204 citations · 209 across the 10 of their papers we have counts for
11 papers
Collapsibility of Performance Metrics in Clinical Predictive AI
João Matos, Ben Van Calster, Richard D. Riley +2
Background: Population level assessments of predictive artificial intelligence (AI) can conceal performance disparities across subgroups. Fairness evaluations commonly rely on perf…
Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
Glen P. Martin, Sian Bladon, Rebecca Whittle +3
Clinical prediction models must be developed using sufficiently large datasets to minimise overfitting and ensure robust predictive performance. Existing sample size calculations a…
Code Sharing in Healthcare Research: A Practical Guide and Recommendations for Good Practice
Lukas Hughes-Noehrer, Matthew J Parkes, Andrew Stewart +8
As computational analysis becomes increasingly more complex in health research, transparent sharing of analytical code is vital for reproducibility and trust. This practical guide,…
Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
Amardeep Legha, Joie Ensor, Rebecca Whittle +7
When prospectively developing a new clinical prediction model (CPM), fixed sample size calculations are typically conducted before data collection based on sensible assumptions. Bu…
A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes
Rebecca Whittle, Richard D Riley, Lucinda Archer +4
Clinical prediction models enable healthcare professionals to estimate individual outcomes using patient characteristics. Current sample size guidelines for developing or updating…
Critical Appraisal of Fairness Metrics in Clinical Predictive AI
João Matos, Ben Van Calster, Leo Anthony Celi +6
Predictive artificial intelligence (AI) offers an opportunity to improve clinical practice and patient outcomes, but risks perpetuating biases if fairness is inadequately addressed…
A general sample size framework for developing or updating a clinical prediction model
Richard D Riley, Rebecca Whittle, Mohsen Sadatsafavi +4
Aims: To propose a general sample size framework for developing or updating a clinical prediction model using any statistical or machine learning method, based on drawing samples f…
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes
Richard D Riley, Gary S Collins, Lucinda Archer +9
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 the ov…
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Richard D Riley, Gary S Collins, Rebecca Whittle +11
When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instabil…
Extended sample size calculations for evaluation of prediction models using a threshold for classification
Rebecca Whittle, Joie Ensor, Lucinda Archer +10
When evaluating the performance of a model for individualised risk prediction, the sample size needs to be large enough to precisely estimate the performance measures of interest.…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…