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

Description Usage Arguments Details Value References See Also Examples

View source: R/regdistdicho.R

Provides adjusted distributional estimates for the comparison of proportions for a dichotomised dependent continuous variable derived from a linear regression of the continuous outcome on the grouping variable and other covariates as described in Sauzet et al. 2015.

1 2	regdistdicho(mod, group_var, cp = 0, tail = c("lower", "upper"), conf.level = 0.95, dist = c("normal", "sk_normal", "gamma"), alpha = 1)

`mod`	A linear model of the form lm(lhs ~ rhs) where lhs is a numeric variable giving the data values and rhs is the grouping variable and other covariates.
`group_var`	A character string specifying the name of the grouping variable.
`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 of the error variable in the linear regression, must be either 'normal' (default), 'sk_normal or 'gamma'.
`alpha`	A numeric value specifying further parameter of the skew normal / gamma distribution.

regdistdicho returns 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. The estimation is based on the marginal means of a linear regression of the outcome on the grouping variable and other covariates.

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 marginal 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.

Peacock J.L., Sauzet O., Ewings S.M., Kerry S.M. Dichotomising continuous data while retaining statistical power using a distributional approach. 2012 Statist. Med; 26:3089-3103. Sauzet, O., Peacock, J. L. Estimating dichotomised outcomes in two groups with unequal variances: a distributional approach. 2014 Statist. Med; 33 4547-4559 ;DOI: 10.1002/sim.6255. Sauzet, O., Brekenkamp, J., Brenne, S. , Borde, T., David, M., Razum, O., Peacock, J.L. 2015. A distributional approach to obtain adjusted differences in population at risk with a comparison with other regressions methods using perinatal data. In preparation. 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, distdichoigen

## Proportions of low birth weight babies among smoking and non-smoking mothers
## (data from Peacock et al. 1995)
mod_smoke <- lm(birthwt ~ smoke + gest, data = bwsmoke)
regdistdicho(mod = mod_smoke, group_var = 'smoke', cp = 2500, tail = 'lower')