Understanding Adherence Requires Pragmatic Trials

Apter, Andrea J.

doi:10.1001/jamapediatrics.2014.3661

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…The Breathe Well study was a pragmatic interventional study designed to evaluate adherence in pediatric asthma by comparing usual care to speech recognition calls using personalized EHR information (eg, child's name and sex, physician name, medication name) when inhaled corticosteroid refills were due. 67,68 The intervention resulted in a 25% increase in adherence (9% absolute difference); however, there was no observed difference in sick visits or β-agonist use. 67 The study capitalized on Kaiser Permanente databases to identify participants (enrolling >1100 children) and collect outcome measures, highlighting that EHR databases can facilitate the execution of large-scale pragmatic trials.…”

Section: Late-phase Studiesmentioning

confidence: 95%

“…Although pragmatic drug trials in children are generally lacking, recent interventional pragmatic trials provide some insight into trial execution. The Breathe Well study was a pragmatic interventional study designed to evaluate adherence in pediatric asthma by comparing usual care to speech recognition calls using personalized EHR information (eg, child's name and sex, physician name, medication name) when inhaled corticosteroid refills were due . The intervention resulted in a 25% increase in adherence (9% absolute difference); however, there was no observed difference in sick visits or β‐agonist use .…”

Section: Clinical Trial Optimizationmentioning

confidence: 99%

“…The study capitalized on Kaiser Permanente databases to identify participants (enrolling >1100 children) and collect outcome measures, highlighting that EHR databases can facilitate the execution of large‐scale pragmatic trials. However, participants were required to have Kaiser Permanente insurance, a working telephone, and to have filled at least 1 prescription in the 6 months before enrollment, potentially excluding those with very low adherence, low income, and at risk for poor outcomes . Broad inclusion criteria are therefore essential to optimize generalizability when designing pediatric pragmatic trials.…”

Section: Clinical Trial Optimizationmentioning

confidence: 99%

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Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children

Balevic

Cohen‐Wolkowiez

2018

The Journal of Clinical Pharma

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Almost half of recent pediatric trials failed to achieve labeling indications, due in large part to inadequate study design. Therefore, innovative study methods are crucial to optimize trial design while also reducing the potential harms inherent with drug investigation. Several methods exist to optimize the amount of pharmacokinetic (PK) data collected from the smallest possible volume and with the fewest number of procedures, including the use of opportunistic and sparse sampling, alternative and non-invasive matrices, and micro-volume assays. In addition, large research networks using master protocols promote collaboration, reduce regulatory burden, and increase trial efficiency for both early- and late-phase trials. Large pragmatic trials that leverage electronic health records can capitalize on central management strategies to reduce costs, enroll patients with rare diseases on a large scale, and augment study generalizability. Further, trial efficiency and safety can be optimized through Bayesian adaptive techniques that permit planned protocol changes based on analyses of prior and accumulated data. In addition to these trial design features, advances in modeling and simulation have paved the way for systems-based and physiologically-based models that individualize pediatric dosing recommendations and support drug approval. Lastly, given the low prevalence of many pediatric diseases, collecting de-identified genetic and clinical data on a large scale is a potentially transformative way to augment clinical pharmacology research in children.

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Section: Late-phase Studiesmentioning

confidence: 95%

Section: Clinical Trial Optimizationmentioning

confidence: 99%

Section: Clinical Trial Optimizationmentioning

confidence: 99%

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Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children

Balevic

Cohen‐Wolkowiez

2018

The Journal of Clinical Pharma

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“…Various platforms, including mailed questionnaires, web-based forms, 152 and research kits, are used for these kinds of trials [10,15,18]. Consequently, 153 pragmatism is an emerging source of clinical evidence, from pediatric asthma and 154 cardiovascular diseases to monetary incentives for smoking cessation [6,42,95]. However, 155 it is subject to several challenges such as patient recruitment, insufficient data 156 collection, and treatment variability [33,101].…”

mentioning

confidence: 99%

“…We calculated DRW of a participant when we computed all the cutoff values of DRW 248 indexes for each CRFs (5,6). N is the number of CRFs assigned to a participant.…”

mentioning

confidence: 99%

Reliable data collection in participatory trials to assess digital healthcare apps

Park¹,

Park

Kim

et al. 2020

Preprint

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Digital healthcare mobile apps are experiencing exponential growth due to the development of the mobile network and widespread usage of smartphones. Unlike high growth rates, sufficient validated apps are few. Because mobile health apps are low-risk technology, and it tends to reduce regulatory oversight. With this tendency, the apps have a direct trade characteristic between developers and end-users. The existing platforms are not suitable to collect reliable data for evaluating the effectiveness of the apps. Moreover, these platforms only reflect the perspectives of developers and experts, not end-users. For instance, the methods of data collection in clinical trials are not appropriate for participant-oriented assessment of healthcare apps since its complexity and high cost. Thus, we identified a need for a participant-oriented data collection platform for end-users as an interpretable, systematic, and sustainable tool-as a first step to validate the effectiveness of the apps. To collect reliable data in the participatory trial format, we have defined data preparation, storage, and sharing stage. Interpretable data preparation consists of a protocol database system and semantic feature retrieval method to create a protocol without professional knowledge. Collected data reliability weight calculation belongs to the systematic data storage stage. To sustainable data collection, we integrate the weight method and reward distribution function. We validate the methods with 718 of human participants to conduct statistical tests. The validation results showed that the methods have significant differences in the comparative experiment, and we convinced the methods are essential to reliable data collection. Furthermore, we created a web-based system for our pilot platform to collect reliable data in an integrated pipeline. We validate the platform features with existing clinical and pragmatic trial data collection platforms. In conclusion, we show that the method and platform support reliable data collection, forging a path to effectiveness validation of digital healthcare apps. 1/371 Digital healthcare has become one of the most promising fields in the healthcare 2 industry with the widespread popularity of wireless devices such as smartphones. 3Notably, approximately 200 mobile health apps are listed daily in mobile app stores, 4 and investments in digital health are booming [25,89]. While many of the apps are 5 general wellness applications that help users to manage their activities, some healthcare 6 apps claim to improve a symptom or disease directly. The treatments proposed by the 7 apps take the form of information or visual stimuli, which are effectively delivered to 8 users as digital therapy. reSET-O, an 84-day prescription digital therapeutic app for the 9 treatment of opioid use disorder, has been approved by the U.S. Food and Drug 10 Administration (FDA) [51]. Akili Interactive has clinically demonstrated that interactive 11 digital treatment through a video game, which is under review by the FDA, may 12 i...

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Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Hossain

Karim

2022

Pharmaceutical Statistics

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Pragmatic trials offer practical means of obtaining real‐world evidence to help improve decision‐making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable‐based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non‐adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.

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Understanding Adherence Requires Pragmatic Trials

Cited by 8 publications

References 7 publications

Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children

Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children

Reliable data collection in participatory trials to assess digital healthcare apps

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

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