:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Odeh-Couvertier, Valerie, Zayas-Caban, Gabriel, Patterson, Brian, Cochran, Amy
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	https://arxiv.org/abs/2510.25052
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Combining missing data imputation and internal validation in clinical risk prediction models
by: Mi, Junhui, et al.
Published: (2024)

General measures of effect size to calculate power and sample size for Wald tests with generalized linear models
by: Cochran, Amy L, et al.
Published: (2025)

Designing and evaluating advanced adaptive randomised clinical trials: a practical guide
by: Granholm, Anders, et al.
Published: (2025)

Robust Power and Sample Size Calculations in Quasi-likelihood Models: Methods and Practice
by: Yuan, Shijie, et al.
Published: (2026)

A proxy-based approach for unmeasured confounding in electronic health records research
by: Kottler, Haley Colgate, et al.
Published: (2025)

Design-based theory for causal inference from adaptive experiments
by: Li, Xinran, et al.
Published: (2026)

Bayesian sample size calculations for external validation studies of risk prediction models
by: Sadatsafavi, Mohsen, et al.
Published: (2025)

Prediction meets causal inference: the role of treatment in clinical prediction models
by: van Geloven, Nan, et al.
Published: (2020)

Scoping review of methodology for aiding generalisability and transportability of clinical prediction models
by: Ploddi, Kritchavat, et al.
Published: (2024)

The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
by: van Geloven, Nan, et al.
Published: (2024)

A general sample size framework for developing or updating a clinical prediction model
by: Riley, Richard D, et al.
Published: (2025)

Expected value of sample information calculations for risk prediction model development
by: Safari, Abdollah, et al.
Published: (2024)

Valid model-free spatial prediction
by: Mao, Huiying, et al.
Published: (2020)

The design of selection experiments using a model-based approach
by: Cullis, Brian R, et al.
Published: (2026)

Moving beyond spatial and random cross-validation in environmental modelling: a call for prediction-domain adaptive evaluation
by: Linnenbrink, Jan, et al.
Published: (2026)

Progression to the mean: A practical Bayesian workflow for the development and deployment of clinical prediction models
by: Sadatsafavi, Mohsen, et al.
Published: (2026)

An adaptive design for optimizing treatment assignment in randomized clinical trials
by: Zhang, Wei, et al.
Published: (2025)

Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
by: Legha, Amardeep, et al.
Published: (2025)

Cross-validated risk scores adaptive enrichment (CADEN) design
by: Cherlin, Svetlana, et al.
Published: (2021)

Practical considerations for Gaussian Process modeling for causal inference quasi-experimental studies with panel data
by: Vega, Sofia L., et al.
Published: (2025)

Non-parametric inference on calibration of predicted risks
by: Sadatsafavi, Mohsen, et al.
Published: (2023)

Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
by: Son, Dongjae, et al.
Published: (2024)

Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)

Finite-sample adjustments for comparing clustered adaptive interventions using data from a clustered SMART
by: Pan, Wenchu, et al.
Published: (2024)

Modeling complex measurement error in microbiome experiments to estimate relative abundances and detection effects
by: Clausen, David S, et al.
Published: (2022)

The harms of class imbalance corrections for machine learning based prediction models: a simulation study
by: Carriero, Alex, et al.
Published: (2024)

A general Bayesian approach to design adaptive clinical trials with time-to-event outcomes
by: McGree, James M., et al.
Published: (2023)

Assessing covariate-adjusted risk differences in small-sample clinical trials
by: Schnuerch, Martin, et al.
Published: (2026)

Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
by: Michaelides, Damianos, et al.
Published: (2021)

Evaluating the impact of instrumental variables in propensity score models using synthetic and negative control experiments
by: Tian, Yuxi, et al.
Published: (2024)

Dynamic prediction of death risk given a renewal hospitalization process
by: Pérez-Izquierdo, Telmo J., et al.
Published: (2024)

Exact statistical analysis for response-adaptive clinical trials: A general and computationally tractable approach
by: Baas, Stef, et al.
Published: (2024)

What makes a study design quasi-experimental? The case of difference-in-differences
by: Renson, Audrey, et al.
Published: (2025)

Asset price movement prediction using empirical mode decomposition and Gaussian mixture models
by: Palma, Gabriel R., et al.
Published: (2025)

A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
by: Riley, Richard D, et al.
Published: (2024)

Simulation-based Bayesian predictive probability of success for interim monitoring of clinical trials with competing event data: two case studies
by: Micoli, Chiara, et al.
Published: (2024)

Regression adjustment in covariate-adaptive randomized experiments with missing covariates
by: Fu, Wanjia, et al.
Published: (2025)

A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes
by: Riley, Richard D, et al.
Published: (2025)

A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models -- Part 3: continuous outcomes
by: Whittle, Rebecca, et al.
Published: (2025)

The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
by: Barreñada, Lasai, et al.
Published: (2025)