Efficiency of Multivariate Tests in Trials in Progressive Supranuclear Palsy
arxiv(2023)
摘要
Measuring disease progression in clinical trials for testing novel treatments
for multifaceted diseases as Progressive Supranuclear Palsy (PSP), remains
challenging. In this study we assess a range of statistical approaches to
compare outcomes measured by the items of the Progressive Supranuclear Palsy
Rating Scale (PSPRS). We consider several statistical approaches, including sum
scores, as an FDA-recommended version of the PSPRS, multivariate tests, and
analysis approaches based on multiple comparisons of the individual items. We
propose two novel approaches which measure disease status based on Item
Response Theory models. We assess the performance of these tests in an
extensive simulation study and illustrate their use with a re-analysis of the
ABBV-8E12 clinical trial. Furthermore, we discuss the impact of the
FDA-recommended scoring of item scores on the power of the statistical tests.
We find that classical approaches as the PSPRS sum score demonstrate moderate
to high power when treatment effects are consistent across the individual
items. The tests based on Item Response Theory models yield the highest power
when the simulated data are generated from an IRT model. The multiple testing
based approaches have a higher power in settings where the treatment effect is
limited to certain domains or items. The FDA-recommended item rescoring tends
to decrease the simulated power. The study shows that there is no
one-size-fits-all testing procedure for evaluating treatment effects using
PSPRS items; the optimal method varies based on the specific effect size
patterns. The efficiency of the PSPRS sum score, while generally robust and
straightforward to apply, varies depending on the effect sizes' patterns
encountered and more powerful alternatives are available in specific settings.
These findings can have important implications for the design of future
clinical trials in PSP.
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