ArticleBiometrics2015
Treatment decisions based on scalar and functional baseline covariates.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A sparse additive model for treatment effect-modifier selection.Biostatistics (Oxford, England) · 2022Trial
- Article
- A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules.Stat · 2022Article
- A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.Statistica Sinica · 2021Article
- Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.Journal of the American Statistical Association · 2020Article
- 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
- A general statistical framework for subgroup identification and comparative treatment scoring.Biometrics · 2017Article
- LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS.The annals of applied statistics · 2017Article
- 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
- Generated effect modifiers (GEM's) in randomized clinical trials.Biostatistics (Oxford, England) · 2017Article
- Flexible functional regression methods for estimating individualized treatment regimes.Stat (International Statistical Institute) · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.