Wrapper for _CI_2x2 functions on Chapter 4.

the_2x2_table_CIs_ratio(n, alpha = 0.05)

Arguments

n

frequency matrix

alpha

type I error

Value

NULL. This function should be called for its printed output

Table orientation

In most study designs, rows designate a grouping of the data, for instance, into treatment or exposure groups, while the columns indicate a dichotomous outcome, usually with the first column representing the outcome of interest. This designation of rows and columns may not be relevant in all study designs, please see the introduction to chapter 4 for details.

See also

the_2x2_table_CIs_difference the_2x2_table_CIs_OR the_2x2_table_tests

Examples

# An RCT of high vs standard dose of epinephrine (Perondi et al., 2004)
the_2x2_table_CIs_ratio(perondi_2004)
#> Estimate of pi_1: 7 / 34 = 0.206
#> Estimate of pi_2: 1 / 34 = 0.029
#> Estimate of phi = pi_1 / pi_2: 7.000
#> 
#> Interval method                            95% CI      Log width
#> ----------------------------------------------------------------
#> Katz log                              0.910 to 53.870    4.081
#> Adjusted log                          0.924 to 27.052    3.377
#> Price-Bonett approximate Bayes        0.921 to 36.545    3.681
#> Inverse sinh                          1.167 to 41.983    3.583
#> Adjusted inverse sinh                 1.166 to 42.029    3.585
#> MOVER-R Wilson                        1.154 to 41.976    3.594
#> Miettinen-Nurminen asymptotic score   1.209 to 43.033    3.573
#> Koopman asymptotic score              1.221 to 42.576    3.552
#> ----------------------------------------------------------------

# The association between CHRNA4 genotype and XFS (Ritland et al., 2007)
the_2x2_table_CIs_ratio(ritland_2007)
#> Estimate of pi_1: 0 / 16 = 0.000
#> Estimate of pi_2: 15 / 72 = 0.208
#> Estimate of phi = pi_1 / pi_2: 0.000
#> 
#> Interval method                            95% CI      Log width
#> ----------------------------------------------------------------
#> Katz log                              0.000 to    Inf      Inf
#> Adjusted log                          0.009 to  2.251    5.530
#> Price-Bonett approximate Bayes        0.001 to  3.425    7.836
#> Inverse sinh                          0.000 to  1.152      Inf
#> Adjusted inverse sinh                 0.000 to  1.152      Inf
#> MOVER-R Wilson                        0.000 to  1.002      Inf
#> Miettinen-Nurminen asymptotic score   0.000 to  0.971      Inf
#> Koopman asymptotic score              0.000 to  0.962      Inf
#> ----------------------------------------------------------------