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{cicalc} contains a number of confidence intervals commonly used in the pharma industry. For a full list of available methods see the package index
You can install the development version of cicalc from GitHub with:
# install.packages("devtools")
devtools::install_github("GSK-Biostatistics/cicalc")A subsection of common CI’s available in this package are below:
library(cicalc)
# Example of CI for proportions
x <- c(
TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, FALSE, FALSE, FALSE
)
ci_prop_wald(x, conf.level = 0.9)
#>
#> ── Wald Confidence Interval without Continuity Correction ──────────────────────
#> • 5 responses out of 10
#> • Estimate: 0.5
#> • 90% Confidence Interval:
#> (0.2399, 0.7601)
ci_prop_wilson(x, correct = TRUE)
#>
#> ── Wilson Confidence Interval with continuity correction ───────────────────────
#> • 5 responses out of 10
#> • Estimate: 0.5
#> • 95% Confidence Interval:
#> (0.2366, 0.7634)
ci_prop_clopper_pearson(x)
#>
#> ── Clopper-Pearson Confidence Interval ─────────────────────────────────────────
#> • 5 responses out of 10
#> • Estimate: 0.5
#> • 95% Confidence Interval:
#> (0.1871, 0.8129)
ci_prop_agresti_coull(x)
#>
#> ── Agresti-Coull Confidence Interval ───────────────────────────────────────────
#> • 5 responses out of 10
#> • Estimate: 0.5
#> • 95% Confidence Interval:
#> (0.2366, 0.7634)
ci_prop_jeffreys(x)
#>
#> ── Jeffreys Interval ───────────────────────────────────────────────────────────
#> • 5 responses out of 10
#> • Estimate: 0.5
#> • 95% Confidence Interval:
#> (0.2235, 0.7765)
# Example of CI for difference of proportions
# Generate binary samples
responses <- expand(c(9, 3), c(10, 10))
arm <- rep(c("treat", "control"), times = c(10, 10))
# Calculate 95% confidence interval for difference in proportions
ci_prop_diff_mn(x = responses, by = arm)
#>
#> ── Miettinen-Nurminen Confidence Interval ──────────────────────────────────────
#> • 9/10 - 3/10
#> • Estimate: 0.6
#> • 95% Confidence Interval:
#> (0.17, 0.8406)
# Stratified
strat_df <- dplyr::tibble(
responses = expand(c(9, 3, 7, 2), c(10, 10, 10, 10)),
arm = rep(c("treat", "control"), 20),
strata = rep(c("stratum1", "stratum2"), times = c(20, 20))
)
ci_prop_diff_mn_strata(x = responses, by = arm, strata = strata, data = strat_df)
#>
#> ── Stratified Score Miettinen-Nurminen Confidence Interval ─────────────────────
#> • 12/20 - 9/20
#> • Weights: stratum1 = 5, stratum2 = 5
#> • Estimate: 0.15
#> • 95% Confidence Interval:
#> (-0.1606, 0.4338)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.