32 citations · 86 across the 23 of their papers we have counts for
7 papers · 1 filter
Non-parametric assessment of the calibration of individualized treatment effects
Mohsen Sadatsafavi, Jeroen Hoogland, Thomas P. A. Debray +1
An important aspect of the performance of algorithms that predict individualized treatment effects (ITE) is moderate calibration, i.e., the average treatment effect among individua…
Transportability of Prognostic Markers: Rethinking Common Practices through a Sufficient-Component-Cause Perspective
Mohsen Sadatsafavi, Gavin Pereira, Wenjia Chen
Transportability, the ability to maintain performance across populations, is a desirable property of markers of clinical outcomes. However, empirical findings indicate that markers…
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…
Bayesian sample size calculations for external validation studies of risk prediction models
Mohsen Sadatsafavi, Paul Gustafson, Solmaz Setayeshgar +2
Contemporary sample size calculations for external validation of risk prediction models require users to specify fixed values of assumed model performance metrics alongside target…
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…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
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…