presize

Bland (2009) recommended to base study sizes on the width of the confidence interval rather the power of a statistical test. The goal of presize is to provide functions for such precision based sample size calculations. For a given sample size, the functions will return the precision (width of the confidence interval), and vice versa.

Installation

presize can be installed from CRAN in the usual manner:

install.packages("presize")

You can install the development version of presize from github with:

# install.packages("remotes")
remotes::install_github("CTU-Bern/presize")

Note that remotes treats any warnings (e.g. that a certain package was built under a different version of R) as errors. If you see such an error, run the following line and try again:

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true")

Overview

presize will provide functions for

• descriptive statistics
  • mean (prec_mean)
  • proportion (prec_prop)
  • rate (prec_rate)
• absolute and relative differences
  • mean difference (prec_meandiff)
  • risk difference (prec_riskdiff)
  • odds ration (prec_or)
  • risk ratio (prec_riskratio)
  • rate ratio (prec_rateratio)
• correlation measures
  • correlation coefficient (prec_cor)
  • Cohens kappa (prec_kappa)
  • ICC (prec_icc)
  • limit of agreement from Bland Altman plot (prec_lim_agree)
• diagnostic measures
  • sens (prec_sens¹)
  • spec (prec_spec¹)
  • likelihood ratios (prec_lr)
    • positive likelihood ratio (prec_pos_lr²)
    • negative likelihood ratio (prec_neg_lr²)
  • AUC (prec_auc)

¹ Simple wrappers for prec_prop.

² Wrappers for prec_lr with values provided via sens and spec

Example

This is a basic example which shows you how to solve a common problem:

library(presize)

# calculate sample size for a proportion of 0.2, or 0.4 with a precision of 0.2
prec_prop(p = c(.2, .4), n = 10, method = "wilson")
#> 
#>      precision for a proportion with Wilson confidence interval. 
#> 
#>     p      padj  n conf.width conf.level        lwr       upr
#> 1 0.2 0.2832598 10  0.4531554       0.95 0.05668215 0.5098375
#> 2 0.4 0.4277533 10  0.5191459       0.95 0.16818033 0.6873262
#> 
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.

THe problem being addressed is ‘how wide is the confidence interval width given proportions of events of 20 and 40% and only 10 participants’.

Shiny app

An online interactive version of the package is available here. The app can also be launched locally via launch_presize_app() in RStudio.

Funding

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