Adjustment for Variable Adherence Under Hierarchical Structure

Holmes, Tyson H.; Zulman, Donna M.; Kushida, Clete A.

doi:10.1097/mlr.0000000000000464

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Because of this deception ( fabrication ) and poor overall adherence, valid conclusions could only be made in 6 out of 34 patients. In intention-to-treat analyses, undetected non-adherence may lead to biased estimates of treatment effects when analyses are misinterpreted as assessments of treatment as received(16). Rebound effects (due to sudden uncounteracted physiologic responses to the actions of the withdrawn drug) and recurrent first dose effects from drug holidays may confound efficacy and side effects of a new drug(17).…”

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confidence: 99%

Deception in clinical trials and its impact on recruitment and adherence of study participants

Lee

Holmes

Neri

et al. 2018

Contemporary Clinical Trials

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Deceptive practices by participants in clinical research are prevalent. It has been shown that as high as 75% of participants withheld information to avoid exclusion from studies. Self-reported adherence has been found to be largely inaccurate. Overcoming deception is a critical issue, since the safety of study participants, the integrity of research data and research resources are at risk. In this review article, we examine deception from the perspective of investigators conducting clinical trials; we describe the types (concealment, fabrication, drug holidays and collusion), prevalence, risks, and predictors of deception, and propose an approach to reduce the impact of deception, especially on adherence, in clinical trials.

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Section: Introductionmentioning

confidence: 99%

Deception in clinical trials and its impact on recruitment and adherence of study participants

Lee

Holmes

Neri

et al. 2018

Contemporary Clinical Trials

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View full text Add to dashboard Cite

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Joint mixed‐effects models for causal inference with longitudinal data

Shardell

Ferrucci

2017

Statistics in Medicine

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Causal inference with observational longitudinal data and time-varying exposures is complicated due to the potential for time-dependent confounding and unmeasured confounding. Most causal inference methods that handle time-dependent confounding rely on either the assumption of no unmeasured confounders or the availability of an unconfounded variable that is associated with the exposure (eg, an instrumental variable). Furthermore, when data are incomplete, validity of many methods often depends on the assumption of missing at random. We propose an approach that combines a parametric joint mixed-effects model for the study outcome and the exposure with g-computation to identify and estimate causal effects in the presence of time-dependent confounding and unmeasured confounding. G-computation can estimate participant-specific or population-average causal effects using parameters of the joint model. The joint model is a type of shared parameter model where the outcome and exposure-selection models share common random effect(s). We also extend the joint model to handle missing data and truncation by death when missingness is possibly not at random. We evaluate the performance of the proposed method using simulation studies and compare the method to both linear mixed- and fixed-effects models combined with g-computation as well as to targeted maximum likelihood estimation. We apply the method to an epidemiologic study of vitamin D and depressive symptoms in older adults and include code using SAS PROC NLMIXED software to enhance the accessibility of the method to applied researchers.

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Adherence to continuous positive airway pressure improves attention/psychomotor function and sleepiness: a bias-reduction method with further assessment of APPLES

Holmes

Kushida

2017

Sleep Medicine

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Objective/Background Variable adherence to prescribed therapies for sleep disorders is commonplace. This study was designed to integrate three available statistical technologies (instrumental variables, residual inclusion, and shrinkage) to allow sleep investigators to employ data on variable adherence in the estimation of the causal effect of treatment as received on clinical outcomes. Patients/Methods Using data from the Apnea Positive Pressure Long-term Efficacy Study (APPLES), regression adjustment for observed and unobserved confounders was applied to two primary neurocognitive outcomes, plus two measures of sleepiness. We demonstrate how to obtain estimates of reduced uncertainty for the causal effect of treatment as received for continuous positive airway pressure (CPAP) within clinical subpopulations (defined by baseline disease severity) of sleep apnea patients. Results and Conclusions Following six months of treatment, statistically significant improvements caused by device adherence were detected for subjective sleepiness in mild, moderate and severe disease, objective sleepiness in severe disease, and attention and psychomotor function in moderate disease. Some evidence for worsening of learning and memory due to increased adherence in moderate disease was also detected. Application to APPLES illustrates that this method can yield bias corrections for unobserved confounders that are substantial—revealing new clinical findings. Use of this fully general method throughout sleep research could sharpen understanding of the true efficacy of pharmacotherapies, medical devices, and behavioral interventions. Extensive technical appendices are provided to facilitate application of this general method. Clinicaltrials.gov identifier NCT00051363.

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Adjustment for Variable Adherence Under Hierarchical Structure

Cited by 4 publications

References 44 publications

Deception in clinical trials and its impact on recruitment and adherence of study participants

Deception in clinical trials and its impact on recruitment and adherence of study participants

Joint mixed‐effects models for causal inference with longitudinal data

Adherence to continuous positive airway pressure improves attention/psychomotor function and sleepiness: a bias-reduction method with further assessment of APPLES

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