Evidence map›Paper›PMID 42380083›Full record

ArticleStatistics in medicine2026

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Zhilan Lou, Jun Shao, Ting Ye, Tuo Wang, Yanyao Yi, Yu Du

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PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhilan LouSchool of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China.
Jun ShaoDepartment of Statistics, University of Wisconsin, Madison, Wisconsin, USA.
Ting YeDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.ORCID https://orcid.org/0000-0001-6009-641X
Tuo WangGlobal Statistical Science, Eli Lilly and Company, Indianapolis, Indiana, USA.
Yanyao YiGlobal Statistical Science, Eli Lilly and Company, Indianapolis, Indiana, USA.ORCID https://orcid.org/0000-0003-1540-1862
Yu DuGlobal Statistical Science, Eli Lilly and Company, Indianapolis, Indiana, USA.

Funding

National Natural Science Foundation of China 12001483Zhejiang Provincial Philosophy and Social Science Planning Project 26NDJC128YB
6 · The paper itself

Abstract

We apply covariate adjustment to the Wilcoxon two sample statistic and Wilcoxon-Mann-Whitney test in comparing two treatments. The covariate adjustment through calibration not only improves efficiency in estimation/inference but also widens the application scope of the Wilcoxon two sample statistic and Wilcoxon-Mann-Whitney test to situations where covariate-adaptive randomization is used. We motivate how to adjust covariates to reduce variance, establish the asymptotic distribution of adjusted Wilcoxon two sample statistic, and provide explicitly the guaranteed efficiency gain. The asymptotic distribution of adjusted Wilcoxon two sample statistic is invariant to all commonly used covariate-adaptive randomization schemes so that a unified formula can be used in inference regardless of which covariate-adaptive randomization is applied.

Indexed as

Data Interpretation, StatisticalComputer SimulationHumansModels, StatisticalRandom AllocationStatistics, Nonparametricconfidence intervalscovariate‐adaptive randomizationcovariate calibrationinvariance of asymptotic distributionWilcoxon–Mann–Whitney test

Identifiers

PMID42380083
PMCPMC13349453

What Socratic holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.