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Carina Törn


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


  • Mona Haghighi
  • Suzanne Bennett Johnson
  • Xiaoning Qian
  • Kristian F. Lynch
  • Kendra Vehik
  • Shuai Huang
  • Marian Rewers
  • Katherine Barriga
  • Judith Baxter
  • George Eisenbarth
  • Nicole Frank
  • Patricia Gesualdo
  • Michelle Hoffman
  • Jill Norris
  • Lisa Ide
  • Jessie Robinson
  • Kathleen Waugh
  • Jin Xiong She
  • Desmond Schatz
  • Diane Hopkins
  • Leigh Steed
  • Angela Choate
  • Katherine Silvis
  • Meena Shankar
  • Yi Hua Huang
  • Ping Yang
  • Hong Jie Wang
  • Jessica Leggett
  • Kim English
  • Richard McIndoe
  • Angela Dequesada
  • Michael Haller
  • Stephen W. Anderson
  • Anette G. Ziegler
  • Heike Boerschmann
  • Ezio Bonifacio
  • Melanie Bunk
  • Johannes Försch
  • Lydia Henneberger
  • Michael Hummel
  • Sandra Hummel
  • Gesa Joslowski
  • Mathilde Kersting
  • Annette Knopff
  • Nadja Kocher
  • Sibylle Koletzko
  • Stephanie Krause
  • Claudia Lauber
  • Ulrike Mollenhauer
  • Claudia Peplow
  • Maren Pflüger
  • Daniela Pöhlmann
  • Claudia Ramminger
  • Sargol Rash-Sur
  • Roswith Roth
  • Julia Schenkel
  • Leonore Thümer
  • Katja Voit
  • Christiane Winkler
  • Marina Zwilling
  • Olli G. Simell
  • Kirsti Nanto-Salonen
  • Jorma Ilonen
  • Mikael Knip
  • Riitta Veijola
  • Tuula Simell
  • Heikki Hyöty
  • Suvi M. Virtanen
  • Carina Kronberg-Kippilä
  • Maija Torma
  • Barbara Simell
  • Eeva Ruohonen
  • Minna Romo
  • Elina Mantymaki
  • Heidi Schroderus
  • Mia Nyblom
  • Aino Stenius
  • Åke Lernmark
  • Daniel Agardh
  • Peter Almgren
  • Eva Andersson
  • Carin Andrén-Aronsson
  • Maria Ask
  • Ulla Marie Karlsson
  • Corrado Cilio
  • Jenny Bremer
  • Emilie Ericson-Hallström
  • Thomas Gard
  • Joanna Gerardsson
  • Ulrika Gustavsson
  • Gertie Hansson
  • Monica Hansen
  • Susanne Hyberg
  • Rasmus Håkansson
  • Sten Ivarsson
  • Fredrik Johansen
  • Helena Larsson
  • Barbro Lernmark
  • Maria Markan
  • Theodosia Massadakis
  • Jessica Melin
  • Maria Månsson-Martinez
  • Anita Nilsson
  • Emma Nilsson
  • Kobra Rahmati
  • Sara Rang
  • Monica Sedig Järvirova
  • Sara Sibthorpe
  • Birgitta Sjöberg
  • Carina Törn
  • Anne Wallin
  • Åsa Wimar
  • William A. Hagopian
  • Xiang Yan
  • Michael Killian
  • Claire Cowen Crouch
  • Kristen M. Hay
  • Stephen Ayres
  • Carissa Adams
  • Brandi Bratrude
  • Greer Fowler
  • Czarina Franco
  • Carla Hammar
  • Diana Heaney
  • Patrick Marcus
  • Arlene Meyer
  • Denise Mulenga
  • Elizabeth Scott
  • Jennifer Skidmore
  • Erin Small
  • Joshua Stabbert
  • Viktoria Stepitova
  • Dorothy Becker
  • Margaret Franciscus
  • Maryellen Dalmagro-Elias Smith
  • Ashi Daftary
  • Jeffrey P. Krischer
  • Michael Abbondondolo
  • Lori Ballard
  • Rasheedah Brown
  • David Cuthbertson
  • Christopher Eberhard
  • Veena Gowda
  • Hye Seung Lee
  • Shu Liu
  • Jamie Malloy
  • Cristina McCarthy
  • Wendy McLeod
  • Laura Smith
  • Stephen Smith
  • Susan Smith
  • Ulla Uusitalo
  • Jimin Yang
  • Beena Akolkar
  • Thomas Briese
  • Henry Erlich
  • Steve Oberste

Summary, in English

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.


  • Diabetes and Celiac Unit
  • Genomics, Diabetes and Endocrinology
  • Diabetes - Immunovirology
  • Paediatric Endocrinology
  • EXODIAB: Excellence of Diabetes Research in Sweden

Publishing year





Scientific Reports



Document type

Journal article


Nature Publishing Group


  • Public Health, Global Health, Social Medicine and Epidemiology



Research group

  • Diabetes and Celiac Unit
  • Genomics, Diabetes and Endocrinology
  • Diabetes - Immunovirology
  • Paediatric Endocrinology


  • ISSN: 2045-2322