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Emerg Themes Epidemiol. 2016 Jun 7;13:8. doi: 10.1186/s12982-016-0050-2. eCollection 2016.

A distributional approach to obtain adjusted comparisons of proportions of a population at risk.

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Department of Epidemiology and International Public Health, Bielefeld School of Public Health (BiSPH), Bielefeld University, Bielefeld, Germany.
Alice Salomon Hochschule Berlin, University of Applied Sciences, Berlin, Germany.
Department of Gynecology, Campus Virchow-Klinikum, Charité Universitätsmedizin Berlin, Berlin, Germany.
Division of Health and Social Care Research, King's College London, London, UK ; NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London, London, UK.



Dichotomisation of continuous data has statistical drawbacks such as loss of power but may be useful in epidemiological research to define high risk individuals.


We extend a methodology for the presentation of comparison of proportions derived from a comparison of means for a continuous outcome to reflect the relationship between a continuous outcome and covariates in a linear (mixed) model without losing statistical power. The so called "distributional method" is described and using perinatal data for illustration, results from the distributional method are compared to those of logistic regression and to quantile regression for three different outcomes.


Estimates obtained using the distributional method for the comparison of proportions are consistently more precise than those obtained using logistic regression. For one of the three outcomes the estimates obtained from the distributional method and from logistic regression disagreed highlighting that the relationships between outcome and covariate differ conceptually between the two models.


When an outcome follows the required condition of distribution shift between exposure groups, the results of a linear regression model can be followed by the corresponding comparison of proportions at risk. This dual approach provides more precise estimates than logistic regression thus avoiding the drawback of the usual dichotomisation of continuous outcomes.


Dichotomisation; Linear model; Logistic model; Quantile regression

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