bi2by_ni_OR {EQUIVNONINF}R Documentation

Objective Bayesian test for noninferiority in the two-sample setting with binary data and the odds ratio as the parameter of interest

Description

Implementation of the construction described on pp. 179–181 of Wellek S (2010) Testing statistical hypotheses of equivalence and noninferiority. Second edition.

Usage

bi2by_ni_OR(N1,N2,EPS,SW,NSUB,ALPHA,MAXH)

Arguments

N1

size of sample 1

N2

size of sample 2

EPS

noninferiority margin to the deviation of the odds ratio from unity

SW

width of the search grid for determining the maximum of the rejection probability on the common boundary of the hypotheses

NSUB

number of subintervals for partitioning the range of integration

ALPHA

target significance level

MAXH

maximum number of interval halving steps to be carried out in finding the maximally admissible nominal level

Details

The program uses 96-point Gauss-Legendre quadrature on each of the NSUB intervals into which the range of integration is partitioned.

Value

N1

size of sample 1

N2

size of sample 2

EPS

noninferiority margin to the deviation of the odds ratio from unity

NSUB

number of subintervals for partitioning the range of integration

SW

width of the search grid for determining the maximum of the rejection probability on the common boundary of the hypotheses

ALPHA0

result of the search for the largest admissible nominal level

SIZE0

size of the critical region corresponding to alpha_0

SIZE_UNC

size of the critical region of the test at uncorrected nominal level

Author(s)

Stefan Wellek <stefan.wellek@zi-mannheim.de>
Peter Ziegler <peter.ziegler@zi-mannheim.de>

References

Wellek S: Statistical methods for the analysis of two-arm non-inferiority trials with binary outcomes. Biometrical Journal 47 (2005), 48–61.

Wellek S: Testing statistical hypotheses of equivalence and noninferiority. Second edition. Boca Raton: Chapman & Hall/CRC Press, 2010, Par. 6.6.2.

Examples

bi2by_ni_OR(10,10,1/3,.0005,10,.05,12)

[Package EQUIVNONINF version 1.0.2 Index]