distdichoigen: normal, skew-normal or gamma distributed data (immediate...
In distdichoR: Distributional Method for the Dichotomisation of Continuous Outcomes

Description Usage Arguments Details Value References See Also Examples

View source: R/distdichoigen.R

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

1 2	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"))

`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'.

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.

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.

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).

distdicho, distdichoi, distdichogen, regdistdicho

# 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')