Sensitivity of estimands in clinical trials with imperfect compliance
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Heng Chen
und
Daniel F. Heitjan
Abstract
In clinical trials that are subject to noncompliance, the commonly used intention-to-treat estimand is valid as a causal effect of treatment assignment but is sensitive to the level of compliance. An alternative estimand, the complier average causal effect (CACE), measures the average effect of treatment received in the latent subset of subjects who would comply with either assigned treatment. Because the principal stratum of compliers can vary with the circumstances of the trial, CACE too depends on the compliance fraction. We propose a model in which an underlying latent proto-compliance interacts with trial characteristics to determine a subject’s compliance behavior. When the latent compliance is independent of the individual treatment effect, the average causal effect is constant across compliance classes, and CACE is robust across trials and equal to the population average causal effect. We demonstrate the potential degree of sensitivity of CACE in a simulation study, an analysis of data from a trial of vitamin A supplementation in children, and a meta-analysis of trials of epidural analgesia in labor.
Acknowledgment
We thank the reviewers for many helpful comments and suggestions.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Survival analysis using deep learning with medical imaging
- Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions
- Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events
- Sensitivity of estimands in clinical trials with imperfect compliance
- Highly robust causal semiparametric U-statistic with applications in biomedical studies
- Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation
- Penalized logistic regression with prior information for microarray gene expression classification
- Bayesian learners in gradient boosting for linear mixed models
- Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates
- HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies
- Heterogeneity in meta-analysis: a comprehensive overview
- On stochastic dynamic modeling of incidence data
- Power of testing for exposure effects under incomplete mediation
- Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables
- Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset
- Assessing HIV-infected patient retention in a program of differentiated care in sub-Saharan Africa: a G-estimation approach
- Prediction-based variable selection for component-wise gradient boosting
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Survival analysis using deep learning with medical imaging
- Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions
- Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events
- Sensitivity of estimands in clinical trials with imperfect compliance
- Highly robust causal semiparametric U-statistic with applications in biomedical studies
- Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation
- Penalized logistic regression with prior information for microarray gene expression classification
- Bayesian learners in gradient boosting for linear mixed models
- Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates
- HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies
- Heterogeneity in meta-analysis: a comprehensive overview
- On stochastic dynamic modeling of incidence data
- Power of testing for exposure effects under incomplete mediation
- Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables
- Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset
- Assessing HIV-infected patient retention in a program of differentiated care in sub-Saharan Africa: a G-estimation approach
- Prediction-based variable selection for component-wise gradient boosting
