Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.

Copper Range Railroad General Freight Department Rules and Regulations

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A treatment method for Class II Division 1 patients with extraction of permanent maxillary first molars

Throughout the years, various treatment modalities have been presented for the treatment of Class II Division 1 malocclusions. The goal of this paper is to present a treatment approach that involves the extraction of the maxillary first molars followed by use of fixed appliances with low-friction brackets. This treatment approach has proven to be an efficient treatment modality for Class II Division 1 malocclusions, especially with noncompliant patients.