Dichotomisation using a distributional approach when the outcome is skewed
Sauzet O, Ofuya M, Peacock JL (2015)
BMC Medical Research Methodology 15(1): 40.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Sauzet, OdileUniBi;
Ofuya, Mercy;
Peacock, Janet L
Einrichtung
Abstract / Bemerkung
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.
Stichworte
Dichotomisation Distributional method Birthweight High blood pressure BMI Gestational age
Erscheinungsjahr
2015
Zeitschriftentitel
BMC Medical Research Methodology
Band
15
Ausgabe
1
Art.-Nr.
40
ISSN
1471-2288
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2900858
Zitieren
Sauzet O, Ofuya M, Peacock JL. Dichotomisation using a distributional approach when the outcome is skewed. BMC Medical Research Methodology. 2015;15(1): 40.
Sauzet, O., Ofuya, M., & Peacock, J. L. (2015). Dichotomisation using a distributional approach when the outcome is skewed. BMC Medical Research Methodology, 15(1), 40. doi:10.1186/s12874-015-0028-8
Sauzet, Odile, Ofuya, Mercy, and Peacock, Janet L. 2015. “Dichotomisation using a distributional approach when the outcome is skewed”. BMC Medical Research Methodology 15 (1): 40.
Sauzet, O., Ofuya, M., and Peacock, J. L. (2015). Dichotomisation using a distributional approach when the outcome is skewed. BMC Medical Research Methodology 15:40.
Sauzet, O., Ofuya, M., & Peacock, J.L., 2015. Dichotomisation using a distributional approach when the outcome is skewed. BMC Medical Research Methodology, 15(1): 40.
O. Sauzet, M. Ofuya, and J.L. Peacock, “Dichotomisation using a distributional approach when the outcome is skewed”, BMC Medical Research Methodology, vol. 15, 2015, : 40.
Sauzet, O., Ofuya, M., Peacock, J.L.: Dichotomisation using a distributional approach when the outcome is skewed. BMC Medical Research Methodology. 15, : 40 (2015).
Sauzet, Odile, Ofuya, Mercy, and Peacock, Janet L. “Dichotomisation using a distributional approach when the outcome is skewed”. BMC Medical Research Methodology 15.1 (2015): 40.
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Zuletzt Hochgeladen
2019-09-06T09:18:35Z
MD5 Prüfsumme
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Daten bereitgestellt von European Bioinformatics Institute (EBI)
3 Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
Using marginal standardisation to estimate relative risk without dichotomising continuous outcomes.
Chen Y, Ning Y, Kao SL, Støer NC, Müller-Riemenschneider F, Venkataraman K, Khoo EYH, Tai ES, Tan CS., BMC Med Res Methodol 19(1), 2019
PMID: 31357938
Chen Y, Ning Y, Kao SL, Støer NC, Müller-Riemenschneider F, Venkataraman K, Khoo EYH, Tai ES, Tan CS., BMC Med Res Methodol 19(1), 2019
PMID: 31357938
Comparison of Dichotomized and Distributional Approaches in Rare Event Clinical Trial Design: a Fixed Bayesian Design.
Lei Y, Carlson S, Yelland LN, Makrides M, Gibson R, Gajewski BJ., J Appl Stat 44(8), 2017
PMID: 28503016
Lei Y, Carlson S, Yelland LN, Makrides M, Gibson R, Gajewski BJ., J Appl Stat 44(8), 2017
PMID: 28503016
A distributional approach to obtain adjusted comparisons of proportions of a population at risk.
Sauzet O, Breckenkamp J, Borde T, Brenne S, David M, Razum O, Peacock JL., Emerg Themes Epidemiol 13(), 2016
PMID: 27279891
Sauzet O, Breckenkamp J, Borde T, Brenne S, David M, Razum O, Peacock JL., Emerg Themes Epidemiol 13(), 2016
PMID: 27279891
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