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Bland (2009) recommended tobase study sizes on the width of the confidence interval rather thepower of a statistical test. The goal ofpresize is to providefunctions for such precision based sample size calculations. For a givensample size, the functions will return the precision (width of theconfidence interval), and vice versa.

presize can be installed from CRAN in the usual manner:

install.packages("presize")

You can install the development version ofpresize with:

install.packages('presize',repos= c('https://ctu-bern.r-universe.dev','https://cloud.r-project.org'))

presize provides functions for

¹ Simple wrappers forprec_prop.

² Wrappers forprec_lr with values provided via sens andspec

Suppose we want to estimate the proportion of hospital admissions withdiabetes. Diabetes has a prevalence of approximately 10% (Emerging RiskFactors Collaboration et al. (2010)). We assume a slightly higherproportion of diabetics, 15%, as diabetes is a risk factor for a widerange of conditions. We want to estimate the prevalence of diabetes towithin 5% (plus/minus 2.5%). Withpresize, this is simple. We use theprec_prop (precision of a proportion) function and pass our 15% and 5%as argumentsp andconf.width:

library(presize)# load the packageprec_prop(p=0.15,conf.width=0.05)#> Warning in prec_prop(p = 0.15, conf.width = 0.05): more than one method was#> chosen, 'wilson' will be used#>#>      sample size for a proportion with Wilson confidence interval.#>#>      p      padj        n conf.width conf.level       lwr       upr#> 1 0.15 0.1517077 783.4897       0.05       0.95 0.1267077 0.1767077#>#> NOTE: padj is the adjusted proportion, from which the ci is calculated.

In the n column, we see that we would need to ask 784 (rounding 783.5up) patients to achieve the desired CI width. Disappointingly, we alsoknow that we only have funds to collect the data from 600 patients. Wewonder if 600 patients would yield sufficient precision - we could alsoaccept a CI width of 6% (plus/minus 3%). In such a case, we can pass theargumentsp andn.

prec_prop(p=0.15,n=600)#> Warning in prec_prop(p = 0.15, n = 600): more than one method was chosen,#> 'wilson' will be used#>#>      precision for a proportion with Wilson confidence interval.#>#>      p      padj   n conf.width conf.level       lwr       upr#> 1 0.15 0.1522266 600 0.05713404       0.95 0.1236596 0.1807936#>#> NOTE: padj is the adjusted proportion, from which the ci is calculated.

Now we see that with 600 patients, the CI would have a width of 5.7%. Weare happy with this and continue planning our study with those values.All of the functions listed in Table 1 can be used similarly.

We can also look at a range of scenarios simulatenously by passing avector to one of the arguments, which could be used to create somethinganalogous to a power curve:

prec_prop(p=0.15,n= seq(600,800,50))#> Warning in prec_prop(p = 0.15, n = seq(600, 800, 50)): more than one method was#> chosen, 'wilson' will be used#>#>      precision for a proportion with Wilson confidence interval.#>#>      p      padj   n conf.width conf.level       lwr       upr#> 1 0.15 0.1522266 600 0.05713404       0.95 0.1236596 0.1807936#> 2 0.15 0.1520563 650 0.05489329       0.95 0.1246097 0.1795030#> 3 0.15 0.1519102 700 0.05289705       0.95 0.1254617 0.1783588#> 4 0.15 0.1517835 750 0.05110386       0.95 0.1262316 0.1773355#> 5 0.15 0.1516726 800 0.04948148       0.95 0.1269319 0.1764133#>#> NOTE: padj is the adjusted proportion, from which the ci is calculated.

An online interactive version of the package is availablehere. The app can also be launchedlocally vialaunch_presize_app() in RStudio.

The package website, including more details on the functions, can befoundhere.

If you have a question, feel free to make a thread on thediscussion page.

If you encounter a bug, please create anissue.

Contributions topresize are welcome. If you have ideas, open anissue or adiscussionthread on GitHub.

If you want to contribute code, please feel free to fork the repository,make your changes and make a pull request to have them integrated intothe package. New functionality should have accompanying tests and passcontinuous integration. See also thecontributingguidelines.

presize was largely developed at CTU Bern, with collaboration from CTUBasel. Funding was provided by the Swiss Clinical Trial Organisation.

If you usepresize, please cite it in your publication as:
Haynes et al., (2021). presize: An R-package for precision-based samplesize calculation in clinical research. Journal of Open Source Software,6(60), 3118,https://doi.org/10.21105/joss.03118

The package logo was created withggplot2 andhexSticker with iconsfromFont Awesome (via theemojifontpackage).