Generalized McNemar's test

Performs a generalized McNemar's test to jointly compare sensitivity and specificity.

Usage

sesp.gen.mcnemar(tab)

Arguments

tab

An object of class `tab.paired`.

Value

A vector containing the test statistic and the p-value.

References

Lachenbruch P.A., Lynch C.J. (1998). Assessing screening tests: extensions of McNemar's test. Stat Med, 17(19): 2207-17.

See also

[tab.paired()], [read.tab.paired()] and [sesp.mcnemar()]

Examples

# Example 1:
data(Paired1) # Hypothetical study data
ftable(Paired1)
#>      y2   0   1
#> d y1           
#> 0 0     155  22
#>   1      53  31
#> 1 0      32  22
#>   1      78 319
paired.layout1 <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
print(paired.layout1)
#> Two binary diagnostic tests (paired design)
#> 
#> Test1: 'y1'
#> Test2: 'y2'
#> 
#> Diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.        319         22   341
#> Test2 neg.         78         32   110
#> Total             397         54   451
#> 
#> Non-diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.         31         22    53
#> Test2 neg.         53        155   208
#> Total              84        177   261
#> 
sesp.gen.mcnemar(paired.layout1)
#> test.statistic        p.value 
#>   4.417333e+01   2.557894e-10 

# Example 2 (from Lachenbruch and Lynch (1998)):
paired.layout2 <- read.tab.paired(
  d.a  = 850, d.b  = 40, d.c  = 60, d.d  =  50, 
  nd.a =  60, nd.b = 25, nd.c = 15, nd.d = 900, 
  testnames = c("T1", "T2")
)
print(paired.layout2)
#> Two binary diagnostic tests (paired design)
#> 
#> Test1: 'T1'
#> Test2: 'T2'
#> 
#> Diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.        850         40   890
#> Test2 neg.         60         50   110
#> Total             910         90  1000
#> 
#> Non-diseased:
#>            Test1 pos. Test1 neg. Total
#> Test2 pos.         60         25    85
#> Test2 neg.         15        900   915
#> Total              75        925  1000
#> 
sesp.gen.mcnemar(paired.layout2)
#> test.statistic        p.value 
#>     6.50000000     0.03877421