Kernel smoothing for ROC curve and estimation for thyroid stimulating hormone

Receiver Operating Characteristic (ROC) Curves are frequently used in biomedical informatics research to evaluate classification and prediction models to support decision, diagnosis, and prognosis. ROC analysis investigates the accuracy of models and has ability to separate positive from negative cases. It is especially useful in evaluating predictive models and compare to other tests which produce output values in a continuous range. Empirical ROC curve is jagged but a true ROC curve is smooth. For this purpose kernel smoothing were used. The Area Under ROC Curve (AUC) frequently is used as a measure of the effectiveness of diagnostic markers. In this study we compare estimation of this area based on normal assumptions and kernel smoothing. This study used measurements of TSH from patients and non-diseased people of congenital hypothyroidism screening in Isfahan province. Using the method, TSH ROC curves from Isfahani's infants were fitted. For evaluating of accuracy of this test, AUC and its standard error calculated. Also effectiveness of the kernel methods in comparison to other methods showed

Estimation of the parameters in the truncated normal distribution when the truncation point is known

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN016956 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

EPIDEMIOLOGY OF CANCERS IN ISFAHAN PROVINCE: A RETROSPECTIVE STUDY (1981-1996)

Introduction. Malignancies occur word wide with variety of patterns depending on an individual's environmental situation and life style. Having data about the distribution and incidence of different malignancies is necessary for the formulation of an effective prevention plan for any specific area. Methods. In the central part of Iran (Isfahan Province) a retrospective study was designed to describe cancers' epidemiologic factors by collecting patients' data from the Isfahan Oncology center for the period of 1981-1996.
 Results. The frequency of malignancies was as follows: Skin cancers 5111 cases with a frequency of 20.9 percent (62.9 percent males and 37.3 percent females). Leukemia 3108 cases with a frequency of 12.7 percent (59.9 percent males and 40.1 percent females). Breast cancer 2796 cases with a frequency of 11.4 percent (6.5 percent males and 93.7 percent females). cancers of the digestive system 2017 cases (60.5 percent males and 39.5 percent females). Non Hodgekin lymphoma 1953 cases with relative frequency of 8 percent (66.9 percent male and 33.1 percent females). cancers of the urinary system 1567 cases with a frequency 7.6 percent (82.5 males and 17.5 females). Head and neck cancers 1545 cases (6.3 percent). Cancer of the reproductive system 1313 cases with a frequency of 5.37 percent. Tumors of the central nervous system 1276 cases with a frequency of 5.22 percent. Lung cancers 933 cases with a frequency of 3.7 percent (80.7 percent males and 19.3 percent females). Hodgekins 913 cases with a frequency of 3.7 percent. Sarcoma of the soft tissue 801 cases (3.28 percent) and bone tissue 657 cases (2.7 percent). Endocrine malignancies 422 cases and 1.73 percent of all was at the bottom of categories during the period of study.
 Discussion. In the present study, the prevalence of acute leukemia and also lymphatic and hodgekine malignancies were more frequent than the world wide data presented in the literature. The frequency of lung tumors, however, was less than in the developed countries

External Validation of Three Prediction Tools for Patients at Risk of a Complicated Course of Clostridium difficile

OBJECTIVE Estimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011). METHODS The validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules. RESULTS A complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78. CONCLUSION All 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings