Cover Image

Non-parametric assessment of the calibration of individualized treatment effects

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sadatsafavi, Mohsen, Hoogland, Jeroen, Debray, Thomas P. A., Petkau, John
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Identification of distributions for risks based on the first moment and c-statistic
by: Sadatsafavi, Mohsen, et al.
Published: (2024)

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

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

Evaluating Treatment Benefit Predictors using Observational Data: Contending with Identification and Confounding Bias
by: Xia, Yuan, et al.
Published: (2024)

Bayesian hierarchical models with calibrated mixtures of g-priors for assessing treatment effect moderation in meta-analysis
by: Wang, Qiao, et al.
Published: (2024)

Transporting treatment effects by calibrating large-scale observational outcomes
by: Li, Harrison H
Published: (2026)

Bayesian sample size calculations for external validation studies of risk prediction models
by: Sadatsafavi, Mohsen, 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)

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

Estimating treatment effects with a unified semi-parametric difference-in-differences approach
by: Thome, Julia C., et al.
Published: (2025)

Trustworthy assessment of heterogeneous treatment effect estimator
by: Gao, Zijun
Published: (2024)

Enhanced inference for distributions and quantiles of individual treatment effects in various experiments
by: Chen, Zhe, et al.
Published: (2024)

Scalable calibration of individual-based epidemic models through categorical approximations
by: Rimella, Lorenzo, et al.
Published: (2025)

Interactive identification of individuals with positive treatment effect while controlling false discoveries
by: Duan, Boyan, et al.
Published: (2021)

Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials
by: Li, Changjun, et al.
Published: (2026)

Hidden in Plain Sight: How Non-Collapsibility Biases Treatment Effects in (Network) Meta-Analysis
by: Campbell, Harlan, et al.
Published: (2026)

A studentized permutation test for the treatment effect in individual participant data meta-analysis
by: Tran, Phuc Thien, et al.
Published: (2025)

Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments
by: Martin, Axel, et al.
Published: (2024)

On regional treatment effect assessment using robust MAP priors
by: Zhang, Xin, et al.
Published: (2026)

Randomization-based Z-estimation for evaluating average and individual treatment effects
by: Qu, Tianyi, et al.
Published: (2024)

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials
by: Chen, Zhe, et al.
Published: (2023)

Augmented two-stage estimation for treatment crossover in oncology trials: Leveraging external data for improved precision
by: Campbell, Harlan, et al.
Published: (2024)

Non-parametric Replication of Instrumental Variable Estimates Across Studies
by: Zawadzki, Roy S., et al.
Published: (2024)

Nonparametric inference for sublevel-set probabilities of conditional average treatment effect functions
by: Munch, Anders, et al.
Published: (2026)

Non-parametric Bayesian inference via loss functions under model misspecification
by: Luo, Yu, et al.
Published: (2021)

Non-parametric Causal Inference in Dynamic Thresholding Designs
by: Ghosh, Aditya, et al.
Published: (2025)

In-sample calibration yields conformal calibration guarantees
by: Allen, Sam, 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)

Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
by: Wen, Lan, et al.
Published: (2026)

Non-parametric estimators of scaled cash flows
by: Bathke, T., et al.
Published: (2024)

Optimal individualized treatment regimes for survival data with competing risks
by: Zhou, Christina W., et al.
Published: (2024)

Computational methods for fast Bayesian model assessment via calibrated posterior p-values
by: Paganin, Sally, et al.
Published: (2023)

C-learning in estimation of optimal individualized treatment regimes for recurrent disease
by: Zhan, Zi-Shu, et al.
Published: (2025)

Non-parametric Hypothesis Tests for Distributional Group Symmetry
by: Chiu, Kenny, et al.
Published: (2023)

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments
by: Bian, Yuan, et al.
Published: (2026)

Hypothesizing an effect size by considering individual variation
by: Gelman, Andrew, et al.
Published: (2026)

Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution
by: Hou, Haoze, et al.
Published: (2025)

A semi-parametric model for assessing the effect of temperature on ice accumulation rate from Antarctic ice core data
by: Srivastava, Radhendushka, et al.
Published: (2023)

Multiple imputation for multilevel data with continuous and binary variables
by: Audigier, Vincent, et al.
Published: (2017)