Adaptive Treatment Strategies in Chronic Disease
Open accessAbstract
An adaptive treatment strategy (ATS) is a rule for adapting a treatment plan to a patient's history of previous treatments and the response to those treatments. The ongoing management of chronic disease defines an ATS, which may be implicit and hidden or explicit and well-specified. The ATS is characterized by the use of intermediate, early markers of response to dynamically alter treatment decisions, in order to achieve a favorable ultimate outcome. We illustrate the ATS concept and describe how the effect of initial treatment decisions depends on the performance of subsequent decisions at later stages. We show how to compare two or more ATSs, or to determine an optimal ATS, using a sequential multiple assignment randomized (SMAR) trial. Designers of clinical trials might find the ATS concept useful in improving the efficiency and ecological relevance of clinical trials.
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Many health disorders are managed through a sequential, individualized approach in which treatment is adapted and readapted over time according to the individual's needs and changing status.
“The management of many health disorders, such as obesity, substance use, or depression, often entails a sequential, individualized approach whereby treatment (e.g., behavioral or medical interventions, or a combination) is adapted and readapted over time in response to the specific needs and evolving status of the individual [1,2].”
From Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014) · cites this paper as [2]
Even with slow tapering, the likelihood of withdrawal syndromes and persistent postwithdrawal disorders and symptoms is very high.
“Even with slow tapering, the likelihood of withdrawal syndromes and persistent postwithdrawal disorders and symptoms is very high”
From The Deceptive Manifestations of Treatment Resistance in Depression: A New Look at the Problem (2020) · cites this paper as [32]
The likelihood of response of withdrawal and postwithdrawal syndromes to new antidepressants is very variable.
“the likelihood of response of these syndromes to the introduction of new antidepressants is very variable [29-33].”
From The Deceptive Manifestations of Treatment Resistance in Depression: A New Look at the Problem (2020) · cites this paper as [32]
Many STAR*D findings can be interpreted in light of the oppositional model of tolerance: pharmacological manipulations by switching or augmentation may propel depressive illness into a refractory phase with low remission, high relapse, and high intolerance.
“Many of the STAR * D findings can be interpreted in light of the oppositional model of tolerance [32]: pharmacological manipulations, either by switching or augmentation (steps 1 and 2) may propel depressive illness into a refractory phase, characterized by low remission, high relapse, and high intolerance (steps 3 and 4).”
From The Deceptive Manifestations of Treatment Resistance in Depression: A New Look at the Problem (2020) · cites this paper as [32]
Resistance to re-challenge, loss of clinical effects, paradoxical reactions, withdrawal and postwithdrawal syndromes tend to cluster and may share a common mechanism subsumed under the oppositional model of tolerance.
“Resistance to re-challenge, loss of clinical effects, paradoxical reactions, withdrawal and postwithdrawal syndromes tend to cluster and may share a common mechanism that has been subsumed under the rubric of oppositional model of tolerance [26, 32].”
From The Deceptive Manifestations of Treatment Resistance in Depression: A New Look at the Problem (2020) · cites this paper as [32]
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- Authors
- Lavori, P. W., & Dawson, R.
- Affiliations
- Stanford Medicine (US)
- Harvard University (US)
- Venue
- Annual Review of Medicine
- Publisher
- Annual Reviews
- Published
- 2008-02-01
- Volume
- 59
- Issue
- 1
- Pages
- 443-453
- ISSN
- 0066-4219 (ISSN-L)
- 1545-326X
- PMC
- PMC2739674
- OpenAlex
- W2145829640
- Cited by
- 113
- References
- 17
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 2 · 2023: 5 · 2022: 12 · 2021: 10 · 2020: 7 · 2019: 5 · 2018: 6 · 2017: 4 · 2016: 5 · 2015: 6 · 2014: 8 · 2013: 8 · 2012: 9
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH051481, K08MH001481)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- adaptive treatment strategies, chronic disease management, sequential multiple assignment randomized trial, treatment response biomarkers, treatment decision-making, optimal treatment strategy, clinical trial design, ecological validity
- MeSH terms
- Chronic Disease, Clinical Protocols, Humans, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care
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