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Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Categorical variables were assessed using a 1-sided chi-square test or Fisher exact test and continuous variables using a 2-sided t test. Missing values were imputed using the random forest missing data algorithm (impute.rfsrc function in R package random Forest SRC) [23]. Three modeling strategies were used to identify potential predictors of NGS-based testing with 2 sets of outcomes for ever versus never NGS-tested (model 1) and early versus late NGS-tested (model 2).

Alan James Michael Brnabic, Ilya Lipkovich, Zbigniew Kadziola, Dan He, Peter M Krein, Lisa M Hess

JMIR Cancer 2025;11:e64399

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