brkTest: Barnard-Rohmel-Kieser Test In nivm: Noninferiority Tests with Variable Margins

Description

A variable margin difference in proportion test for non-inferiority. The test is based on Barnard's test.

Usage

 1 brkTest(x1, n1, x2, n2, threshold = 0.2, delta = 0.1, control = brkControl())

Arguments

 x1 number of events in the control group n1 number of individuals in the control group x2 number of events in the test group n2 number of events in the test group threshold proportion in the control group associated with the threshold, above that threshold use a constant difference margin, below the threshold use a difference margin with a constant odds ratio. We use only continuous variable margins that meet at the threshold. delta difference in proportions at the threshold control list of parameters for algorithm control, see brkControl

Details

This test is labeled T4 in Rohmel and Keiser (2013).

Value

a list of class brk, with elements:

 statistic the threshold, delta (difference margin at threshold), and odds ratio at threshold data.name gives x1,x2,n1,n2 as a character string method description of test p.value one-sided p-value FullResults a list with 4 matrices, each n1+1 by n2+1 representing the total sample space. R=a matrix with logical values with TRUE elements representing the rejection region, its 'sig.level' attribute gives the significance level of the test; PVALbounds=a matrix of p-value bounds, pb; PVALsymbols=a matrix of symbols that describe the pb, '<=' means 'p<=pb', '=' means 'p=pb' and '>' means 'p>pb'; PVALUES=a matrix giving the p-value expression, e.g., 'p<=.00321' or 'p>0.025'.

Michael P. Fay

References

Rohmel, J, and Kieser, M (2013). "Investigations on non-inferiority - - the Food and Drug Administration draft guidance on treatments for nosocomial pneumonia as a case for exact tests for binomial proportions" Statistics in Medicine 32:2335-2348.

Examples

 1 2 3 x<-brkTest(3,8,0,6) x x\$FullResults\$PVALUES

Example output

Barnard-Rohmel-Kieser Test

data:  x1=3 n1=8 x2=0 n2=6
threshold = 0.2000, difference = 0.1000, odds ratio = 1.7143

One-sided p-value:
p>0.025
Note: to save computational time, only bound on p-value calculated.

For rejection region and p-values for any possible
result with these sample sizes, save output as x,
and see the list x\$FullResults
x2=0          x2=1          x2=2         x2=3         x2=4
x1=0 "p>0.025"     "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=1 "p>0.025"     "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=2 "p>0.025"     "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=3 "p>0.025"     "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=4 "p=0.011699"  "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=5 "p=0.003668"  "p>0.025"     "p>0.025"    "p>0.025"    "p>0.025"
x1=6 "p=0.000852"  "p=0.008467"  "p>0.025"    "p>0.025"    "p>0.025"
x1=7 "p<=0.000293" "p=0.002501"  "p=0.013915" "p>0.025"    "p>0.025"
x1=8 "p<=0.000293" "p<=0.000293" "p=0.001404" "p=0.006229" "p=0.024348"
x2=5      x2=6
x1=0 "p>0.025" "p>0.025"
x1=1 "p>0.025" "p>0.025"
x1=2 "p>0.025" "p>0.025"
x1=3 "p>0.025" "p>0.025"
x1=4 "p>0.025" "p>0.025"
x1=5 "p>0.025" "p>0.025"
x1=6 "p>0.025" "p>0.025"
x1=7 "p>0.025" "p>0.025"
x1=8 "p>0.025" "p>0.025"

nivm documentation built on May 2, 2019, 8:22 a.m.