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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Persistent symptoms after elective sigmoid resection for diverticulitis

PURPOSE: Despite technically successful surgery for diverticular disease, a significant group of patients who experience persistent or recurrent symptoms remains. This study was designed to determine the incidence and pattern of persistent symptoms and their association with peroperative parameters. METHODS: Follow-up (33 (range, 4-72) months) through structured interviews with patients who had surgery for diverticulitis in our department from December 1999 to November 2004 was conducted. Of 162 patients, 124 (76.5 percent) were available for follow-up. Nonparametric tests were used for comparison of patients who had undergone elective (n = 68) or emergency (n = 56) procedures. RESULTS: Of patients who had elective surgery, 25 percent suffered persistent symptoms, including painful constipation, painful abdominal distension, abdominal cramps, and frequent painful diarrhea. Neither the stage of disease (complicated or uncomplicated) nor the surgical technique (laparotomy or laparoscopy) were significantly related to the occurrence of symptoms. Recurrent diverticulitis was not observed. Similar results were obtained from comparisons with emergency patients. CONCLUSIONS: The prevalence of persistent symptoms after successful surgery for diverticular disease may be an additional reason to carefully discuss the indication for prophylactic surgery. In any case, preoperative counseling and informed consent regarding the possibility of persistent symptoms after prophylactic elective surgery is essential.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.