A non‐parametric Bayesian approach for adjusting partial compliance in sequential decision making Article Swipe
Indrabati Bhattacharya
,
Brent A. Johnson
,
William J. Artman
,
Andrew Wilson
,
Kevin G. Lynch
,
James R. McKay
,
Ashkan Ertefaie
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/sim.9742
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/sim.9742
Existing methods for estimating the mean outcome under a given sequential treatment rule often rely on intention‐to‐treat analyses, which estimate the effect of following a certain treatment rule regardless of compliance behavior of patients. There are two major concerns with intention‐to‐treat analyses: (1) the estimated effects are often biased toward the null effect; (2) the results are not generalizable and reproducible due to the potentially differential compliance behavior. These are particularly problematic in settings with a high level of non‐compliance, such as substance use disorder studies. Our work is motivated by the Adaptive Treatment for Alcohol and Cocaine Dependence study (ENGAGE), which is a multi‐stage trial that aimed to construct optimal treatment strategies to engage patients in therapy. Due to the relatively low level of compliance in this trial, intention‐to‐treat analyses essentially estimate the effect of being randomized to a certain treatment, instead of the actual effect of the treatment. We obviate this challenge by defining the target parameter as the mean outcome under a dynamic treatment regime conditional on a potential compliance stratum. We propose a flexible non‐parametric Bayesian approach based on principal stratification, which consists of a Gaussian copula model for the joint distribution of the potential compliances, and a Dirichlet process mixture model for the treatment sequence specific outcomes. We conduct extensive simulation studies which highlight the utility of our approach in the context of multi‐stage randomized trials. We show robustness of our estimator to non‐linear and non‐Gaussian settings as well.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/sim.9742
- OA Status
- green
- Cited By
- 3
- References
- 43
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W3203881937Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/sim.9742Digital Object Identifier
- Title
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A non‐parametric Bayesian approach for adjusting partial compliance in sequential decision makingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-10Full publication date if available
- Authors
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Indrabati Bhattacharya, Brent A. Johnson, William J. Artman, Andrew Wilson, Kevin G. Lynch, James R. McKay, Ashkan ErtefaieList of authors in order
- Landing page
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https://doi.org/10.1002/sim.9742Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/11647968Direct OA link when available
- Concepts
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Computer science, Econometrics, Randomized controlled trial, Bayesian probability, Context (archaeology), Parametric statistics, Average treatment effect, Bivariate analysis, Prior probability, Estimator, Statistics, Mathematics, Machine learning, Artificial intelligence, Medicine, Surgery, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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