Copertina

G-computation for increasing performances of clinical trials with individual randomization and binary response

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	de Keizer, Joe, Lenain, Rémi, Porcher, Raphaël, Zoha, Sarah, Chatton, Arthur, Foucher, Yohann
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting
di: Chatton, A., et al.
Pubblicazione: (2020)

Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes
di: Chatton, Arthur, et al.
Pubblicazione: (2023)

Evaluating virtual-control-augmented trials for reproducing treatment effect from original RCTs
di: Fernandes, Alex, et al.
Pubblicazione: (2025)

Bias reduction in g-computation for covariate adjustment in randomized clinical trials
di: Zhang, Xin, et al.
Pubblicazione: (2025)

Choosing Covariate Balancing Methods for Causal Inference: Practical Insights from a Simulation Study
di: Peyrot, Etienne, et al.
Pubblicazione: (2024)

Family-wise error rate control in clinical trials with overlapping populations
di: Luschei, Remi, et al.
Pubblicazione: (2025)

Optimal Bayesian predictive probability for delayed response in single-arm clinical trials with binary efficacy outcome
di: Yoshimoto, Takuya, et al.
Pubblicazione: (2024)

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

Estimating Complier Average Causal Effects with Mixtures of Experts
di: Grolleau, François, et al.
Pubblicazione: (2024)

Optimal treatment regimes for the net benefit of a treatment
di: Petit, François, et al.
Pubblicazione: (2025)

Constrained Markov decision processes for response-adaptive procedures in clinical trials with binary outcomes
di: Baas, Stef, et al.
Pubblicazione: (2024)

A unified Bayesian framework for interval hypothesis testing in clinical trials
di: Chakraborty, Abhisek, et al.
Pubblicazione: (2024)

Cognitive factor-based selection increases power in Alzheimer's dementia randomized clinical trials
di: Gallini, Julia, et al.
Pubblicazione: (2025)

Estimand framework and intercurrent events handling for clinical trials with time-to-event outcomes
di: Fang, Yixin, et al.
Pubblicazione: (2025)

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
di: Karlsson, Rickard, et al.
Pubblicazione: (2025)

Robust low-rank estimation with multiple binary responses using pairwise AUC loss
di: Mai, The Tien
Pubblicazione: (2026)

Fast computation of exact confidence intervals for randomized experiments with binary outcomes
di: Aronow, P. M., et al.
Pubblicazione: (2023)

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

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction
di: Stroh, Rémi, et al.
Pubblicazione: (2020)

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

Data reuse enables cost-efficient randomized trials of medical AI models
di: Nercessian, Michael, et al.
Pubblicazione: (2025)

Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials
di: Perrin, Valentine, et al.
Pubblicazione: (2024)

Is checking for sequential positivity violations getting you down? Try sPoRT!
di: Chatton, Arthur, et al.
Pubblicazione: (2024)

Covariate-adjusted win statistics in randomized clinical trials with ordinal outcomes
di: Cao, Zhiqiang, et al.
Pubblicazione: (2025)

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

Adaptive clinical trials based on design-optimal e-values with automatic curtailment: An application to single-arm trials with binary data
di: Baas, Stef, et al.
Pubblicazione: (2026)

Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning
di: Balzer, Laura B., et al.
Pubblicazione: (2025)

Cluster randomized trials designed to support generalizable inferences
di: Robertson, Sarah E., et al.
Pubblicazione: (2022)

Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials
di: Fang, Xi, et al.
Pubblicazione: (2026)

A robust score test in g-computation for covariate adjustment in randomized clinical trials leveraging different variance estimators via influence functions
di: Zhang, Xin, et al.
Pubblicazione: (2025)

Incorporating external data for analyzing randomized clinical trials: A transfer learning approach
di: Gu, Yujia, et al.
Pubblicazione: (2024)

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

What if we had built a prediction model with a survival super learner instead of a Cox model 10 years ago?
di: Chatton, Arthur, et al.
Pubblicazione: (2024)

Testing for similarity of dose response in multi-regional clinical trials
di: Dette, Holger, et al.
Pubblicazione: (2024)

Dynamic borrowing from historical controls via the synthetic prior with covariates in randomized clinical trials
di: Schwartz, Daniel E., et al.
Pubblicazione: (2024)

Random forests for binary geospatial data
di: Saha, Arkajyoti, et al.
Pubblicazione: (2023)

Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials
di: Gao, Chenyin, et al.
Pubblicazione: (2024)

Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
di: Van Lancker, Kelly, et al.
Pubblicazione: (2024)

Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
di: Richards, Jordan, et al.
Pubblicazione: (2022)

The MELODIC family for simultaneous binary logistic regression in a reduced space
di: de Rooij, Mark, et al.
Pubblicazione: (2021)