Cover Image

Is 1:1 Always Most Powerful? Why Careful Determination of Allocation Ratios Matters in Trial Design

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pin, Lukas, Baas, Stef, Robertson, David S., Villar, Sofía S.
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

Informed Burn-In Decisions in RAR: Harmonizing Adaptivity and Inferential Precision Based on Study Setting
by: Pin, Lukas, et al.
Published: (2025)

A computational method for type I error rate control in power-maximizing response-adaptive randomization
by: Baas, Stef, et al.
Published: (2025)

A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive randomization?
by: Tang, Edwin Y. N., et al.
Published: (2025)

Thompson, Ulam, or Gauss? Multi-criteria recommendations for posterior probability computation methods in Bayesian response-adaptive trials
by: Kaddaj, Daniel, 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)

An integer programming-based approach to construct exact two-sample binomial tests with maximum power
by: Baas, Stef, et al.
Published: (2025)

Safety-Driven Response Adaptive Randomisation: An Application in Non-inferiority Oncology Trials
by: Chiaruttini, Maria Vittoria, et al.
Published: (2025)

Operationalizing Allocation Probability Tests: Practical Guidance on Optimized Implementation for Power and Robustness
by: Zetterstrom, Stina, et al.
Published: (2026)

Revisiting Optimal Allocations for Binary Responses: Insights from Considering Type-I Error Rate Control
by: Pin, Lukas, et al.
Published: (2025)

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

Bayesian Covariance Structure Modeling of Multi-Way Nested Data
by: Baas, Stef, et al.
Published: (2022)

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

The Win Ratio at the Design Stage of Clinical Trials
by: Kronthaler, David, et al.
Published: (2025)

Implementing Response-Adaptive Randomisation in Stratified Rare-disease Trials: Design Challenges and Practical Solutions
by: Das, Rajenki, et al.
Published: (2024)

Predicting missing values: A good idea?
by: van Buuren, Stef
Published: (2026)

BMW: Bayesian Model-Assisted Adaptive Phase II Clinical Trial Design for Win Ratio Statistic
by: Zhu, Di, et al.
Published: (2026)

An Eigengap Ratio Test for Determining the Number of Communities in Network Data
by: Wu, Yujia, et al.
Published: (2024)

Random Indicator Imputation for Missing Not At Random Data
by: Jolani, Shahab, et al.
Published: (2024)

Robust Estimation of Moderated Causal Excursion Odds Ratio in Micro-Randomized Trials
by: Yu, Jiaxin, et al.
Published: (2025)

Non-collapsibility and Built-in Selection Bias of Hazard Ratio in Randomized Controlled Trials
by: Bian, Helen, et al.
Published: (2024)

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials
by: Bing, Ante, et al.
Published: (2025)

LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
by: van Buuren, Stef
Published: (2026)

Randomization-based Inference for MCP-Mod
by: Pin, Lukas, et al.
Published: (2024)

HIMCE: High-dimensional multiple imputation via covariance-mode updating for neuroimaging and spatiotemporal blocks
by: Huang, Hsin-Hsiung, et al.
Published: (2026)

Improving the Balance of Unobserved Covariates From Information Theory in Multi-Arm Randomization with Unequal Allocation Ratio
by: Ma, Xingjian, et al.
Published: (2023)

Active-Controlled Trial Design for HIV Prevention Trials with a Counterfactual Placebo
by: Gao, Fei, et al.
Published: (2024)

Most Powerful Test with Exact Family-Wise Error Rate Control: Necessary Conditions and a Path to Fast Computing
by: Dubey, Prasanjit, et al.
Published: (2025)

Externally Valid Policy Evaluation Combining Trial and Observational Data
by: Ek, Sofia, et al.
Published: (2023)

Comparing HIV Vaccine Immunogenicity across Trials with Different Populations and Study Designs
by: Jin, Yutong, et al.
Published: (2024)

Studying Optimal Designs for Multivariate Crossover Trials
by: Niphadkar, Shubham, et al.
Published: (2025)

Design of Bayesian Clinical Trials with Clustered Data
by: Hagar, Luke, et al.
Published: (2025)

Randomization Inference For the Always-Reporter Average Treatment Effect
by: Chang, Haoge, et al.
Published: (2026)

The Optimality of Blocking Designs in Equally and Unequally Allocated Randomized Experiments with General Response
by: Azriel, David, et al.
Published: (2022)

MUCE: Bayesian Hierarchical Modeling for the Design and Analysis of Phase 1b Multiple Expansion Cohort Trials
by: Lyu, Jiaying, et al.
Published: (2020)

Safe, Always-Valid Alpha-Investing Rules For Doubly Sequential Online Inference
by: Yao, Zeyu, et al.
Published: (2025)

Synergy-Informed Design of Platform Trials for Combination Therapies
by: Xi, Nan Miles, et al.
Published: (2025)

Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
by: Schochet, Peter Z., et al.
Published: (2020)

Block Designs that Provide Optimal Power in the Cochran-Mantel-Haenszel Test
by: Azriel, David, et al.
Published: (2025)

Kernel Model Validation: How To Do It, And Why You Should Care
by: Graziani, Carlo, et al.
Published: (2025)

Behavioral Carry-Over Effect and Power Consideration in Crossover Trials
by: Shi, Danni, et al.
Published: (2023)