Assessing the Scope and Predictors of Intentional Dose Non-adherence in Clinical Trials.

Background: Although there is broad agreement that the accurate estimation of non-adherence rates in clinical trials is essential to determining the dose-response relationship, treatment safety and efficacy effects, no accurate estimates have ever been produced.

Methods: This study used a novel platform combining artificial intelligence and virtual patient monitoring to identify and quantify the scope of unreported intentional non-adherence in clinical trials of new medical therapies. Nearly 260,000 observations were drawn from a convenience sample of 2976 study volunteers participating in 23 clinical trials of psychiatric, neurological and neuromuscular diseases.

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy.

Background And Purpose: This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding.

Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia.

Background: Accurately monitoring and collecting drug adherence data can allow for better understanding and interpretation of the outcomes of clinical trials. Most clinical trials use a combination of pill counts and self-reported data to measure drug adherence, despite the drawbacks of relying on these types of indirect measures. It is assumed that doses are taken, but the exact timing of these events is often incomplete and imprecise.

Mitigating the Effects of Nonadherence in Clinical Trials.

Accounting for subject nonadherence and eliminating inappropriate subjects in clinical trials are critical elements of a successful study. Nonadherence can increase variance, lower study power, and reduce the magnitude of treatment effects. Inappropriate subjects (including those who do not have the illness under study, fail to report exclusionary conditions, falsely report medication adherence, or participate in concurrent trials) confound safety and efficacy signals.