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.
presize can be installed from CRAN in the usual manner:
You can install the development version of
install.packages('presize', repos = 'https://ctu-bern.r-universe.dev')
presize provides functions for
||Wilson, Agresti-Coull, exact, Wald (see Brown, Cai, and DasGupta 2001)|
||Score, variance stabilizing, exact, Wald (see Barker 2002)|
||Newcombe (Newcombe 1998), Miettinen-Nurminen (Miettinen and Nurminen 1985), Agresti-Caffo (Agresti and Caffo 2000), Wald|
||Gart, Wolff, independence smoothed logit (see Fagerland, Lydersen, and Laake 2015)|
||Koopman (Koopman 1984), Katz (Katz et al. 1978)|
||Rothman (Rothman and Greenland 2018)|
||Pearson, Kendall, Spearman (see Bonnett and Wright 2000)|
|Limit of agreement||
||Bland and Altman (1986)|
||Rotondi and Donner (2012)|
|Area under the curve||
||Hanley and McNeil (1982)|
|Negative likelilood ratio2||
||Simel, Samsa, and Matchar (1991)|
|Positive likelilood ratio2||
||Simel, Samsa, and Matchar (1991)|
|Generic likelilood ratio||
||Simel, Samsa, and Matchar (1991)|
1 Simple wrappers for
2 Wrappers for
prec_lr with values provided via sens and spec
Suppose we want to estimate the proportion of hospital admissions with diabetes. Diabetes has a prevalence of approximately 10% (Emerging Risk Factors Collaboration et al. (2010)). We assume a slightly higher proportion of diabetics, 15%, as diabetes is a risk factor for a wide range of conditions. We want to estimate the prevalence of diabetes to within 5% (plus/minus 2.5%). With
presize, this is simple. We use the
prec_prop (precision of a proportion) function and pass our 15% and 5% as arguments
library(presize) # load the package prec_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 783.5 up) patients to achieve the desired CI width. Disappointingly, we also know that we only have funds to collect the data from 600 patients. We wonder if 600 patients would yield sufficient precision - we could also accept a CI width of 6% (plus/minus 3%). In such a case, we can pass the arguments
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%. We are 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 a vector to one of the arguments, which could be used to create something analogous 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.
The package website, including more details on the functions, can be found here.
If you have a question, feel free to make a thread on the discussion page.
If you encounter a bug, please create an issue.
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 into the package. New functionality should have accompanying tests and pass continuous integration. See also the contributing guidelines.
presize was largely developed at CTU Bern, with collaboration from CTU Basel. Funding was provided by the Swiss Clinical Trial Organisation.
If you use
presize, please cite it in your publication as:
Haynes et al., (2021). presize: An R-package for precision-based sample size calculation in clinical research. Journal of Open Source Software, 6(60), 3118, https://doi.org/10.21105/joss.03118
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Barker, L. 2002. “A Comparison of Nine Confidence Intervals for a Poisson Parameter When the Expected Number of Events Is ≤ 5.” The Americal Statistician 56 (2): 85–89. https://doi.org/10.1198/000313002317572736.
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Emerging Risk Factors Collaboration, N Sarwar, P Gao, S R Seshasai, R Gobin, S Kaptoge, E Di Angelantonio, et al. 2010. “Diabetes Mellitus, Fasting Blood Glucose Concentration, and Risk of Vascular Disease: A Collaborative Meta-Analysis of 102 Prospective Studies.” Lancet 375 (9733): 2215–22. https://doi.org/10.1016/S0140-6736(10)60484-9.
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Koopman, P A R. 1984. “Confidence Intervals for the Ratio of Two Binomial Proportions.” Biometrics 40: 513–17. https://doi.org/10.2307/2531551.
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Rotondi, M A, and A Donner. 2012. “A Confidence Interval Approach to Sample Size Estimation for Interobserver Agreement Studies with Multiple Raters and Outcomes.” Journal of Clinical Epidemiology 65: 778–84. https://doi.org/10.1016/j.jclinepi.2011.10.019.
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