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Jaime Lynn Speiser

Showing results (1-10 of 16) with videos related to

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Journal of Biomedical Informatics|March 30, 2021
A random forest method with feature selection for developing medical prediction models with clustered and longitudinal dataJaime Lynn Speiser
The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences|May 19, 2022
Waste Not, Want Not: Proper Design, Analysis, and Interpretation Are Essential to Advancing Aging Research Across the Translational Science SpectrumMichelle Shardell, Jaime Lynn Speiser
BMC Medical Research Methodology|April 11, 2025
OpenClustered: an R package with a benchmark suite of clustered datasets for methodological evaluation and comparisonNathaniel Sean O'Connell, Jaime Lynn Speiser
Statistics in Medicine|November 5, 2014
Random forest classification of etiologies for an orphan diseaseJaime Lynn Speiser, Valerie L Durkalski, William M Lee
Plos One|April 18, 2015
Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree modelsJaime Lynn Speiser, William M Lee, Constantine J Karvellas, et al.
Clinical Trials (London, England)|May 27, 2023
Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trialsJaime Lynn Speiser, Walter T Ambrosius, Nicholas M Pajewski
Expert Systems with Applications|September 24, 2020
A Comparison of Random Forest Variable Selection Methods for Classification Prediction ModelingJaime Lynn Speiser, Michael E Miller, Janet Tooze, et al.
Briefings in Bioinformatics|March 10, 2025
A comparison of random forest variable selection methods for regression modeling of continuous outcomesNathaniel S O'Connell, Byron C Jaeger, Garrett S Bullock, et al.
JMIR Medical Informatics|March 13, 2025
Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record DataMolly Ehrig, Garrett S Bullock, Xiaoyan Iris Leng, et al.
Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society|October 29, 2019
BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomesJaime Lynn Speiser, Bethany J Wolf, Dongjun Chung, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
Journal of Biomedical Informatics|March 30, 2021
A random forest method with feature selection for developing medical prediction models with clustered and longitudinal dataJaime Lynn Speiser
The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences|May 19, 2022
Waste Not, Want Not: Proper Design, Analysis, and Interpretation Are Essential to Advancing Aging Research Across the Translational Science SpectrumMichelle Shardell, Jaime Lynn Speiser
BMC Medical Research Methodology|April 11, 2025
OpenClustered: an R package with a benchmark suite of clustered datasets for methodological evaluation and comparisonNathaniel Sean O'Connell, Jaime Lynn Speiser
Statistics in Medicine|November 5, 2014
Random forest classification of etiologies for an orphan diseaseJaime Lynn Speiser, Valerie L Durkalski, William M Lee
Plos One|April 18, 2015
Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree modelsJaime Lynn Speiser, William M Lee, Constantine J Karvellas, et al.
Clinical Trials (London, England)|May 27, 2023
Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trialsJaime Lynn Speiser, Walter T Ambrosius, Nicholas M Pajewski
Expert Systems with Applications|September 24, 2020
A Comparison of Random Forest Variable Selection Methods for Classification Prediction ModelingJaime Lynn Speiser, Michael E Miller, Janet Tooze, et al.
Briefings in Bioinformatics|March 10, 2025
A comparison of random forest variable selection methods for regression modeling of continuous outcomesNathaniel S O'Connell, Byron C Jaeger, Garrett S Bullock, et al.
JMIR Medical Informatics|March 13, 2025
Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record DataMolly Ehrig, Garrett S Bullock, Xiaoyan Iris Leng, et al.
Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society|October 29, 2019
BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomesJaime Lynn Speiser, Bethany J Wolf, Dongjun Chung, et al.
Pageof 2