Cited by 496Statistical Methods in EpidemiologyStatistical Methods in Clinical TrialsMeta-analysis and systematic reviews

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Small studies: strengths and limitations

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Hackshaw, A.·2008·European Respiratory Journal

DOI 10.1183/09031936.00136408

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Abstract

A large number of clinical research studies are conducted, including audits of patient data, observational studies, clinical trials and those based on laboratory analyses. While small studies can be published over a short time-frame, there needs to be a balance between those that can be performed quickly and those that should be based on more subjects and hence may take several years to complete. The present article provides an overview of the main considerations associated with small studies. The definition of “small” depends on the main study objective. When simply describing the characteristics of a single group of subjects, for example the prevalence of smoking, the larger the study the more reliable the results. The main results should have 95% confidence intervals (CI), and the width of these depend directly on the sample size: large studies produce narrow intervals and, therefore, more precise results. A study of 20 subjects, for example, is likely to be too small for most investigations. For example, imagine that the proportion of smokers among a particular group of 20 individuals is 25%. The associated 95% CI is 9–49. This means that the true prevalence in these subjects generally is anywhere between a low or high value, which is not a useful result. When comparing characteristics between two or more groups of subjects ( e.g. examining risk factors or treatments for disease), the size of the study depends on the magnitude of the expected effect size, which is usually quantified by a relative risk, odds ratio, absolute risk difference, hazard ratio, or difference between two means or medians. The smaller the true-effect size, the larger the study needs to be 1, 2. This is because it is more difficult to distinguish between a real effect and random variation. Consider mortality as the end-point in a …

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“…(Reilly et al. 2009), but the extent to which these differences can be attributed to the exercises selected in each program remains uncertain, due to the limited sample size, which may reduce the precision of effect estimates, thereby limiting the interpretation of the findings (Hackshaw 2008).”
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Authors: Hackshaw, A.
Affiliations: Cancer Research UK (GB)
Corresponding author: Hackshaw, A.
ORCID: Hackshaw, A.
Venue: European Respiratory Journal
Publisher: European Respiratory Society (ERS)
Published: 2008-10-31
Volume: 32
Issue: 5
Pages: 1141-1143
ISSN: 0903-1936 (ISSN-L)
1399-3003
DOI: 10.1183/09031936.00136408
OpenAlex: W2097412619
Cited by: 496
References: 7
Citations by year: 2026: 29 · 2025: 36 · 2024: 37 · 2023: 56 · 2022: 57 · 2021: 86 · 2020: 51 · 2019: 38 · 2018: 24 · 2017: 20 · 2016: 20 · 2015: 17 · 2014: 9 · 2013: 5 · 2012: 5
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: European Respiratory Journal (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: small sample size, sample size determination, confidence intervals, statistical power, effect size, clinical research design, research limitations, prevalence estimation
MeSH terms: Antineoplastic Agents, Clinical Trials as Topic, Data Interpretation, Statistical, Diet Therapy, Humans, Lung Neoplasms, Research Design, Risk Factors, Smoking, Thalidomide, Time Factors, Reproducibility of Results, Weight Loss, Sample Size, Small Cell Lung Carcinoma

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