Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.36387

Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials

Shuoyang Wang·Wanyu Zhang·Lingli Yang·Kexuan Li
View original source ↗Read source document ↗Export metadata ↓

Abstract

In some randomized trials the primary outcome is costly or slow to measure, while auxiliary variables that predict it are available for everyone. The outcome can then be measured in a probability sample. We study an adaptive design in which the statistician who selects outcomes to measure is blinded to treatment assignment. Outcomes from an initial random sample are used to fit pooled models for the outcome and its residual variance. These set the sampling probabilities for the remaining participants and may be refitted as outcomes accumulate. After unblinding, arm means are estimated by augmented inverse probability weighting. The estimator is unbiased for the complete-data treatment difference for any working models, and a martingale central limit theorem gives Wald intervals under repeated updating. We bound the variance lost by estimating the sampling rule. The bound is linear in the error of the fitted residual variance, quadratic when the optimal probabilities are not truncated, and relates the initial sample size to the learning rate of the models. Relative to designs using treatment assignment, the blinded design loses a term due to unequal residual variances in the two arms and a term due to the conditional treatment effect, which is second order near the null. In simulations, coverage was near nominal. Adaptive sampling was 12% to 34% more efficient than simple random sampling and needed 10% to 26% fewer measured outcomes for the same precision. In a resampling study of an antifungal trial, adaptive sampling reduced the sampling variance by 39%.

Explore related subjects

stat.ME

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shuoyang Wang, Wanyu Zhang, Lingli Yang, Kexuan Li. 2026-09-28. Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials. https://arxiv.org/abs/2609.36387

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Delaunay Weighted Two-sample Test for High-dimensional Data by Incorporating Geometric Information

Two-sample hypothesis testing is a fundamental problem with various applications, which faces new challenges in the high-dimensional context. To mitigate the issue of the curse of dimensionality, high-dimensional data are typically assumed to lie on a low-dimensional manifold. To incorporate geometric information in the data, we propose to apply the Delaunay triangulation and develop the Delaunay weight to measure the geometric proximity among data points. In contrast to existing similarity measures that only utilize pairwise distances, the Delaunay weight can take both the distance and direction information into account. A detailed computation procedure is developed to learn the unknown manifold and approximate the Delaunay weight. We further propose a novel nonparametric test statistic using the Delaunay weight matrix. Asymptotic normality under the null and consistency under the alternative of the test statistic are developed. Applied to simulated data, the new test shows robustness to the learning of the unknown manifold and exhibits substantial power gain if the distributions differ in the principal directions of covariance matrices. The proposed test also detects significant differences on a real dataset of mice protein expression levels.

stat.ME↗
arXiv2026

Randomization Tests in Switchback Experiments

Switchback experiments assign an experimental unit, such as a market or a platform, to treatment or control over successive blocks of time. Inference can be challenging because these experiments often contain only a small number of randomized blocks, while outcomes may exhibit serial dependence, seasonality, and treatment effects that persist across periods. We develop a conditional randomization test for the null of no total treatment effect that is finite-sample valid under standard assumptions on temporal interference without requiring a parametric model for outcomes. We also develop randomization tests for these assumptions: a carryover test that assesses whether past assignments continue to affect outcomes beyond a prespecified horizon and a non-anticipation test that assesses whether future assignments affect current outcomes. For hypotheses about average treatment effects, we establish asymptotic validity of studentized randomization tests under additional regularity conditions. Finally, we derive power approximations and characterize the tradeoffs between experimental design choices, the number of informative randomized comparisons, and statistical power. Numerical experiments with stylized potential outcomes and a dynamic rideshare model illustrate finite-sample performance and implications for experimental design.

stat.ME↗
arXiv2026

Outcome-blinded sample size re-estimation for externally controlled single-arm trials using baseline covariates

Externally controlled single-arm trials provide an option when limited patient populations or ethical constraints make concurrent randomized controls impractical, including in rare diseases and investigator-initiated trials with limited recruitment. Standardization improves comparability by aligning external controls with the enrolled population, but does not by itself preserve planned power. Differences between anticipated and enrolled covariate distributions can change the precision of the standardized control estimate and leave a fixed-size trial underpowered. We propose an outcome-blinded, covariate-adaptive sample size re-estimation procedure that translates these changes in precision into updated recruitment targets. Historical-control data and accumulating active-arm baseline covariates are used to update external-control standardization and its estimated precision. Adaptation requires no active-arm outcomes and retains the prespecified clinically meaningful effect to be detected. We state sufficient conditions for type I error calibration and power after adaptive stopping, assess operating characteristics through simulations, and illustrate implementation using Alzheimer's Disease Cooperative Study data. In the primary simulations, adaptive recruitment restored power lost under standardized fixed designs, achieving at least the target power across the distribution-shift settings and outcome models examined. Type I error rates were close to nominal, although mild inflation remained with a smaller historical-control sample. The application illustrated how accumulating baseline information guided recruitment revisions. By adapting sample size to the precision of the population-standardized control estimate, the proposed procedure addresses a source of power loss that standardization alone does not resolve.

stat.ME↗