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Practical investigation of the performance of robust logistic regression to predict the genetic risk of hypertension

Kesselmeier, Miriam ; Legrand, Carine ; Peil, Barbara ; Kabisch, Maria ; Fischer, Christine ; Hamann, Ute ; Bermejo, Justo Lorenzo

In: BMC Proceedings, 8 (2014), Nr. S65. pp. 1-6. ISSN 1753-6561

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Download (320kB) | Lizenz: Creative Commons LizenzvertragPractical investigation of the performance of robust logistic regression to predict the genetic risk of hypertension by Kesselmeier, Miriam ; Legrand, Carine ; Peil, Barbara ; Kabisch, Maria ; Fischer, Christine ; Hamann, Ute ; Bermejo, Justo Lorenzo underlies the terms of Creative Commons Attribution 3.0 Germany

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Abstract

Logistic regression is usually applied to investigate the association between inherited genetic variants and a binary disease phenotype. A limitation of standard methods used to estimate the parameters of logistic regression models is their strong dependence on a few observations deviating from the majority of the data. We used data from the Genetic Analysis Workshop 18 to explore the possible benefit of robust logistic regression to estimate the genetic risk of hypertension. The comparison between standard and robust methods relied on the influence of departing hypertension profiles (outliers) on the estimated odds ratios, areas under the receiver operating characteristic curves, and clinical net benefit. Our results confirmed that single outliers may substantially affect the estimated genotype relative risks. The ranking of variants by probability values was different in standard and in robust logistic regression. For cutoff probabilities between 0.2 and 0.6, the clinical net benefit estimated by leave-one-out cross-validation in the investigated sample was slightly larger under robust regression, but the overall area under the receiver operating characteristic curve was larger for standard logistic regression. The potential advantage of robust statistics in the context of genetic association studies should be investigated in future analyses based on real and simulated data.

Item Type: Article
Journal or Publication Title: BMC Proceedings
Volume: 8
Number: S65
Publisher: BioMed Central
Place of Publication: London
Date Deposited: 26 Feb 2016 13:00
Date: 2014
ISSN: 1753-6561
Page Range: pp. 1-6
Faculties / Institutes: Service facilities > German Cancer Research Center (DKFZ)
Medizinische Fakultät Heidelberg > Institut für Humangenetik
Medizinische Fakultät Heidelberg > Institut für Medizinische Biometrie und Informatik
Subjects: 610 Medical sciences Medicine
Additional Information: erschienen in: BMC Proceedings 2014, 8 (Suppl 1):S65. From: Genetic Analysis Workshop 18, Stevenson, WA, USA. 13-17 October 2012
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