A method for sample size calculation via E‐value in the planning of observational studies. (20th August 2020)

• Record Type: Journal Article
• Authors: Fang, Yixin; He, Weili; Hu, Xiaofei; Wang, Hongwei
• Is Part Of: Pharmaceutical statistics. Volume 20:Number 1(2021)
• Page Start: 163
• Page End: 174
• Publication Date: 2020-08-20
• Subjects: causal inference -- observational studies -- power analysis -- real world data -- sample size calculation; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2064
• Languages: English
• ISSNs: 1539-1604

Abstract

Summary: Confounding adjustment plays a key role in designing observational studies such as cross‐sectional studies, case‐control studies, and cohort studies. In this article, we propose a simple method for sample size calculation in observational research in the presence of confounding. The method is motivated by the notion of E‐value, using some bounding factor to quantify the impact of confounders on the effect size. The method can be applied to calculate the needed sample size in observational research when the outcome variable is binary, continuous, or time‐to‐event. The method can be implemented straightforwardly using existing commercial software such as the PASS software. We demonstrate the performance of the proposed method through numerical examples, simulation studies, and a real application, which show that the proposed method is conservative in providing a slightly bigger sample size than what it needs to achieve a given power.