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Sci Rep. 2016 Aug 26;6:30828. doi: 10.1038/srep30828.

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

Collaborators (184)

Johnson, Lynch KF, Vehik K, Rewers M, Bautista K, Baxter, Bedoy R, Morales DF, Driscoll K, Frohnert BI, Gesualdo P, Hoffman M, Karban R, Liu E, Norris J, Samper-Imaz A, Steck A, Waugh K, Wright H, Sharma A, Hopkins D, Young G, She JX, Williams J, Silvis K, Steed L, Gardiner M, McIndoe R, Schatz D, Thomas J, Adams J, Jacobsen L, Haller M, Triplett M, Anderson SW, Mykkänen J, Lindfors K, Adamsson A, Jokipuu S, Kallio T, Karlsson L, Mäntymäki E, Rajala P, Riikonen M, Rouhiainen J, Romo M, Leppänen M, Vainionpää S, Vähä-Mäkilä M, Stenius A, Toppari J, Simell OG, Simell T, Sjöberg M, Varjonen E, Hyöty H, Knip M, Kurppa K, Lönnrot M, Niininen T, Nyblom M, Ahonen S, Kovanen L, Koreasalo M, Riikonen A, Virtanen SM, Akerlund M, Ilonen J, Kähönen M, Larva-aho T, Multasuo K, Veijola R, Niinistö S, Rautanen J, Ziegler AG, Hummel M, Hummel S, Janz N, Knopff A, Peplow C, Roth R, Scholz M, Stock J, Warncke K, Wendel L, Winkler Ch, Beyerlein A, Bonifacio E, Koletzko S, Foterek K, Kersting M, Lernmark, Agardh D, Aronsson CA, Ask M, Bremer J, Carlsson UM, Cilio C, Ericson-Hallström E, Fransson L, Gard T, Gerardsson J, Bennet R, Hansen M, Hansson G, Hyberg S, Johansen F, Jonsdottir B, Larsson HE, Lindström M, Lundgren M, Månsson-Martinez M, Markan M, Melin J, Mestan Z, Ottoson K, Rahmati K, Ramelius A, Salami F, Sibthorpe S, Sjöberg B, Swartling U, Amboh ET, Törn C, Wallin A, Wimar, Åberg S, Hagopian WA, Killian M, Crouch CC, Skidmore J, Carson J, Dalzell M, Dunson K, Hervey R, Johnson C, Lyons R, Meyer A, Mulenga D, Tarr A, Uland M, Willis J, Becker D, Franciscus M, Dalmagro-Elias Smith M, Daftary A, Klein MB, Yates Ch, Krischer JP, Abbondondolo M, Austin-Gonzalez S, Avendano M, Baethke S, Brown R, Burkhardt B, Butterworth M, Clasen J, Cuthbertson D, Eberhard Ch, Fiske S, Garcia D, Garmeson J, Gowda V, Heyman K, Perez Laras F, Lee HS, Liu Sh, Liu X, Malloy J, McCarthy C, Meulemans S, Parikh H, Shaffer Ch, Smith L, Smith S, Sulman N, Tamura R, Uusitalo U, Vijayakandipan P, Wood K, Yang J, Akolkar B, Bourcier K, Briese T.

Author information

1
Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, Florida, USA.
2
Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, Florida, USA.
3
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
4
Health Informatics Institute, University of South Florida, Tampa, Florida, USA.
5
Department of Industrial &Systems Engineering, University of Washington, Seattle, Washington, USA.

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.

PMID:
27561809
PMCID:
PMC5000469
DOI:
10.1038/srep30828
[Indexed for MEDLINE]
Free PMC Article

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