ArticleStatistics in medicine2012
Longitudinal structural mixed models for the analysis of surgical trials with noncompliance.
Article in Statistics in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
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- Investigational vertebroplasty safety and efficacy trial (INVEST): patient-reported outcomes through 1 year.Radiology · 2013Trial
- Dynamic Predictions and Predictimands for Salvage Therapy in Recurrent Prostate Cancer Using Joint Models.Statistics in medicine · 2026Article
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- Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality.BMC medical research methodology · 2021Article
- Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview.Statistical methods in medical research · 2021Review
- Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.Statistical science : a review journal of the Institute of Mathematical Statistics · 2020Article
- Instrumental variable methods for a binary outcome were used to informatively address noncompliance in a randomized trial in surgery.Journal of clinical epidemiology · 2018Article
- Joint mixed-effects models for causal inference with longitudinal data.Statistics in medicine · 2018Article
- Adjustment for Variable Adherence Under Hierarchical Structure: Instrumental Variable Modeling Through Compound Residual Inclusion.Medical care · 2017Article
- TO THE EDITOR.Spine · 2017Article
- A causal model for longitudinal randomised trials with time-dependent non-compliance.Statistics in medicine · 2015Article
- Analyzing longitudinal data to characterize the accuracy of markers used to select treatment.Statistics in medicine · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Patient noncompliance complicates the analysis of many randomized trials seeking to evaluate the effect of surgical intervention as compared with a nonsurgical treatment. If selection for treatment depends on intermediate patient characteristics or outcomes, then 'as-treated' analyses may be biased for the estimation of causal effects. Therefore, the selection mechanism for treatment and/or compliance should be carefully considered when conducting analysis of surgical trials. We compare the performance of alternative methods when endogenous processes lead to patient crossover. We adopt an underlying longitudinal structural mixed model that is a natural example of a structural nested model. Likelihood-based methods are not typically used in this context; however, we show that standard linear mixed models will be valid under selection mechanisms that depend only on past covariate and outcome history. If there are underlying patient characteristics that influence selection, then likelihood methods can be extended via maximization of the joint likelihood of exposure and outcomes. Semi-parametric causal estimation methods such as marginal structural models, g-estimation, and instrumental variable approaches can also be valid, and we both review and evaluate their implementation in this setting. The assumptions required for valid estimation vary across approaches; thus, the choice of methods for analysis should be driven by which outcome and selection assumptions are plausible.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.