Sample size estimation and power analysis for clinical research studies
Open accessAbstract
Determining the optimal sample size for a study assures an adequate power to detect statistical significance. Hence, it is a critical step in the design of a planned research protocol. Using too many participants in a study is expensive and exposes more number of subjects to procedure. Similarly, if study is underpowered, it will be statistically inconclusive and may make the whole protocol a failure. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for single group mean, survey type of studies, 2 group studies based on means and proportions or rates, correlation studies and for case-control for assessing the categorical outcome are presented in detail.
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Understanding rates of eligibility in a population helps power and inform future trials.
“Understanding rates of eligibility in a population helps power and inform future trials, 26”
From Wellbeing After Stroke (WAterS): Feasibility Testing of a Co-developed Acceptance and Commitment Therapy Intervention to Support Psychological Adjustment After Stroke. (2024) · cites this paper as [26]
The Introduction’s “Withdrawals, missing data and losses to follow-up” section says recruitment estimates should account for eligible people who decline participation, and that estimates can come from comparable populations or pilot studies. It does not address the rate at which people in a population meet a trial’s eligibility criteria; its discussion concerns participation and attrition among eligible subjects. (LLM, conf=0.82)
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- Authors
- Suresh, K., & Chandrashekara, S.
- Affiliations
- National Institute of Animal Nutrition and Physiology (IN)
- Department of Immunology and Reumatology, ChanRe Rheumatology and Immunology Center and Research, Bangalore, India
- Venue
- Journal of Human Reproductive Sciences
- Published
- 2012-01
- Volume
- 5
- Issue
- 1
- Pages
- 7-13
- ISSN
- 0974-1208 (ISSN-L)
- 1998-4766
- PMC
- PMC3409926
- OpenAlex
- W2038287973
- Cited by
- 1,024
- References
- 20
- Citations by year
- 2026: 47 · 2025: 94 · 2024: 115 · 2023: 98 · 2022: 127 · 2021: 118 · 2020: 85 · 2019: 65 · 2018: 62 · 2017: 56 · 2016: 52 · 2015: 67 · 2014: 31 · 2013: 6
- FWCI
- 31.52
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2015-07-01)
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Journal of Human Reproductive Sciences (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- sample size determination, statistical power, clinical research design, two-group comparison, proportion, rates, correlation, case-control study, categorical outcomes, survey studies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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