ArticleStat (International Statistical Institute)2016
Flexible functional regression methods for estimating individualized treatment regimes.
Article in Stat (International Statistical Institute), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Article
- Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial.BJPsych open · 2019Article
- Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown.Journal of the Royal Statistical Society. Series C, Applied statistics · 2018Article
- Statistical Analysis Plan for Stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) Study.Contemporary clinical trials communications · 2017Article
- Functional feature construction for individualized treatment regimes.Journal of the American Statistical Association · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A major focus of personalized medicine is on the development of individualized treatment rules. Good decision rules have the potential to significantly advance patient care and reduce the burden of a host of diseases. Statistical methods for developing such rules are progressing rapidly, but few methods have considered the use of pre-treatment functional data to guide in decision-making. Furthermore, those methods that do allow for the incorporation of functional pre-treatment covariates typically make strong assumptions about the relationships between the functional covariates and the response of interest. We propose two approaches for using functional data to select an optimal treatment that address some of the shortcomings of previously developed methods. Specifically, we combine the flexibility of functional additive regression models with
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Registered trials
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