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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 SpeiserThe Journals of Gerontology. Series A, Biological Sciences and Medical Sciences|November 5, 2024
Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big DataChixiang Chen, Terrence E Murphy, Jaime Lynn Speiser, et al.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 SpeiserStatistics in Medicine|November 5, 2014
Random forest classification of etiologies for an orphan diseaseJaime Lynn Speiser, Valerie L Durkalski, William M LeePlos 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 PajewskiExpert 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.Statistics in Medicine|August 17, 2016
Instrumental variable analysis of multiplicative models with potentially invalid instrumentsMichelle Shardell, Luigi FerrucciJournal of Alzheimer'S Disease : JAD|March 3, 2025
Genetic geroscience and Alzheimer's disease: The pleiotropy is the point!Michelle Shardell, Chixiang ChenStatistics in Medicine|December 6, 2017
Joint mixed-effects models for causal inference with longitudinal dataMichelle Shardell, Luigi FerrucciPageof 14