Evidence map›Paper›PMID 42742386›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2026

Estimation of Underlying Normal Distribution Parameters From Dichotomized Data.

Zhaoze Liu, Longwen Shang, Mary Lesperance, Shuiqing Zhou, Xuekui Zhang

Abstract read
PubMed Full text at PMC Publisher
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

5 authors.

Zhaoze LiuDepartment of Mathematics and Statistics, University of Victoria, British Columbia, Canada.ORCID https://orcid.org/0009-0003-1722-916X
Longwen ShangDepartment of Mathematics and Statistics, University of Victoria, British Columbia, Canada.
Mary LesperanceDepartment of Mathematics and Statistics, University of Victoria, British Columbia, Canada.
Shuiqing ZhouInnovation Research Institute of Zhejiang University of Technology, Shengzhou, China.
Xuekui ZhangDepartment of Mathematics and Statistics, University of Victoria, British Columbia, Canada.ORCID https://orcid.org/0000-0003-4728-2343

Funding

Canada Research Chair CRC-2021-00232Michael Smith Health Research BC Scholar SCH-2022-2553Natural Sciences and Engineering Research Council of Canada RGPIN-2022-03050
6 · The paper itself

Abstract

Accurately estimating the parameters of a continuous distribution from dichotomized or aggregated data is a common problem in biomedical and environmental research. Many studies report only the proportion of subjects exceeding a threshold, without releasing individual-level measurements. To address this limitation, we develop a hierarchical binomial-probit modeling framework to reconstruct the parameters of an underlying normal distribution from threshold-based data. The framework considers two principal settings. In the fixed-mean model, all studies are assumed to share a common mean and variance, and the parameters

Indexed as

BiometryBayes TheoremLinear ModelsMarkov ChainsModels, StatisticalMonte Carlo MethodNormal DistributionBayesian MCMCdichotomized dataGLMGLMMhierarchical modelMLE

Identifiers

PMID42742386
PMCPMC13576801

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.