| Title: | Sample Size Computation for Fixed N with Optimal Reward |
|---|---|
| Description: | Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided. |
| Authors: | Maurits Kaptein [aut, cre] |
| Maintainer: | Maurits Kaptein <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2025-01-28 03:08:48 UTC |
| Source: | https://github.com/cran/ssev |
Function to compute the optimal sample size for a comparison of two means (with equal or unequal variances) or proportions. Function returns the standard sample size for an RCT with the specified power, as well as the optimal sample size for a population of size N.
compute_sample_size(means = NULL, sds = NULL, proportions = NULL, N = Inf, power = 0.8, sig.level = 0.05, ties = 0.5, .verbose = FALSE, ...)compute_sample_size(means = NULL, sds = NULL, proportions = NULL, N = Inf, power = 0.8, sig.level = 0.05, ties = 0.5, .verbose = FALSE, ...)
means |
A vector of length 2 containing the (assumed) means of the two groups |
sds |
A vector containing the (assumed) standard deviations of the two groups. When only one element is supplied equal variances are assumed. |
proportions |
A vector of length 2 containing the (assumed) proportions of the two groups |
N |
Estimated population size |
power |
Desired power for the classical RCT |
sig.level |
Significance level of the test used (alpha) |
ties |
Probability of choosing the first group in case of a tie (i.e., H0 is not rejected) |
.verbose |
Whether or not verbose output should be provided, default FALSE |
... |
further arguments passed to or from other methods. |
An object of type ssev
compute_sample_size(means=c(0,1), sds=2, N=100) compute_sample_size(means=c(0,1), sds=2, N=10000, power=.9) compute_sample_size(means=c(0,1), sds=c(1,2), N=10000) compute_sample_size(proportions=c(.5,.7), N=5000)compute_sample_size(means=c(0,1), sds=2, N=100) compute_sample_size(means=c(0,1), sds=2, N=10000, power=.9) compute_sample_size(means=c(0,1), sds=c(1,2), N=10000) compute_sample_size(proportions=c(.5,.7), N=5000)
Comparing means with equal variances
ev_means_equal(n, N, means, sd, sig.level, ties)ev_means_equal(n, N, means, sd, sig.level, ties)
n |
Sample size per group |
N |
Population size (estimate) |
means |
Vector of estimated means |
sd |
Standard deviation of the groups (assumed equal) |
sig.level |
Significance level |
ties |
Tie-breaking probability |
A scalar indicating the expected mean reward per unit in the population
Comparing means with unequal variances
ev_means_unequal(n, N, means, sds, sig.level, ties)ev_means_unequal(n, N, means, sds, sig.level, ties)
n |
Sample size per group |
N |
Population size (estimate) |
means |
Vector of estimated means |
sds |
Vector of standard deviation of the groups |
sig.level |
Significance level |
ties |
Tie-breaking probability |
A scalar indicating the expected mean reward per unit in the population
Comparing proportions
ev_proportions(n, N, proportions, sig.level, ties)ev_proportions(n, N, proportions, sig.level, ties)
n |
Sample size per group |
N |
Population size (estimate) |
proportions |
Vector of two proportions |
sig.level |
Significance level |
ties |
Tie-breaking probability |
A scalar indicating the expected mean reward per unit in the population
Pretty printing of ssev object
## S3 method for class 'ssev' print(x, digits = getOption("digits"), ...)## S3 method for class 'ssev' print(x, digits = getOption("digits"), ...)
x |
Object of type ssev for pretty printing |
digits |
Standard number of digits for pretty printing, default is getOption("digits") |
... |
further arguments passed to or from other methods. |