Inadequate assessment of adherence to maintenance medication leads to loss of power and increased costs in trials of severe asthma therapy: results from a systematic literature review and modelling study.
Matshediso C MokokaMelissa J McDonnellElaine MacHaleBreda CushenFiona BolandSarah CormicanChristina DohertyProf Frank DoyleRichard W CostelloGarrett GreenePublished in: The European respiratory journal (2019)
Adherence to inhaled maintenance therapy in severe asthma is rarely adequately assessed, and its influence on trial outcomes is unknown. We systematically determined how adherence to maintenance therapy is assessed in clinical trials of "add-on" therapy for severe asthma. We model the improvement in trial power that could be achieved by accurately assessing adherence.A systematic search of six major databases identified randomised trials of add-on therapy for severe asthma. The relationship between measuring adherence and study outcomes was assessed. An estimate of potential improvements in statistical power and sample size was derived using digitally recorded adherence trial data.87 randomised controlled trials enrolling 22 173 participants were included. Adherence assessment was not reported in 67 trials (n=13 931, 63%). Studies that reported adherence used a range of self-report and subjective methods. None of the studies employed an objective assessment of adherence. Studies that reported adherence had a significantly reduced pooled variance in forced expiratory volume in 1 s (FEV1) compared to those that did not assess adherence: s2=0.144 L2 versus s2=0.168 L2, p<0.0001. Power to detect clinically relevant changes in FEV1 was significantly higher in trials that reported adherence assessment (mean power achieved 59% versus 49%). Modelling suggests that up to 50% of variance in FEV1 outcomes is attributable to undetected variations in adherence. Controlling for such variations could potentially halve the required sample size.Few trials of add-on therapy monitor adherence to maintenance inhaled therapy, resulting in a greater variance in trial outcomes and inadequate power for determining efficacy.
Keyphrases
- clinical trial
- glycemic control
- study protocol
- stem cells
- randomized controlled trial
- phase iii
- healthcare
- adipose tissue
- type diabetes
- machine learning
- mesenchymal stem cells
- skeletal muscle
- big data
- phase ii
- physical activity
- bone marrow
- electronic health record
- weight loss
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- smoking cessation
- drug induced