Can machine learning improve randomized clinical trial analysis?-Reference-Cited by-同舟云学术

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Can machine learning improve randomized clinical trial analysis?

Published:2021-10 Issue: Volume:91 Page:499-502
ISSN:1059-1311
Container-title:Seizure
language:en
Short-container-title:Seizure

Author:

Romero Juan,Chiang Sharon,Goldenholz Daniel M.

Funder

Harvard Medical School

National Institute of Neurological Disorders and Stroke

Publisher

Elsevier BV

Subject

Neurology (clinical),Neurology,General Medicine

Reference8 articles.

1. Treatment outcomes in patients with newly diagnosed epilepsy treated with established and new antiepileptic drugs a 30-year longitudinal cohort study;Chen;JAMA Neurol,2018

2. Primary efficacy endpoint in clinical trials of antiepileptic drugs: change or percentage change;Siddiqui;Drug Inf J,2010

3. Comparing the efficacy, exposure, and cost of clinical trial analysis methods;Oliveira;Epilepsia,2019

4. Natural variability in seizure frequency: Implications for trials and placebo;Romero;Epilepsy Res,2020

5. Statistical efficiency of patient data in randomized clinical trials of epilepsy treatments;Romero;Epilepsia,2020

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1. Minimum clinical utility standards for wearable seizure detectors: A simulation study;Epilepsia;2024-02-17

2. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist;Current Neurology and Neuroscience Reports;2023-12

3. Increasing challenges to trial recruitment and conduct over time;Epilepsia;2023-07-25

4. Prescriptive Method for Optimizing Cost of Data Collection and Annotation in Machine Learning of Clinical Ultrasound;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24

5. Machine Learning in Clinical Trials: A Primer with Applications to Neurology;Neurotherapeutics;2023-07

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