AAN 2024 | Optimizing MDS-UPDRS to detect meaningful change in motor progression
Samuel Dickson, PhD, Pentara Corporation, Millcreek, UT, talks on the re-weighting of MDS-UPDRS for optimal sensitivity to Parkinson’s disease (PD) progression. Optimizing outcome measures is critical for detecting disease modification in clinical trials. As PD impacts motor abilities across diverse domains, Dr Dickson and his team wanted to examine composite scores of re-weighted combinations of MDS-UPDRS items to optimize the detection of meaningful change in motor progression. Using Parkinson’s Progression Markers Initiative (PPMI) data, the sensitivity of individual items to disease progression was assessed and selected items were weighted and summed to create composite scores. Using the most response items increased power by 8%, compared to using the totality of items from existing tools. This interview took place at the American Academy of Neurology (AAN) Annual Meeting 2024 in Denver, CO.
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Transcript
The idea is that in clinical trials, especially complex diseases, we need to be able to have the outcomes that are the most sensitive to disease progression, otherwise we’ll fail, even in large studies, to find the right outcome for the treatments. For example, lecanemab and donanemab both relied, in their Phase II studies, on composite outcomes. Lecanemab with ADCOMS and donanemab with iADRS...
The idea is that in clinical trials, especially complex diseases, we need to be able to have the outcomes that are the most sensitive to disease progression, otherwise we’ll fail, even in large studies, to find the right outcome for the treatments. For example, lecanemab and donanemab both relied, in their Phase II studies, on composite outcomes. Lecanemab with ADCOMS and donanemab with iADRS. And had they not? If they had just used CDR Sum of Boxes, which failed to be significant in both those studies, maybe we wouldn’t have gone on to the next study. By reweighting MDS-UPDRS, we can optimize it to disease progression and make it easier to get the right result with smaller sample sizes or have more power.
What we’re presenting this week is our reweighting based on PPMI, but we’re also using CPP (the Critical Path for Parkinson’s disease data) to optimize the weights so that we can reduce or increase the signal-to-noise ratio. We’re reweighting the items in the MDS-UPDRS part two and three. At least one of them is a motor composite score. We’re doing that using partial least squares regression, with time as the dependent variable, which helps us to identify which things are affecting disease progression the most.
By reweighting the items in the MDS-UPDRS part two and three, we’ve been able to increase the signal-to-noise ratio by 13%, which means that for a study where you have the same sample size, you can increase the power by 8%. So, you go from 80% power to 88% power. Similarly, if you want to maintain 80% power, you can reduce your sample size by 22%.
So this would increase success for disease-modifying trials by making have more power with the same number of subjects. Therefore, for instance with lecanemab and donanemab, when they increased their sample size they were actually able to get success on CDR Sum of Boxes in both cases in their Phase III. This is like an automatic increase in sample size just by using a composite score.
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Disclosures
Sam Dickson is a Senior VP of Pentara Corporation, a company which consults with dozens of companies in the Alzheimer’s space, including Eisai, Biogen, Lilly, and Nutricia.
