Causal inference under interference with prognostic scores for dynamic group therapy studies
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Bing Han
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Susan M. Paddock
Abstract
Group therapy is a common treatment modality for behavioral health conditions. Patients often enter and exit groups on an ongoing basis, leading to dynamic therapy groups. Examining the effect of high versus low session attendance on patient outcomes is a research question of interest. However, there are several challenges to identifying causal effects in this setting, including the lack of randomization, interference among patients, and the interrelatedness of patient participation. Dynamic therapy groups motivate a unique causal inference scenario, as the treatment statuses are completely defined by the patient attendance record for the therapy session, which is also the structure inducing interference. We adopt the Rubin causal model framework to define the causal effect of high versus low session attendance of group therapy at both the individual patient and peer levels. We propose a strategy to identify individual, peer, and total effects of high attendance versus low attendance on patient outcomes by the prognostic score stratification. We examine performance of our approach via simulation and apply it to data from a group cognitive behavioral therapy trial for treating depression among patients in a substance use disorders treatment setting.
Funding source: National Institute On Alcohol Abuse And Alcoholism of the National Institutes of Health
Award Identifier / Grant number: R01AA019663
Funding source: National Institute of Drug Abuse of the National Institutes of Health
Award Identifier / Grant number: R01DA040721
Acknowledgments
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This work was supported by the National Institute On Alcohol Abuse And Alcoholism of the National Institutes of Health [R01AA019663] and the National Institute of Drug Abuse of the National Institutes of Health [R01DA040721].
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Proposition 1 is a direct result from Assumption 2.
Proof of Proposition 2
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Proof of Proposition 3
Noticing that (V i , Z i ) is determined by M,
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Doubly robust adaptive LASSO for effect modifier discovery
- Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
- Review
- Review and comparison of treatment effect estimators using propensity and prognostic scores
- Research Articles
- Error rate control for classification rules in multiclass mixture models
- Regression trees and ensembles for cumulative incidence functions
- Causal inference under over-simplified longitudinal causal models
- Causal inference under interference with prognostic scores for dynamic group therapy studies
- Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers
- A Bayesian semiparametric accelerate failure time mixture cure model
- Quantifying the extent of visit irregularity in longitudinal data
- An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis
- A robust hazard ratio for general modeling of survival-times
- Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring
- A parametric approach to relaxing the independence assumption in relative survival analysis
- The number of response categories in ordered response models
- A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome
- Spike detection for calcium activity
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Doubly robust adaptive LASSO for effect modifier discovery
- Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
- Review
- Review and comparison of treatment effect estimators using propensity and prognostic scores
- Research Articles
- Error rate control for classification rules in multiclass mixture models
- Regression trees and ensembles for cumulative incidence functions
- Causal inference under over-simplified longitudinal causal models
- Causal inference under interference with prognostic scores for dynamic group therapy studies
- Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers
- A Bayesian semiparametric accelerate failure time mixture cure model
- Quantifying the extent of visit irregularity in longitudinal data
- An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis
- A robust hazard ratio for general modeling of survival-times
- Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring
- A parametric approach to relaxing the independence assumption in relative survival analysis
- The number of response categories in ordered response models
- A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome
- Spike detection for calcium activity
