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Prediction Summary Measures for a Nonlinear Model and for Right-Censored Time-to-Event Data

Room 306, Statistics Building 1130
Gang Li
University of California, Los Angeles

The R-squared statistic, or coefficient of determination, is commonly used to measure the predictive power of a linear model.  It is interpreted as the fraction of variation in the response explained by the predictors. Despite its popularity, a direct equivalent measure is not available for nonlinear regression models and for right-censored time-to-event data. In this talk, I will show that in addition to a measure of explained variation, another measure of explained prediction error is required to assess the predictive power of a nonlinear model. The measures are well defined even for a mis-specified model. I will discuss how to extend these measures to right censored time-to-event data and illustrate the methodology using simulated and real data examples.

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