English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sample size calculation is a crucial step for planning a clinical study. A study that is too small leads to inconclusive results; a study that is too large is a waste of resources. Either case might be unethical. Furthermore, Bland (2009) called for a focus on the width of confidence intervals rather than the power of the test in sample size calculations. Indeed, many research projects aim to estimate a quantity rather than test a hypothesis, sample size calculation approaches for which are largely missing from other software packages. There are many software packages for hypothesis-based sample size calculation, such as Stata, PASS, G*Power, including many R packages, such as pwr (Champely, 2020) and TrialSize [(Zhang et al., 2020); see other packages detailed on the CRAN Clinical Trials taskview].

presize: An R-package for precision-based sample size calculation in clinical research