coin (version 1.3-1)

malformations: Maternal Drinking and Congenital Sex Organ Malformation

Description

A subset of data from a study on the relationship between maternal alcohol consumption and congenital malformations.

Usage

malformations

Format

A data frame with 32574 observations on 2 variables.

consumption

alcohol consumption, an ordered factor with levels "0", "<1", "1-2", "3-5" and ">=6".

malformation

congenital sex organ malformation, a factor with levels "Present" and "Absent".

Details

Data from a prospective study undertaken to determine whether moderate or light drinking during the first trimester of pregnancy increases the risk for congenital malformations (Mills and Graubard, 1987). The subset given here concerns only sex organ malformation (Mills and Graubard, 1987, Tab. 4).

This data set was used by Graubard and Korn (1987) to illustrate that different choices of scores for ordinal variables can lead to conflicting conclusions. Zheng (2008) also used the data, demonstrating two different score-independent tests for ordered categorical data; see also Winell and Lindb<U+00E4>ck (2018).

References

Graubard, B. I. and Korn, E. L. (1987). Choice of column scores for testing independence in ordered \(2 \times K\) contingency tables. Biometrics 43(2), 471--476. 10.2307/2531828

Winell, H. and Lindb<U+00E4>ck, J. (2018). A general score-independent test for order-restricted inference. Statistics in Medicine 37(21), 3078--3090. 10.1002/sim.7690

Zheng, G. (2008). Analysis of ordered categorical data: Two score-independent approaches. Biometrics 64(4), 1276<U+2013>-1279. 10.1111/j.1541-0420.2008.00992.x

Examples

# NOT RUN {
## Graubard and Korn (1987, Tab. 3)

## One-sided approximative (Monte Carlo) Cochran-Armitage test
## Note: midpoint scores (p < 0.05)
midpoints <- c(0, 0.5, 1.5, 4.0, 7.0)
chisq_test(malformation ~ consumption, data = malformations,
           distribution = approximate(nresample = 1000),
           alternative = "greater",
           scores = list(consumption = midpoints))

## One-sided approximative (Monte Carlo) Cochran-Armitage test
## Note: midrank scores (p > 0.05)
midranks <- c(8557.5, 24375.5, 32013.0, 32473.0, 32555.5)
chisq_test(malformation ~ consumption, data = malformations,
           distribution = approximate(nresample = 1000),
           alternative = "greater",
           scores = list(consumption = midranks))

## One-sided approximative (Monte Carlo) Cochran-Armitage test
## Note: equally spaced scores (p > 0.05)
chisq_test(malformation ~ consumption, data = malformations,
           distribution = approximate(nresample = 1000),
           alternative = "greater")

# }
# NOT RUN {
## One-sided approximative (Monte Carlo) score-independent test
## Winell and Lindbaeck (2018)
(it <- independence_test(malformation ~ consumption, data = malformations,
                         distribution = approximate(nresample = 1000,
                                                    parallel = "snow",
                                                    ncpus = 8),
                         alternative = "greater",
                         xtrafo = function(data)
                             trafo(data, ordered_trafo = zheng_trafo)))

## Extract the "best" set of scores
ss <- statistic(it, type = "standardized")
idx <- which(ss == max(ss), arr.ind = TRUE)
ss[idx[1], idx[2], drop = FALSE]
# }