Variable selection in modelling clustered data via within-cluster resampling.

Shangyuan Ye, Tingting Yu, Daniel A Caroff, Susan S Huang, Bo Zhang, Rui Wang

Abstract read

In one paragraph

Article in The Canadian journal of statistics = Revue canadienne de statistique, 2025. 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

–field-weighted citation impact

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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shangyuan YeBiostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Oregon, U.S.A.ORCID 0000-0002-7504-8215

Tingting YuDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Massachusetts, U.S.A.

Daniel A CaroffDepartment of Infectious Diseases, Lahey Hospital and Medical Center, Massachusetts, U.S.A.

Susan S HuangDivision of Infectious Diseases, University of California Irvine School of Medicine, California, U.S.A.

Bo ZhangDepartment of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital and Harvard Medical School, Massachusetts, U.S.A.

Rui WangDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Massachusetts, U.S.A.ORCID 0000-0001-5007-193X

Funding

AHRQ HHS R01 HS027791

6 · The paper itself

Abstract

In many biomedical applications, there is a need to build risk-adjustment models based on clustered data. However, methods for variable selection that are applicable to clustered discrete data settings with a large number of candidate variables and potentially large cluster sizes are lacking. We develop a new variable selection approach that combines within-cluster resampling techniques with penalized likelihood methods to select variables for high-dimensional clustered data. We derive an upper bound on the expected number of falsely selected variables, demonstrate the oracle properties of the proposed method, and evaluate the finite sample performance of the method through extensive simulations. We illustrate the proposed approach using a colon surgical site infection data set consisting of 39,468 individuals from 149 hospitals to build risk-adjustment models that account for both the main effects of various risk factors and their two-way interactions.

Indexed as

Clustered datastability selectionvariable selectionwithin-cluster resampling

Identifiers

PMID40040799

DOI10.1002/cjs.11824

PMCPMC11878247

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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 OpenQuestion graph.