Bettina Petersen1, Iris Vesper1, Bernhild Pachwald1, Nicole Dagenbach1, Sina Buck2, Delia Waldenmaier3, Lutz Heinemann4. 1. Roche Diabetes Care GmbH, Mannheim, Germany. 2. Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Lise-Meitner-Strasse 8/2, 89081, Ulm, Germany. sina.buck@idt-ulm.de. 3. Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Lise-Meitner-Strasse 8/2, 89081, Ulm, Germany. 4. Science Consulting in Diabetes GmbH, Neuss, Germany.
Abstract
INTRODUCTION: Several clinical studies investigated improvements of patient outcomes due to diabetes management interventions. However, chronic disease management is intricate with complex multifactorial behavior patterns. Such studies thus have to be well designed in order to allocate all observed effects to the defined intervention and to exclude effects of other confounders as well as possible. This article aims to provide challenges in interpreting diabetes management intervention studies and suggests approaches for optimizing study implementation and for avoiding pitfalls based on current experiences. MAIN BODY: Lessons from the STeP and ProValue studies demonstrated the difficulty in medical device studies that rely on behavioral changes in intervention group patients. To successfully engage patients, priority should be given to health care professionals being engaged, operational support in technical issues being available, and adherence being assessed in detail. Another difficulty is to avoid contamination of the control group with the intervention; therefore, strict allocation concealment should be maintained. However, randomization and blinding are not always possible. A limited effect size due to improvements regarding clinical endpoints in the control group is often caused by the Hawthorne effect. Improvements in the control group can also be caused with increased attention paid to the subjects. In order to reduce improvements in the control group, it is essential to identify the specific reasons and adjust study procedures accordingly. A pilot phase is indispensable for this. Another option is to include a third study arm to control for enhanced standard of care and study effects. Furthermore, retrospective data collection could be a feasible option. Adaptive study designs might reduce the necessity of a separate pilot study and combine the exploratory and confirmatory stages of an investigation in one single study. CONCLUSION: There are several aspects to consider in medical device studies when using interventions that rely on changes in behavior to achieve an effective implementation and significant study results. Improvements in the control group may reduce effect sizes and limit statistical significance; therefore, alternatives to the traditional randomized controlled trials may be considered.
INTRODUCTION: Several clinical studies investigated improvements of patient outcomes due to diabetes management interventions. However, chronic disease management is intricate with complex multifactorial behavior patterns. Such studies thus have to be well designed in order to allocate all observed effects to the defined intervention and to exclude effects of other confounders as well as possible. This article aims to provide challenges in interpreting diabetes management intervention studies and suggests approaches for optimizing study implementation and for avoiding pitfalls based on current experiences. MAIN BODY: Lessons from the STeP and ProValue studies demonstrated the difficulty in medical device studies that rely on behavioral changes in intervention group patients. To successfully engage patients, priority should be given to health care professionals being engaged, operational support in technical issues being available, and adherence being assessed in detail. Another difficulty is to avoid contamination of the control group with the intervention; therefore, strict allocation concealment should be maintained. However, randomization and blinding are not always possible. A limited effect size due to improvements regarding clinical endpoints in the control group is often caused by the Hawthorne effect. Improvements in the control group can also be caused with increased attention paid to the subjects. In order to reduce improvements in the control group, it is essential to identify the specific reasons and adjust study procedures accordingly. A pilot phase is indispensable for this. Another option is to include a third study arm to control for enhanced standard of care and study effects. Furthermore, retrospective data collection could be a feasible option. Adaptive study designs might reduce the necessity of a separate pilot study and combine the exploratory and confirmatory stages of an investigation in one single study. CONCLUSION: There are several aspects to consider in medical device studies when using interventions that rely on changes in behavior to achieve an effective implementation and significant study results. Improvements in the control group may reduce effect sizes and limit statistical significance; therefore, alternatives to the traditional randomized controlled trials may be considered.
Entities:
Keywords:
Adaptive study design; Behavior-based; Diabetes; Medical device studies; ProValue; Randomized controlled trials; STeP; Study effect; Study performance
Authors: Mary B Abraham; Jennifer A Nicholas; Michael Crone; Trang T Ly; Elizabeth A Davis; Timothy W Jones Journal: J Diabetes Sci Technol Date: 2017-12-25
Authors: William Polonsky; Lawrence Fisher; Charles Schikman; Deborah Hinnen; Christopher Parkin; Zhihong Jelsovsky; Linda Amstutz; Matthias Schweitzer; Robin Wagner Journal: BMC Fam Pract Date: 2010-05-18 Impact factor: 2.497
Authors: L Taggart; M Truesdale; M E Carey; L Martin-Stacey; J Scott; B Bunting; V Coates; M Brown; T Karatzias; R Northway; J M Clarke Journal: Diabet Med Date: 2017-11-21 Impact factor: 4.359
Authors: Carol Byrd-Bredbenner; FanFan Wu; Kim Spaccarotella; Virginia Quick; Jennifer Martin-Biggers; Yingting Zhang Journal: Int J Behav Nutr Phys Act Date: 2017-07-11 Impact factor: 6.457
Authors: Sarah H Wild; Janet Hanley; Stephanie C Lewis; John A McKnight; Lucy B McCloughan; Paul L Padfield; Richard A Parker; Mary Paterson; Hilary Pinnock; Aziz Sheikh; Brian McKinstry Journal: PLoS Med Date: 2016-07-26 Impact factor: 11.069