A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study

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dc.contributor University of Helsinki, Clinicum en
dc.contributor.author Haghighi, Mona
dc.contributor.author Johnson, Suzanne Bennett
dc.contributor.author Qian, Xiaoning
dc.contributor.author Lynch, Kristian F.
dc.contributor.author Vehik, Kendra
dc.contributor.author Huang, Shuai
dc.contributor.author TEDDY Study Grp
dc.contributor.author Knip, Mikael
dc.date.accessioned 2017-02-02T15:07:01Z
dc.date.available 2017-02-02T15:07:01Z
dc.date.issued 2016-08-26
dc.identifier.citation Haghighi , M , Johnson , S B , Qian , X , Lynch , K F , Vehik , K , Huang , S , TEDDY Study Grp & Knip , M 2016 , ' A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study ' , Scientific Reports , vol. 6 , 30828 . https://doi.org/10.1038/srep30828 en
dc.identifier.issn 2045-2322
dc.identifier.other PURE: 79346418
dc.identifier.other PURE UUID: ca72359b-9e6c-48ea-bde6-be537f8540dd
dc.identifier.other WOS: 000381966300001
dc.identifier.other Scopus: 84984691670
dc.identifier.uri http://hdl.handle.net/10138/174574
dc.description.abstract Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions. en
dc.format.extent 11
dc.language.iso eng
dc.relation.ispartof Scientific Reports
dc.rights en
dc.subject ASSOCIATION ANALYSIS en
dc.subject EPIDEMIOLOGY en
dc.subject STRESS en
dc.subject ONSET en
dc.subject LASSO en
dc.subject TEDDY en
dc.subject 3111 Biomedicine en
dc.subject 3121 General medicine, internal medicine and other clinical medicine en
dc.title A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1038/srep30828
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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