Localizing differences in smooths with simultaneous confidence bounds on the true discovery proportion
David Swanson

TL;DR
This paper introduces a method to identify where two smooth functions differ by providing simultaneous confidence bounds on the true discovery proportion, improving interpretability and statistical rigor in comparing smooths.
Contribution
The paper presents a novel approach using true discovery proportion and simultaneous confidence bounds to localize differences between smooths without ad hoc data subsetting.
Findings
Method accurately localizes differences in simulations
Provides high-confidence lower bounds on true discoveries
Effective in analyzing gait differences in cerebral palsy patients
Abstract
We demonstrate a method for localizing where two spline terms, or smooths, differ using a true discovery proportion (TDP) based interpretation. The procedure yields a statement on the proportion of some region where true differences exist between two smooths, which results from use of hypothesis tests on collections of basis coefficients parameterizing the smooths. The methodology avoids otherwise ad hoc means of making such statements like subsetting the data and then performing hypothesis tests on the truncated spline terms. TDP estimates are 1-alpha confidence bounded simultaneously. This means that the TDP estimate for a region is a lower bound on the proportion of actual difference, or true discoveries, in that region with high confidence regardless of the number of regions at which TDP is estimated. Our procedure is based on closed-testing using Simes local test. This local test…
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods in Clinical Trials · Advanced Statistical Methods and Models
