A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
Authors: Haghighi Mona , Johnson Suzanne Bennett , Qian Xiaoning , Lynch Kristian F. , Vehik Kendra , Huang Shuai ,   The TEDDY Study Group, Johnson Suzanne Bennett , Lynch Kristian F. , Vehik Kendra , Rewers Marian , Bautista Kimberly , Baxter Judith , Bedoy Ruth , Felipe-Morales Daniel , Driscoll Kimberly , Frohnert Brigitte I. , Gesualdo Patricia , Hoffman Michelle , Karban Rachel , Liu Edwin , Norris Jill , Samper-Imaz Adela , Steck Andrea , Waugh Kathleen , Wright Hali , Sharma Ashok , Hopkins Diane , Young Gabriela , She Jin-Xiong , Williams Joshua , Silvis Katherine , Steed Leigh , Gardiner Melissa , McIndoe Richard , Schatz Desmond , Thomas Jamie , Adams Janey , Jacobsen Laura , Haller Michael , Triplett Eric , Anderson Stephen W. , Mykkänen Juha , Lindfors Katri , Adamsson Annika , Jokipuu Sanna , Kallio Tiina , Karlsson Leena , Mäntymäki Elina , Rajala Petra , Riikonen Mika , Rouhiainen Jenni , Romo Minna , Leppänen Maria , Vainionpää Sini , Vähä-Mäkilä Mari , Stenius Aino , Toppari Jorma , Simell Olli G. , Simell Tuula , Sjöberg Maija , Varjonen Eeva , Hyöty Heikki , Knip Mikael , Kurppa Kalle , Lönnrot Maria , Niininen Tiina , Nyblom Mia , Ahonen Suvi , Kovanen Lea , Koreasalo Mirva , Riikonen Anne , Virtanen Suvi M. , Åkerlund Mari , Ilonen Jorma , Kähönen Miia , Latva-aho Tiina , Multasuo Katja , Veijola Riitta , Niinistö Sari , Rautanen Jenna , Ziegler Anette G. , Hummel Michael , Hummel Sandra , Janz Nicole , Knopff Annette , Peplow Claudia , Roth Roswith , Scholz Marlon , Stock Joanna , Warncke Katharina , Wendel Lorena , Winkler Christiane , Beyerlein Andreas , Bonifacio Ezio , Koletzko Sibylle , Foterek Kristina , Kersting Mathilde , Lernmark Åke , Agardh Daniel , Andrén Aronsson Carin , Ask Maria , Bremer Jenny , Carlsson Ulla-Marie , Cilio Corrado , Ericson-Hallström Emelie , Fransson Lina , Gard Thomas , Gerardsson Joanna , Bennet Rasmus , Hansen Monica , Hansson Gertie , Hyberg Susanne , Johansen Fredrik , Jonsdottir Berglind , Elding Larsson Helena , Lindström Marielle , Lundgren Markus , Månsson-Martinez Maria , Markan Maria , Melin Jessica , Mestan Zeliha , Ottosson Karin , Rahmati Kobra , Ramelius Anita , Salami Falastin , Sibthorpe Sara , Sjöberg Birgitta , Swartling Ulrica , Tekum Amboh Evelyn , Törn Carina , Wallin Anne , Wimar Åsa , Åberg Sofie , Hagopian William A. , Killian Michael , Cowen Crouch Claire , Skidmore Jennifer , Carson Josephine , Dalzell Maria , Dunson Kayleen , Hervey Rachel , Johnson Corbin , Lyons Rachel , Meyer Arlene , Mulenga Denise , Tarr Alexander , Uland Morgan , Willis John , Becker Dorothy , Franciscus Margaret , Smith MaryEllen Dalmagro-Elias , Daftary Ashi , Beth Klein Mary , Yates Chrystal , Krischer Jeffrey P. , Abbondondolo Michael , Austin-Gonzalez Sarah , Avendano Maryouri , Baethke Sandra , Brown Rasheedah , Burkhardt Brant , Butterworth Martha , Clasen Joanna , Cuthbertson David , Eberhard Christopher , Fiske Steven , Garcia Dena , Garmeson Jennifer , Gowda Veena , Heyman Kathleen , Perez Laras Francisco , Lee Hye-Seung , Liu Shu , Liu Xiang , Malloy Jamie , McCarthy Cristina , Meulemans Steven , Parikh Hemang , Shaffer Chris , Smith Laura , Smith Susan , Sulman Noah , Tamura Roy , Uusitalo Ulla , Vijayakandipan Ponni , Wood Keith , Yang Jimin , Akolkar Beena , Bourcier Kasia , Briese Thomas 
Published: August 2016

AbstractRegression 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.