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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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