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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-11

AUTHORS

Mona Haghighi, Suzanne Bennett Johnson, Xiaoning Qian, Kristian F. Lynch, Kendra Vehik, Shuai Huang, The TEDDY Study Group, Marian Rewers, Kimberly Bautista, Judith Baxter, Ruth Bedoy, Daniel Felipe-Morales, Kimberly Driscoll, Brigitte I. Frohnert, Patricia Gesualdo, Michelle Hoffman, Rachel Karban, Edwin Liu, Jill Norris, Adela Samper-Imaz, Andrea Steck, Kathleen Waugh, Hali Wright, Ashok Sharma, Diane Hopkins, Gabriela Young, Jin-Xiong She, Joshua Williams, Katherine Silvis, Leigh Steed, Melissa Gardiner, Richard McIndoe, Desmond Schatz, Jamie Thomas, Janey Adams, Laura Jacobsen, Michael Haller, Eric Triplett, Stephen W. Anderson, Juha Mykkänen, Katri Lindfors, Annika Adamsson, Sanna Jokipuu, Tiina Kallio, Leena Karlsson, Elina Mäntymäki, Petra Rajala, Mika Riikonen, Jenni Rouhiainen, Minna Romo, Maria Leppänen, Sini Vainionpää, Mari Vähä-Mäkilä, Aino Stenius, Jorma Toppari, Olli G. Simell, Tuula Simell, Maija Sjöberg, Eeva Varjonen, Heikki Hyöty, Mikael Knip, Kalle Kurppa, Maria Lönnrot, Tiina Niininen, Mia Nyblom, Suvi Ahonen, Lea Kovanen, Mirva Koreasalo, Anne Riikonen, Suvi M. Virtanen, Mari Åkerlund, Jorma Ilonen, Miia Kähönen, Tiina Latva-aho, Katja Multasuo, Riitta Veijola, Sari Niinistö, Jenna Rautanen, Anette G. Ziegler, Michael Hummel, Sandra Hummel, Nicole Janz, Annette Knopff, Claudia Peplow, Roswith Roth, Marlon Scholz, Joanna Stock, Katharina Warncke, Lorena Wendel, Christiane Winkler, Andreas Beyerlein, Ezio Bonifacio, Sibylle Koletzko, Kristina Foterek, Mathilde Kersting, Åke Lernmark, Daniel Agardh, Carin Andrén Aronsson, Maria Ask, Jenny Bremer, Ulla-Marie Carlsson, Corrado Cilio, Emelie Ericson-Hallström, Lina Fransson, Thomas Gard, Joanna Gerardsson, Rasmus Bennet, Monica Hansen, Gertie Hansson, Susanne Hyberg, Fredrik Johansen, Berglind Jonsdottir, Helena Elding Larsson, Marielle Lindström, Markus Lundgren, Maria Månsson-Martinez, Maria Markan, Jessica Melin, Zeliha Mestan, Karin Ottosson, Kobra Rahmati, Anita Ramelius, Falastin Salami, Sara Sibthorpe, Birgitta Sjöberg, Ulrica Swartling, Evelyn Tekum Amboh, Carina Törn, Anne Wallin, Åsa Wimar, Sofie Åberg, William A. Hagopian, Michael Killian, Claire Cowen Crouch, Jennifer Skidmore, Josephine Carson, Maria Dalzell, Kayleen Dunson, Rachel Hervey, Corbin Johnson, Rachel Lyons, Arlene Meyer, Denise Mulenga, Alexander Tarr, Morgan Uland, John Willis, Dorothy Becker, Margaret Franciscus, MaryEllen Dalmagro-Elias Smith, Ashi Daftary, Mary Beth Klein, Chrystal Yates, Jeffrey P. Krischer, Michael Abbondondolo, Sarah Austin-Gonzalez, Maryouri Avendano, Sandra Baethke, Rasheedah Brown, Brant Burkhardt, Martha Butterworth, Joanna Clasen, David Cuthbertson, Christopher Eberhard, Steven Fiske, Dena Garcia, Jennifer Garmeson, Veena Gowda, Kathleen Heyman, Francisco Perez Laras, Hye-Seung Lee, Shu Liu, Xiang Liu, Jamie Malloy, Cristina McCarthy, Steven Meulemans, Hemang Parikh, Chris Shaffer, Laura Smith, Susan Smith, Noah Sulman, Roy Tamura, Ulla Uusitalo, Ponni Vijayakandipan, Keith Wood, Jimin Yang, Beena Akolkar, Kasia Bourcier, Thomas Briese

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. More... »

PAGES

30828

References to SciGraph publications

  • 2007-12. Supervised group Lasso with applications to microarray data analysis in BMC BIOINFORMATICS
  • 2001-10. Random Forests in MACHINE LEARNING
  • 1990-07. An epidemiologic investigation of potential risk factors for suicide attempts in SOCIAL PSYCHIATRY AND PSYCHIATRIC EPIDEMIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/srep30828

    DOI

    http://dx.doi.org/10.1038/srep30828

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1038599959

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27561809


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