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Income and Depression: A Predictive Model for Clinical Trials Patient Dropout
Moe Alsumidaie looks at data collection methods and concepts that can result in predicting patients at risk of dropping out from a clinical trial.
Limited research has been conducted on factors that impact patient dropout; some research suggests that patients who are less physically active were 7.3 times more likely to drop out of a clinical trial, whereas unemployed patients were 4.7 times more likely to drop out. Other research indicates that clinical trial dropout factors may include age, gender, education, and that depressed patients are particularly at risk of attrition.
The subject of patient retention and engagement is starting to generate interest in the clinical research industry, however, due to the limitations of data explaining why patients dropout, study teams are implementing generalized programs in order to minimize subject attrition.
In this article, Applied Clinical Trials' Moe Alsumidaie introduces data collection methods and concepts that can result in predicting patients at risk of dropping out from a clinical trial. Presuming that depression is a risk factor for patient dropout, we will analyze the impact of income on depression rates, and then apply the concept towards clinical trial risk indicator development.
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Key Findings of the NIAGARA and HIMALAYA Trials
November 8th 2024In this episode of the Pharmaceutical Executive podcast, Shubh Goel, head of immuno-oncology, gastrointestinal tumors, US oncology business unit, AstraZeneca, discusses the findings of the NIAGARA trial in bladder cancer and the significance of the five-year overall survival data from the HIMALAYA trial, particularly the long-term efficacy of the STRIDE regimen for unresectable liver cancer.
Novel GLP-1 Receptor Agonist Demonstrates Promising Results Treating Patients with Obesity
January 21st 2025Data from a Phase Ia single ascending dose study found that ASC30 demonstrated dose-proportional pharmacokinetics, a half-life of up to 60 hours, and superior pharmacokinetic properties compared to other oral GLP-1 receptor agonists.
Cell and Gene Therapy Check-in 2024
January 18th 2024Fran Gregory, VP of Emerging Therapies, Cardinal Health discusses her career, how both CAR-T therapies and personalization have been gaining momentum and what kind of progress we expect to see from them, some of the biggest hurdles facing their section of the industry, the importance of patient advocacy and so much more.
Artificial Intelligence Makes Possible a Multiomic Approach in Oncology Drug Discovery
January 7th 2025While challenges remain, AI is accelerating the process by enabling researchers to identify and design new drug candidates more quickly and efficiently with applications in target discovery, structure prediction, and drug optimization.
