Designing a sequentially randomized study with adherence enhancing interventions for diabetes patients
Abstract
Adaptive treatment strategies can change treatment prescription over time in response to intermediate outcomes. They are the natural choice for treating chronic diseases or for prevention, since the condition of subjects tends to change over the long term. Similarly, flexible intervention strategies are vital for generating or sustaining better adherence in long term treatment settings. When a cost‐efficient first‐line treatment is available, for instance, good adherence is expected to help delay or avoid second‐line treatment. Sequentially randomized trials enable unbiased evaluation of how to best adapt adherence supporting interventions to a history of outcomes and adherence with the goal to optimize future treatment response. In this paper we propose and study different sequential designs targeting cost‐efficient control of type II diabetes under first‐line treatment through two different classes of adherence support: by (bio)technical and by behavioural means. We study their respective and joint impact first through double factorial adaptive designs, where interventions are triggered by an elevated risk of current treatment failure predicted by poor surrogate response. We develop the double factorial design and several derived designs that are more cost‐efficient in the context of managed care of diabetes patients. We evaluate the marginal responses over time to different adaptive treatment strategies by means of doubly robust estimators. We consider sample sizes needed to thus detect realistic and worthwhile effects and discuss the relative practical and theoretical merits of the separate designs. Copyright © 2010 John Wiley & Sons, Ltd.
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An alternative less conservative approach is to calculate sample size to contrast two or more embedded AIs by simulation.
“An alternative, potentially less conservative approach, is to calculate the sample size to contrast two (or more) embedded adaptive interventions by simulation experiment [78].”
From Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014) · cites this paper as [78]
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- Authors
- Buyze, J., Van Rompaye, B., & Goetghebeur, E.
- Affiliations
- Ghent University (BE)
- Corresponding author
- Buyze, Jozefien
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2010-01-25
- Volume
- 29
- Issue
- 10
- Pages
- 1114-1126
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2068618257
- Cited by
- 9
- References
- 30
- Citations by year
- 2019: 1 · 2016: 1 · 2015: 1 · 2014: 3 · 2012: 3
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Keywords
- adaptive treatment strategies, medication adherence, type 2 diabetes, first-line therapy, second-line therapy, treatment failure prediction, sample size determination, doubly robust estimation
- MeSH terms
- Computer Simulation, Cost-Benefit Analysis, Diabetes Mellitus, Humans, Models, Statistical, Randomized Controlled Trials as Topic, Sample Size
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