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Your search keyword '"Foraita, Ronja"' showing total 6 results
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6 results on '"Foraita, Ronja"'

5. The Conditional Synergy Index --A measure based on graphical models to assess gene-gene interaction

Author

Foraita, Ronja, Pigeot, Iris, and König, Inke

Subjects
epistasis, genetic epidemiology, 510 Mathematics, CSI, statistics, graphical models, gene-gene-interaction, ddc:510, 2-locus-penetrance models, Conditional Synergy Index
Abstract

In genetic studies of complex diseases, it is important to identify and qualify gene-gene interactions. Interaction is often defined as deviance from genetic additive effects. But this statistical definition needs not to reflect the genes' biological interactions. I propose a new method to detect gene-gene interaction that exploits the concept of synergy and antagonism, which is said to capture biological relationships. The conditional synergy index (CSI) classifies and quantifies interaction on the penetrance scale. The general theory under two-locus disease models is developed. The index assumes a cohort design and genotypes to be dichotomized into risk-genotypes (exposed) and non-risk-genotypes (unexposed) but it does not assume the loci to be in linkage equilibrium. In simulation studies the performance of the CSI, its estimator and variance is investigated and compared to the performance of various epidemiological interaction measure like e.g. Rothman's syngergy index and to appropriate bootstrap estimators. The results exhibit that in contrast to common interaction measures, only the CSI is adequat to detect interaction on the penetrance scale. However, the proposed estimator of the CSI shows a slow convergence.

Published
2008

6. [Detection of drug risks after approval : Methods development for the use of routine statutory health insurance data].

Author

Foraita R, Dijkstra L, Falkenberg F, Garling M, Linder R, Pflock R, Rizkallah MR, Schwaninger M, Wright MN, and Pigeot I

Subjects
Germany, Humans, Adverse Drug Reaction Reporting Systems, Drug-Related Side Effects and Adverse Reactions, Insurance, Health statistics & numerical data, Pharmacovigilance
Abstract

Adverse drug reactions are among the leading causes of death. Pharmacovigilance aims to monitor drugs after they have been released to the market in order to detect potential risks. Data sources commonly used to this end are spontaneous reports sent in by doctors or pharmaceutical companies. Reports alone are rather limited when it comes to detecting potential health risks. Routine statutory health insurance data, however, are a richer source since they not only provide a detailed picture of the patients' wellbeing over time, but also contain information on concomitant medication and comorbidities.To take advantage of their potential and to increase drug safety, we will further develop statistical methods that have shown their merit in other fields as a source of inspiration. A plethora of methods have been proposed over the years for spontaneous reporting data: a comprehensive comparison of these methods and their potential use for longitudinal data should be explored. In addition, we show how methods from machine learning could aid in identifying rare risks. We discuss these so-called enrichment analyses and how utilizing pharmaceutical similarities between drugs and similarities between comorbidities could help to construct risk profiles of the patients prone to experience an adverse drug event.Summarizing these methods will further push drug safety research based on healthcare claim data from German health insurances which form, due to their size, longitudinal coverage, and timeliness, an excellent basis for investigating adverse effects of drugs.

Published
2018
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