Generating Novometric Confidence Intervals in R: Bootstrap Analyses to Compare Model and Chance ESS

Nathaniel J. Rhodes and Paul R. Yarnold

Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University & Optimal Data Analysis LLC

We introduce a method for evaluating the upper and lower bounds of statistical confidence [e.g., an exact discrete confidence interval (CI)] in the expected effect strength for sensitivity of a decision model. The method presented herein uses bootstrap simulations to determine if a given effect is robust and not attributable to random chance (i.e., the lower bound of the model bootstrap CI is greater than the upper bound of the chance bootstrap CI). We evaluated relatively weak, moderate, and strong effects using the novometric bootstrap method. Our approach should serve to increase confidence in the veracity of decision models.

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