Exact (version 3.3)

power.paired.test: Power Calculations for 2x2 Tables with Paired Samples

Description

Calculates the power of the design for known sample size and true probabilities.

Usage

power.paired.test(p12, p21, N, alternative = c("two.sided", "less", "greater"),
    alpha = 0.05, npNumbers = 100, np.interval = FALSE, beta = 0.001,
    method = c("uam", "ucm", "uamcc", "csm", "cm", "am", "amcc"),
    tsmethod = c("square", "central"),
    simulation = FALSE, nsim = 100,
    delta = 0, convexity = TRUE, useStoredCSM = TRUE)

Value

A list with class "power.htest" containing the following components:

N

The total sample size

p12, p21

The respective discordant probabilities

alpha

Significance level

power

Power of the test

alternative

A character string describing the alternative hypothesis

delta

Null hypothesis of the difference in proportion

method

A character string describing the method to determine more extreme tables

Arguments

p12

The probability of success in first group and failure in second group. This is the probability of the discordant pair x12

p21

The probability of failure in first group and success in second group. This is the probability of the discordant pair x21

N

The total sample size

alternative

Indicates the alternative hypothesis: must be either "two.sided", "less", or "greater"

alpha

Significance level

npNumbers

Number: The number of nuisance parameters considered

np.interval

Logical: Indicates if a confidence interval on the nuisance parameter should be computed

beta

Number: Confidence level for constructing the interval of nuisance parameters considered. Only used if np.interval=TRUE

method

Indicates the method for finding the more extreme tables: must be either "UAM", "UCM", "UAMCC", "CSM", "CM", "AM", or "AMCC"

tsmethod

A character string describing the method to implement two-sided tests

simulation

Logical: Indicates if the power calculation is exact or estimated by simulation

nsim

Number of simulations run. Only used if simulation=TRUE

delta

Number: null hypothesis of the difference in proportion

convexity

Logical: assumes convexity for interval approach. Only used if np.interval=TRUE

useStoredCSM

Logical: uses stored CSM ordering matrix. Only used if method="csm"

Author

Peter Calhoun

Details

The power calculations are for paired samples. All possible tables can be represented by an (N+1) x (N+1) matrix. There are two ways to calculate the power: simulate the tables under a trinomial distribution or determine the rejection region for all possible tables and calculate the exact power. The power calculations can be determined for any unconditional exact test in paired.exact.test, the Conditional McNemar's (CM) exact test, the Asymptotic McNemar's (AM) test, or Asymptotic McNemar's test with Continuity Correction (AMCC) (note: asymptotic tests are not exact tests). The power calculations utilize the convexity property, which greatly speeds up computation time (see paired.reject.region documentation).

References

Berger, R.L. and Sidik, K. (2003) Exact unconditional tests for 2 x 2 matched-pairs design. Statistical Methods in Medical Research, 12, 91--108

See Also

paired.reject.region

Examples

# Superiority power #
power.paired.test(p12=0.15, p21=0.45, N=40, method="UAM")
if (FALSE) {
# Ensure that the ExactData R package is available before running the CSM test.
if (requireNamespace("ExactData", quietly = TRUE)) {
power.paired.test(p12=0.15, p21=0.45, N=40, method="CSM")
}
}

# Non-inferiority power #
power.paired.test(p12=0.30, p21=0.30, N=80, method="UAM",
                  alternative="less", delta=0.2)