Noncompliance in randomized controlled trials

Marasinghe, Jeevan P.; Amarasinghe, A.A.W.

doi:10.1503/cmaj.1060189

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…First, they estimate the effect of randomization to a treatment sequence and not the actual treatment effect that is of main interest. In fact, the treatment effect estimates are often biased toward the null effect (Marasinghe & Amarasinghe, 2007;Lin et al, 2008). Second, the concluding results may not be reproducible due to the potential differential compliance behavior in real-world settings (Frangakis & Rubin, 1999;Robins & Tsiatis, 1991;Hewitt et al, 2006;Lin et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Constructing Optimal Treatment Regimes in the Presence of Non-compliance and Two Active Treatment Options

Pham¹,

Ertefaie²

2022

Preprint

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Existing literature on constructing optimal regimes often focuses on intentionto-treat analyses that completely ignore the compliance behavior of individuals. Instrumental variable-based methods have been developed for learning optimal regimes under endogeneity. However, when there are two active treatment arms, the average causal effects of treatments cannot be identified using a binary instrument, and thus the existing methods will not be applicable. To fill this gap, we provide a procedure that identifies an optimal regime and the corresponding value function as a function of a vector of sensitivity parameters. We also derive the canonical gradient of the target parameter and propose a multiply robust classification-based estimator of the optimal regime. Our simulations highlight the need for and usefulness of the proposed method in practice. We implement our method on the Adaptive Treatment for Alcohol and Cocaine Dependence randomized trial.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Constructing Optimal Treatment Regimes in the Presence of Non-compliance and Two Active Treatment Options

Pham¹,

Ertefaie²

2022

Preprint

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“…Intention-to-treat analysis measures the effect of randomization to a particular group on the outcome regardless of whether the individual complied with their assigned treatment. There are two main criticisms of intention-to-treat analyses: 1) the treatment effect estimates are biased due to confounding; the bias is often toward the null hypothesis of no effect because the estimate occurs across individuals with different levels of compliance (Marasinghe & Amarasinghe, 2007;Lin et al, 2008). In fact, the intention-to-treat effect estimate will likely diminish as noncompliance increases; 2) the rate of compliance or the compliance pattern in standard clinical practice may not be the same as the rate or the pattern in the clinical trial (Frangakis & Rubin, 1999;Robins & Tsiatis, 1991;Hewitt et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach

Artman¹,

Ertefaie²,

Lynch³

et al. 2020

Preprint

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The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs of individuals. The Adaptive Treatment for Alcohol and Cocaine Dependence study (ENGAGE) is a multi-stage randomized trial that aimed to provide longitudinal data for constructing treatment strategies to improve patients' engagement in therapy. However, the high rate of noncompliance and lack of analytic tools to account for noncompliance have impeded researchers from using the data to achieve the main goal of the trial. We overcome this issue by defining our target parameter as the mean outcome under different treatment strategies for given potential compliance strata and propose a Bayesian semiparametric model to estimate this quantity. While it adds substantial complexities to the analysis, one important feature of our work is that we consider partial rather than binary compliance classes which is more relevant in longitudinal studies. We assess the performance of our method through comprehensive simulation studies. We illustrate its application on the ENGAGE study and demonstrate that the optimal treatment strategy depends on compliance strata.

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Introduction to Causal Inference Approaches

Stuart¹,

Naeger²

2017

Health Services Research

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Noncompliance in randomized controlled trials

Cited by 6 publications

References 5 publications

Sensitivity Analysis for Constructing Optimal Treatment Regimes in the Presence of Non-compliance and Two Active Treatment Options

Sensitivity Analysis for Constructing Optimal Treatment Regimes in the Presence of Non-compliance and Two Active Treatment Options

Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach

Introduction to Causal Inference Approaches

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