Objective: Parkinson’s disease clinical studies for disease modifying therapies face challenges of large sample size and long study duration due to subtle changes at early stages and fluctuating symptoms that increase with disease progression and use of dopaminergic agents. We compare digital biomarkers to clinical outcomes and estimate study sample size for a proof-of-concept study using each type of outcome. An optimized scoring and weighting of the MDS-UPDRS assessment (PARCOMS) is also compared. Digital markers are shown to be substantially more sensitive outcomes for measuring disease progression (higher signal to noise ratio).

Background: Digital biomarkers collected by smartphone and smartwatch apps in PD are gaining momentum in clinical research. Since digital outcomes are more objective and more frequent than rater assessments, they capture and record the fluctuating symptoms. Digital markers have strong patient adherence (96.25%), satisfaction, and chronometric properties. We explored the advantages of digital biomarkers as markers of disease progression for proof-of-concept PD trials.

Method: Roche’s digital assessment items are compared with the MDS-UPDRS parts 2 and 3, and the PARCOMS optimized composite scale. Decline rates and standard deviations were found in the literature. Using data from Pasadena, PPMI, and Holden trial publications, we calculated the placebo Mean to Standard Deviation Ratio (MSDR) (signal to noise ratio) of disease progression over time. Cohen’s d values of the digital biomarker, MDS-UPDRS2/3, and PARCOMS2/3 assume a 30% reduction in disease progression. Sample size and power calculations were compared between clinical measures, optimized clinical measures and digital outcomes.

Results: The PARCOMS publications reported responsive items, all of which are measured by the digital app with high reliability. Digital biomarkers have the highest MSDR which is almost twice that of MDS-UPDRS and PARCOMS. For a PD trial with a 30% treatment effect, ~106 completers (152 enrolled participants), randomized 1:1 achieves 80% power at alpha=0.10 one-sided which may be sufficient for proof of concept (Table 1).

Conclusion: Leveraging digital biomarkers for proof of concept could increase sensitivity to progression and require a sample size of only ~150 enrolled, and 106 completers.

Table 1

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MDS Abstracts - https://www.mdsabstracts.org/abstract/proof-of-concept-study-design-leveraging-digital-biomarkers-to-enhance-statistical-power-and-reduce-sample-size-in-pd-trials/