Journal of Medical Internet Research

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Identifying Population Segments by Differing Levels of COVID-19 Vaccine Confidence and Evaluating Subsequent Uptake of COVID-19 Prevention Behaviors: Web-Based, Longitudinal, Probability-Based Panel Survey

Reference 29: Hazard rate estimation under random censoring with varying kernels and bandwidthsestimation

Joseph Luchman, Morgane Bennett, Elissa Kranzler, Rugile Tuskeviciute, Ronald Vega, Benjamin Denison, Sarah Trigger, Tyler Nighbor, Monica Vines, Leah Hoffman

JMIR Public Health Surveill 2024;10:e56044

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An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation

In our study, we demonstrated that patient and surgical features that are easy to collect from the electronic medical record can improve the estimation of surgical times using machine learning-based predictive models. Future implementation of machine learning-based models presents an alternative pathway to use electronic medical record data to advance surgical efficiency and enrich patient outcomes.

Rodney Allanigue Gabriel, Bhavya Harjai, Sierra Simpson, Austin Liu Du, Jeffrey Logan Tully, Olivier George, Ruth Waterman

JMIR Perioper Med 2023;6:e39650

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The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study

Our approach allows us to investigate and quantify the error in the estimation of a mean when the randomness of the missingness varies from semicomplete to not complete at all. We also provide an illustration of how increasing the sample size impacts an estimator when the data are not MAR.

Paul Samuel Weiss, Lance Allyn Waller

JMIR Public Health Surveill 2022;8(9):e37887

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Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study

Parameter estimation and inference were conducted under a fully Bayesian framework to better quantify uncertainties in predicted hospitalization rates, including those that are extrapolated to states without COVID-NET data.

Alexia Couture, A Danielle Iuliano, Howard H Chang, Neha N Patel, Matthew Gilmer, Molly Steele, Fiona P Havers, Michael Whitaker, Carrie Reed