Evidence map›Paper›PMID 30071022›Full record

ArticlePloS one2018

A discussion on significance indices for contingency tables under small sample sizes.

Natalia L Oliveira, Carlos A de B Pereira, Marcio A Diniz, Adriano Polpo

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Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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

4 authors.

Natalia L OliveiraDepartment of Statistics and Data Science, Carnegie Mellon Univesity, Pittsburgh, United States of America.
Carlos A de B PereiraDepartment of Statistics, University of Sao Paulo, Sao Paulo, Brazil.
Marcio A DinizDepartment of Statistics, Federal University of Sao Carlos, Sao Carlos, Brazil.
Adriano PolpoDepartment of Statistics, Federal University of Sao Carlos, Sao Carlos, Brazil.ORCID 0000-0002-5959-1808

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypothesis testing in contingency tables is usually based on asymptotic results, thereby restricting its proper use to large samples. To study these tests in small samples, we consider the likelihood ratio test (LRT) and define an accurate index for the celebrated hypotheses of homogeneity, independence, and Hardy-Weinberg equilibrium. The aim is to understand the use of the asymptotic results of the frequentist Likelihood Ratio Test and the Bayesian FBST (Full Bayesian Significance Test) under small-sample scenarios. The proposed exact LRT p-value is used as a benchmark to understand the other indices. We perform analysis in different scenarios, considering different sample sizes and different table dimensions. The conditional Fisher's exact test for 2 × 2 tables and the Barnard's exact test are also discussed. The main message of this paper is that all indices have very similar behavior, except for Fisher and Barnard tests that has a discrete behavior. The most powerful test was the asymptotic p-value from the likelihood ratio test, suggesting that is a good alternative for small sample sizes.

Indexed as

BenchmarkingData Interpretation, StatisticalModels, StatisticalBayes TheoremChi-Square DistributionHumansLikelihood FunctionsResearch DesignSample Size

Identifiers

PMID30071022
PMCPMC6071961

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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.