AbstractBackgroundThere is considerable interest in adjusting for suboptimal adherence in randomized controlled trials. A per-protocol analysis, for example removes individuals who fail to achieve a minimal level of adherence. One can also reassign non-adherers to the control group, censor them at the point of non-adherence, or cross them over to the control. However, there are biases inherent in each of these methods. Here, we describe an application of causal modeling to address this issue.MethodsThe marginal structural model with inverse-probability weighting was implemented using a weighted generalized estimating equation model. Two ancillary models were developed to derive the weights. First, stepwise linear regression was used to mode...
In this dissertation, we develop and evaluate methods for adjusting for treatment non-compliance in ...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Marginal structural models are causal models designed to adjust for time-dependent confounders in ob...
AbstractBackgroundThere is considerable interest in adjusting for suboptimal adherence in randomized...
Summary: Randomized controlled trials of interventions to im-prove adherence to antiretroviral medic...
In pragmatic trials, treatment strategies are randomly assigned at baseline, but patients may not ad...
In pragmatic trials, treatment strategies are randomly assigned at baseline, but patients may not ad...
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are o...
Introduction: The instrumental variable (IV)-based methods (e.g., two-stage least square [2SLS], two...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
In this dissertation, we develop and evaluate methods for adjusting for treatment non-compliance in ...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Marginal structural models are causal models designed to adjust for time-dependent confounders in ob...
AbstractBackgroundThere is considerable interest in adjusting for suboptimal adherence in randomized...
Summary: Randomized controlled trials of interventions to im-prove adherence to antiretroviral medic...
In pragmatic trials, treatment strategies are randomly assigned at baseline, but patients may not ad...
In pragmatic trials, treatment strategies are randomly assigned at baseline, but patients may not ad...
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are o...
Introduction: The instrumental variable (IV)-based methods (e.g., two-stage least square [2SLS], two...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
In this dissertation, we develop and evaluate methods for adjusting for treatment non-compliance in ...
Protocol non-adherence is common and poses unique challenges in the interpretation of trial outcomes...
Marginal structural models are causal models designed to adjust for time-dependent confounders in ob...