Calculates the sample size needed in a clinical trial based on study design and statistical parameters using standard formulas for hypothesis testing (Chow, S. 2017).
Usage
sample_size(
sample = c("one-sample", "two-sample"),
design = NULL,
outcome = c("mean", "proportion"),
type = c("equality", "equivalence", "non-inferiority", "superiority"),
alpha = 0.05,
beta = 0.2,
x1 = NULL,
x2 = NULL,
SD = NULL,
delta = NULL,
dropout = 0,
k = 1
)
# S3 method for class 'sample_size'
print(x, ...)Arguments
- sample
Character string indicating whether one or two samples need to be calculated. Options: "one-sample" or "two-sample".
- design
Character string indicating study design when sample = "two-sample". Options: "parallel" or "crossover". Default: NULL for one-sample tests.
- outcome
Character string indicating the type of outcome variable. Options: "mean" or "proportion".
- type
Character string indicating the type of hypothesis test. Options: "equality", "equivalence", "non-inferiority", or "superiority".
- alpha
Numeric parameter indicating the Type I error rate (significance level). Default: 0.05.
- beta
Numeric parameter indicating the Type II error rate (1 - power). Default: 0.20.
- x1
Numeric value of the mean or proportion for group 1 (treatment group).
- x2
Numeric value of the mean or proportion for group 2 (control group or reference value).
- SD
Numeric value indicating the standard deviation. Required for mean outcomes and crossover designs with proportion outcomes. Default: NULL.
- delta
Numeric value indicating the margin of clinical interest. Required for non-equality tests. Must be negative for non-inferiority and positive for superiority/equivalence. Default: NULL.
- dropout
Numeric value indicating the discontinuation rate expected in the study. Must be between 0 and 1. Default: 0.
- k
Numeric value indicating the allocation ratio (n1/n2) for two-sample tests. Default: 1.
- x
An object of class "sample_size".
- ...
Further arguments passed to or from other methods.
References
Chow, S.-C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample Size Calculations in Clinical Research (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315183084
Examples
# Two-sample parallel non-inferiority test for means with 10% expected dropout
sample_size(sample = 'two-sample', design = 'parallel', outcome = 'mean',
type = 'non-inferiority', x1 = 5.0, x2 = 5.0,
SD = 0.1, delta = -0.05, k = 1, dropout = 0.1)
#>
#> Sample Size Calculation
#>
#> Test type: non-inferiority
#> Design: parallel, two-sample
#> Outcome: mean
#> Alpha (α): 0.050
#> Beta (β): 0.200
#> Power: 80.0%
#>
#> Parameters:
#> x1 (treatment): 5.000
#> x2 (control/reference): 5.000
#> Difference (x1 - x2): 0.000
#> Standard Deviation (σ): 0.100
#> Allocation Ratio (k): 1.00
#> Delta (δ): -0.050
#> Dropout rate: 10.0%
#>
#> Required Sample Size
#> n1 = 56
#> n2 = 56
#> Total = 112
#>
#> Note: Sample size increased by 10.0% to account for potential dropouts.
#>
# One-sample equivalence test for means
sample_size(sample = "one-sample", outcome = "mean", type = "equivalence",
x1 = 0, x2 = 0, SD = 0.1, delta = 0.05)
#>
#> Sample Size Calculation
#>
#> Test type: equivalence
#> Design: one-sample
#> Outcome: mean
#> Alpha (α): 0.050
#> Beta (β): 0.200
#> Power: 80.0%
#>
#> Parameters:
#> x1 (treatment): 0.000
#> x2 (control/reference): 0.000
#> Difference (x1 - x2): 0.000
#> Standard Deviation (σ): 0.100
#> Delta (δ): 0.050
#>
#> Required Sample Size
#> n = 35
#> Total = 35
#>