Print method for sample_size_result objects
Source:R/print_summary_result.R
print.sample_size_result.RdDisplays the study design, sample size metrics, plain-text formula, LaTeX source, calculation process, and literature references. Parameter roles and methodological hints are included where available.
Usage
# S3 method for class 'sample_size_result'
print(x, ...)Examples
# Antihypertensive RCT: 37 patients per arm with these assumptions
res <- calculate("rct", "test", "mean", mean1 = 10, mean2 = 12, sd = 3)
print(res) # sample sizes, both formulas, calculation process, references
#> ────────────────────────────────────────────────────────────────────────────────
#> ✔ Sample Size Calculation Results
#> • Design type: "Two independent samples, mean comparison"
#> • Sample size per group: 37
#> • Total sample size: 74
#> Formula (plain text):
#> `n = 2 * ((Z_alpha/2 + Z_beta) * sd / (mean1 - mean2))^2`
#> Formula (LaTeX):
#> $$n = 2\left(\frac{Z_{\alpha/2} + Z_{\beta}}{\delta}\right)^2 = 2\left(\frac{(Z_{\alpha/2} + Z_{\beta}) \cdot \sigma}{\mu_1 - \mu_2}\right)^2$$
#> Calculation process:
#>
#> • Step 1: State hypotheses: H0: mu1 = mu2 vs H1: mu1 != mu2 (two-sided alpha =
#> 0.05, power = 0.80).
#> • Step 2: Calculate mean difference delta = |mu1 - mu2| = |10 - 12| = 2; common
#> SD sigma = 3.
#> • Step 3: Determine critical values: Z_(alpha/2) = 1.96 (for two-sided alpha =
#> 0.05), Z_beta = 0.8416 (for power = 0.80).
#> • Step 4: Compute initial sample size via normal approximation: n0 = 2 *
#> ((Z_(alpha/2) + Z_beta) * sigma / delta)^2 = 2 * ((1.96 + 0.8416) * 3 / 2)^2 =
#> 35.32.
#> • Step 5: Refine via exact two-sample Student's t-test power iteration
#> (accounting for degrees of freedom df = 2n - 2).
#> • Step 6: Round up to integer: n = 37 per group, total sample size N = 74.
#> Reference:
#> Chow, S. C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample Size Calculations in Clinical Research (3rd ed.). CRC Press. Chapter 3 (Comparing Means), Section 3.2.1 two-sample parallel design, Eq 3.11 (p. 48; the Student t refinement is Table 3.2, p. 49).
#>
#> Also: Machin et al. (2018) Eq 5.4, p. 69 (same formula with the Student t correction term).
#> ────────────────────────────────────────────────────────────────────────────────
#>