Journal article
Predicting an optimal composite outcome variable for Huntington's disease clinical trials
Journal of applied statistics, Vol.48(7), pp.1339-1348
05/19/2021
DOI: 10.1080/02664763.2020.1759034
PMCID: PMC8132919
PMID: 34024983
Abstract
While there is no known cure for Huntington's disease (HD), there are early-phase clinical trials aimed at altering disease progression patterns. There is, however, no obvious single outcome for these trials to evaluate treatment efficacy. Currently used outcomes are, while reasonable, not optimal in any sense. In this paper we derive a method for constructing a composite variable via a linear combination of clinical measures. Our composite variable optimizes the signal-to-noise ratio (SNR) within the context of a longitudinal study design. We also demonstrate how to induce sparsity using a soft-approximation of an
penalty on the coefficients of the composite variable. We applied our method to data from the TRACK-HD study, a longitudinal study aimed at establishing good outcome measures for HD, and found that compared to the existing composite measurement our composite variable provides a larger SNR and allows clinical trials with smaller sample sizes to achieve equivalent power.
Details
- Title: Subtitle
- Predicting an optimal composite outcome variable for Huntington's disease clinical trials
- Creators
- Daniel K Sewell - University of IowaJourney Penney - University of IowaMelissa Jay - University of IowaYing Zhang - University of Nebraska Medical CenterJane S Paulsen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of applied statistics, Vol.48(7), pp.1339-1348
- Publisher
- Taylor & Francis
- DOI
- 10.1080/02664763.2020.1759034
- PMID
- 34024983
- PMCID
- PMC8132919
- ISSN
- 0266-4763
- eISSN
- 1360-0532
- Grant note
- DOI: 10.13039/100006108, name: National Center for Advancing Translational Sciences, and the National Institutes of Health, award: NS040068, NS105509, NS103475; DOI: 10.13039/100000001, name: National Science Foundation, award: 000390183
- Language
- English
- Date published
- 05/19/2021
- Academic Unit
- Psychiatry; Psychological and Brain Sciences; Biostatistics; Public Policy Center (Archive)
- Record Identifier
- 9984227042202771
Metrics
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