binom_sample_size {adaptDiag} | R Documentation |
Calculate the minimum number of samples required for a one-sided exact binomial test to distinguish between two success probabilities with specified alpha and power.
binom_sample_size(alpha = 0.05, power = 0.9, p0 = 0.9, p1 = 0.95)
alpha |
scalar. The desired false positive rate (probability of
incorrectly rejecting the null). Must be be between 0 and 1. Default value
is |
power |
scalar. The the minimum probability of correctly rejects the null when the alternate is true. |
p0 |
scalar. The expected proportion of successes under the null. |
p1 |
scalar. The proportion of successes under the alternate hypothesis. |
This is a one-sided function, such that p_0 < p_1
. It
determines the minimum sample size to evaluate the hypothesis test:
H_0: \, p_1 \le p_0, \, vs.
H_1: \, p_1 > p_0
A list containing the required sample size and the number of successful trials required.
Chow S-C, Shao J, Wang H, Lokhnygina Y. (2017) Sample Size Calculations in Clinical Research, Boca Raton, FL: CRC Press.
# The minimum number of reference positive cases required to demonstrate
# the true sensitivity is >0.7, assuming that the true value is 0.824, with
# 90% power is
binom_sample_size(alpha = 0.05, power = 0.9, p0 = 0.7, p1 = 0.824)
# With a sample size of n = 104, if the true prevalence is 0.2, we would
# require a sample size of at least n = 520 randomly sampled subjects to
# have adequate power to demonstrate the sensitivity of the new test.
# The minimum number of reference negative cases required to demonstrate
# the true specificity is >0.9, assuming that the true value is 0.963, with
# 90% power is
binom_sample_size(alpha = 0.05, power = 0.9, p0 = 0.9, p1 = 0.963)
# The proposed total sample size of n = 520 would be sufficient to
# demonstrate both endpoint goals are met.