Power Calculation for Comparing Two Negative Binomial Rates
Compute sample size or power for comparing two negative binomial rates.
power_NegativeBinomial(n1 = NULL, n2 = NULL, mu1 = NULL, mu2 = NULL,
sig.level = 0.05, power = NULL, duration = 1, theta = NULL,
equal.sample = TRUE, alternative = c("two.sided", "one.sided"),
approach = 3)n1 |
sample size in group 1, or sample size in each group if |
n2 |
sample size in group 2 |
mu1 |
expected rate of events per time unit for group 1 |
mu2 |
expected rate of events per time unit for group 2 |
sig.level |
significance level (Type I error probability) |
power |
power of test (1 minus Type II error probability) |
duration |
(average) treatment duration |
theta |
theta parameter of negative binomial distribution; see rnegbin |
equal.sample |
equal sample sizes for two groups, see details |
alternative |
one- or two-sided test |
approach |
1, 2, or 3, see details |
Exactly one of the parameters n1, n2, and power must be passed as NULL, and that parameter is determined from the others.
If equal.sample = TRUE is used, N in output will denote the number in each group.
The computations are based on the formulas given in Zhu and Lakkis (2014).
For approach 1, the events rates under null hypothesis are set as mu1 for both group 1 and group 2.
For approach 2, the events rates under null hypothesis are set as mu1 and mu2 for group 1 and group 2, respectively.
For approach 3, the events rates under null hypothesis are set as the maximum likelihood estimation of the overall events rate for both group 1 and group 2.
Object of class "power.htest", a list of the arguments (including the computed one) augmented with note and method elements.
H. Zhu and H. Lakkis (2014). Sample size calculation for comparing two negative binomial rates. Statistics in Medicine, 33:376-387.
# calculate power, equal sizes power_NegativeBinomial(n1 = 20, mu1 = 1, mu2 = 2, theta = 0.8) # calculate power, unequal sizes power_NegativeBinomial(n1 = 80, n2 = 40, mu1 = 1, mu2 = 2, theta = 0.8) # calculate n power_NegativeBinomial(mu1 = 1, mu2 = 2, theta = 0.8, power = 0.8)
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