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Comparing Regression Modeling Strategies for Predicting Hometime by Jessalyn K. Holodinsky; Amy Y. X. Yu; Moira K. Kapral; Peter C. Austin is a Mathematics article available to read on EtoBox.

What is Comparing Regression Modeling Strategies for Predicting Hometime about?

## Background Hometime, the total number of days a person is living in the community (not in a healthcare institution) in a defined period of time after a hospitalization, is a patient-centred outcome metric increasingly used in healthcare research. Hometime exhibits several properties which make its statistical analysis difficult: it has a highly non-normal distribution, excess zeros, and is bounded by both a lower and upper limit. The optimal methodology for the analysis of hometime is currently unknown. ## Methods Using administrative data we identified adult patients diagnosed with stroke between April 1, 2010 and December 31, 2017 in Ontario, Canada. 90-day hometime and clinically relevant covariates were determined through administrative data linkage. Fifteen different statistical and machine learning models were fit to the data using a derivation sample. The models’ predictive accuracy and bias were assessed using an independent validation sample. ## Results Seventy-five thousand four hundred seventy-five patients were identified (divided into a derivation set of 49,402 and a test set of 26,073). In general, the machine learning models had lower root mean square error and me

Who reads Comparing Regression Modeling Strategies for Predicting Hometime?

Author

Jessalyn K. Holodinsky; Amy Y. X. Yu; Moira K. Kapral; Peter C. Austin

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Mathematics (Physical Sciences)