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Treffer: An Epidemiologic Approach for Estimating Risk Reduction and Asymptotic Power on the Log-Difference Scale.

Title:
An Epidemiologic Approach for Estimating Risk Reduction and Asymptotic Power on the Log-Difference Scale.
Authors:
Efird JT; Cooperative Studies Program Coordinating Center, VA Boston Healthcare System, Lafayette City Center, 2 Avenue de Lafayette, Boston, MA 02111, USA.; Department of Radiation Oncology, School of Medicine, Case Western Reserve University, Cleveland, OH 44206, USA.
Source:
International journal of environmental research and public health [Int J Environ Res Public Health] 2025 May 01; Vol. 22 (5). Date of Electronic Publication: 2025 May 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE
Imprint Name(s):
Original Publication: Basel : MDPI, c2004-
MeSH Terms:
Risk Reduction Behavior*, Humans ; Epidemiologic Methods ; Sample Size
References:
Trials. 2014 Jul 03;15:264. (PMID: 24993581)
Eur J Epidemiol. 2011 Apr;26(4):253-4. (PMID: 21424218)
Int J Epidemiol. 2017 Apr 1;46(2):746-755. (PMID: 28025257)
Br J Clin Pharmacol. 2014 Jan;77(1):116-21. (PMID: 23617453)
J Clin Epidemiol. 2001 Apr;54(4):343-9. (PMID: 11297884)
Clin Trials. 2020 Oct;17(5):562-566. (PMID: 32666813)
Contemp Clin Trials. 2022 Feb;113:106656. (PMID: 34906747)
BMJ. 2003 Jan 25;326(7382):219. (PMID: 12543843)
Contributed Indexing:
Keywords: clinical trials; common referent-control; conditional independence; multiplicity adjustment; power; risk reduction; sample size
Entry Date(s):
Date Created: 20250528 Date Completed: 20250528 Latest Revision: 20250531
Update Code:
20250531
PubMed Central ID:
PMC12111250
DOI:
10.3390/ijerph22050719
PMID:
40427835
Database:
MEDLINE

Weitere Informationen

When comparing the efficacy or harmfulness of two groups (e.g., drugs, devices, assays, interventions, environmental toxins), it is important to minimize bias by making this comparison with respect to a common referent-control group, assuming random allocation. Under such a scenario, one can estimate risk reduction for a new therapy on a log-difference, relative effect scale. The current manuscript reviews the large-sample framework for this conditionally independent comparison and demonstrates how to estimate test power for a given sample size.