distdichoigen {distdichoR}

normal, skew-normal or gamma distributed data (immediate form)

Description

Immediate form of the distributional method for dichotomising normal, skew normal or gamma distributed data (based on Sauzet et al. 2015).

Usage

distdichoigen(n1, m1, s1, n2, m2, s2, alpha = 1, cp = 0, tail = c("lower",
  "upper"), conf.level = 0.95, dist = c("normal", "sk_normal", "gamma"))

Arguments

n1

A number specifying the number of observations in the exposed group.

m1

A number specifying the mean of the exposed group.

s1

A number specifying the standard deviation of the exposed group.

n2

A number specifying the number of observations in the unexposed (reference) group.

m2

A number specifying the mean of the unexposed (reference) group.

s2

A number specifying the standard deviation of the unexposed (reference) group.

alpha

A numeric value specifying further parameter of the skew normal / gamma distribution.

cp

A numeric value specifying the cut point under which the distributional proportions are computed.

tail

A character string specifying the tail of the distribution in which the proportions are computed, must be either 'lower' (default) or 'upper'.

conf.level

Confidence level of the interval.

dist

A character string specifying the distribution, must be either 'normal' (default), 'sk_normal or 'gamma'.

Details

distdichoigen takes no data, but the number of observations as well as the mean and standard deviations of both groups. It first returns the results of a two-group unpaired t-test. Followed by the distributional estimates and their standard errors (see Sauzet et al. 2014 and Peacock et al. 2012) for a difference in proportions, risk ratio and odds ratio. It also provides the distributional confidence intervals for the statistics estimated. If a skew normal (dist = 'sk_normal') or gamma (dist = 'gamma') distribution is assumed, a third parameter alpha needs to be specified. For (dist = 'sk_normal') alpha is described in psn. For dist = 'gamma' alpha is the shape as described in pgamma.

Value

A list with class 'distdicho' containing the following components:

data.name

The names of the data.

arguments

A list with the specified arguments.

parameter

The mean, standard error and number of observations for both groups.

prop

The estimated proportions below / above the cut point for both groups.

dist.estimates

The difference in proportions, risk ratio and odds ratio of the groups.

se

The estimated standard error of the difference in proportions, the risk ratio and the odds ratio.

ci

The confidence intervals of the difference in proportions, the risk ratio and the odds ratio.

method

A character string indicating the used method.

ttest

A list containing the results of a t-test.

References

Peacock J.L., Sauzet O., Ewings S.M., Kerry S.M. Dichotomising continuous data while retaining statistical power using a distributional approach. Statist. Med; 2012; 26:3089-3103. Sauzet, O., Peacock, J. L. Estimating dichotomised outcomes in two groups with unequal variances: a distributional approach. Statist. Med; 2014 33 4547-4559 ;DOI: 10.1002/sim.6255. Sauzet, O., Ofuya, M., Peacock, J. L. Dichotomisation using a distributional approach when the outcome is skewed BMC Medical Research Methodology 2015, 15:40; doi:10.1186/s12874-015-0028-8. Peacock, J.L., Bland, J.M., Anderson, H.R.: Preterm delivery: effects of socioeconomic factors, psychological stress, smoking, alcohol, and caffeine. BMJ 311(7004), 531-535 (1995).

See Also

distdicho, distdichoi, distdichogen, regdistdicho

Examples

# Immediate form of sk_distdicho
distdichoigen(n1 = 75, m1 = 3250, s1 = 450, n2 = 110, m2 = 2950, s2 = 475,
               cp = 2500, tail = 'lower', alpha = -2.3, dist = 'sk_normal')