Evidence map›Paper›PMID 42681883›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2026

A Likelihood Approach to Proper Analysis of Secondary Outcomes in Matched Case-Control Studies.

Shanshan Liu, Guoqing Diao

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
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

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

2 authors.

Shanshan LiuDepartment of Biostatistics and Bioinformatics, George Washington University, Washington, DC, USA.ORCID https://orcid.org/0000-0002-3596-1827
Guoqing DiaoDepartment of Biostatistics and Bioinformatics, George Washington University, Washington, DC, USA.ORCID https://orcid.org/0000-0001-7304-9591

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Matched case-control studies are commonly employed in epidemiological research for their convenience and efficiency. Analysis of secondary outcomes can yield valuable insights into biological pathways and help identify genetic variants of importance. Naive analysis using standard statistical methods, such as least-squares regression for quantitative traits, can be misleading because they fail to account for unequal sampling induced by the case-control design and matching. In this paper, we propose novel statistical methods that appropriately reflect the study design and sampling scheme in the analysis of secondary outcome data. The new methods provide consistent estimation and accurate coverage probabilities for the confidence interval estimators. We demonstrate the advantages of the new methods through simulation studies and a real application with diabetes patients. R code implementing the proposed methods is publicly available.

Indexed as

BiometryCase-Control StudiesDiabetes MellitusHumansLikelihood Functionsempirical likelihoodmatched case‐controlprofile likelihoodsecondary outcomesemiparametric method

Identifiers

PMID42681883
PMCPMC13534970

What OpenQuestion holds

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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 OpenQuestion graph.