance.Study Design SettingThis study comprises applied examples simulation following methods:...
IntroductionSample size "rules-of-thumb" for external validation of clinical prediction models suggest at least 100 events and non-events. Such blanket guidance is imprecise, not specific to the model or setting. We investigate factors affecting precision performance estimates upon validation, propose a more tailored sample approach.MethodsSimulation logistic regression associated with estimates. Then, explanation illustration simulation-based approach calculate minimum required precisely...
Abstract Previous articles in Statistics Medicine describe how to calculate the sample size required for external validation of prediction models with continuous and binary outcomes. The minimum criteria aim ensure precise estimation key measures a model's predictive performance, including calibration, discrimination, net benefit. Here, we extend guidance time‐to‐event (survival) outcome, cover datasets containing censoring. A simulation‐based framework is proposed, which calculates target...
Abstract Background Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) inform individual diagnosis and prognosis in healthcare. However, most not reliable for use practice. Main body We discuss how the creation a model using regression or machine learning methods) is dependent on sample size data used develop it—were different same from overarching population, could be very even when development methods used. In other words, each...
Background: When developing a clinical prediction model using time-to-event data, previous research focuses on the sample size to minimise overfitting and precisely estimate overall risk. However, instability of individual-level risk estimates may still be large. Methods: We propose decomposition Fisher's information matrix examine calculate required for that aims precise fair estimates. six-step process which can used before data collection or when an existing dataset is available. Steps...
Objectives: Evidence from clinical trials suggests that antihypertensive treatment is associated with an increased risk of common electrolyte abnormalities. We aimed to develop and validate two prediction models estimate the hyperkalaemia hyponatraemia, respectively, facilitate targeted monitoring strategies for individuals indicated therapy. Design methods: Participants aged at least 40 years, registered English primary care practice within Clinical Practice Research Datalink (CPRD), a...
Abstract Objective To develop and externally validate the STRAtifying Treatments In multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment. Design Retrospective cohort study. Setting Primary care data electronic health records contained within UK Clinical Practice Research Datalink (CPRD). Participants Patients aged 40 years older at least one blood...
Abstract Background Falls are common in older adults and can devastate personal independence through injury such as fracture fear of future falls. Methods to identify people for falls prevention interventions currently limited, with high risks bias published prediction models. We have developed externally validated the eFalls model using routinely collected primary care electronic health records (EHR) predict risk emergency department attendance/hospitalisation fall or within 1 year. Data...
To develop and validate prediction models for the risk of future work absence level presenteeism, in adults seeking primary healthcare with musculoskeletal disorders (MSD).
Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks.To develop a prediction model estimating AKI in people potentially indicated for antihypertensive treatment.Observational cohort study using routine primary care from Clinical Practice Research Datalink (CPRD) England.People aged ≥40 years, at least one blood pressure measurement between 130...