title: Dichotomisation using a distributional approach when the outcome is skewed.
abstract: BACKGROUND
  Dichotomisation of continuous outcomes has been rightly criticised by statisticians because of the loss of information incurred. However to communicate a comparison of risks, dichotomised outcomes may be necessary. Peacock et al. developed a distributional approach to the dichotomisation of normally distributed outcomes allowing the presentation of a comparison of proportions with a measure of precision which reflects the comparison of means. Many common health outcomes are skewed so that the distributional method for the dichotomisation of continuous outcomes may not apply.
  
  
  METHODS
  We present a methodology to obtain dichotomised outcomes for skewed variables illustrated with data from several observational studies. We also report the results of a simulation study which tests the robustness of the method to deviation from normality and assess the validity of the newly developed method.
  
  
  RESULTS
  The review showed that the pattern of dichotomisation was varying between outcomes. Birthweight, Blood pressure and BMI can either be transformed to normal so that normal distributional estimates for a comparison of proportions can be obtained or better, the skew-normal method can be used. For gestational age, no satisfactory transformation is available and only the skew-normal method is reliable. The normal distributional method is reliable also when there are small deviations from normality.
  
  
  CONCLUSIONS
  The distributional method with its applicability for common skewed data allows researchers to provide both continuous and dichotomised estimates without losing information or precision. This will have the effect of providing a practical understanding of the difference in means in terms of proportions.
authors.affiliation: Epidemiology and International Public Health, School of Public Health, Universität Bielefeld, Bielefeld, Germany. odile.sauzet@uni-bielefeld.de.
authors.forename: Odile
authors.initials: O
authors.lastname: Sauzet
authors.affiliation: Division of Health and Social Care Research King's College London and NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK. mercy.ofuya@kcl.ac.uk.
authors.forename: Mercy
authors.initials: M
authors.lastname: Ofuya
authors.affiliation: Division of Health and Social Care Research King's College London and NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK. janet.peacock@kcl.ac.uk.
authors.forename: Janet L
authors.initials: JL
authors.lastname: Peacock
mesh_terms: D000465:Algorithms; D001724:Birth Weight; D001794:Blood Pressure; D015992:Body Mass Index; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data*; D012196:Review Literature as Topic; D016008:Statistical Distributions