Statistical Models Commons^™

Beta Binomial Regression, Joseph M. Hilbe

Joseph M Hilbe

Monograph on how to construct, interpret and evaluate beta, beta binomial, and zero inflated beta-binomial regression models. Stata and R code used for examples.

諸外国における最新のデータエディティング事情～混淆正規分布モデルによる多変量外れ値検出法の検証～(高橋将宜、選択的エディティング、セレクティブエディティング), Masayoshi Takahashi

Masayoshi Takahashi

No abstract provided.

Global Quantitative Assessment Of The Colorectal Polyp Burden In Familial Adenomatous Polyposis Using A Web-Based Tool, Patrick M. Lynch, Jeffrey S. Morris, William A. Ross, Miguel A. Rodriguez-Bigas, Juan Posadas, Rossa Khalaf, Diane M. Weber, Valerie O. Sepeda, Bernard Levin, Imad Shureiqi

Jeffrey S. Morris

Background: Accurate measures of the total polyp burden in familial adenomatous polyposis (FAP) are lacking. Current assessment tools include polyp quantitation in limited-field photographs and qualitative total colorectal polyp burden by video.

Objective: To develop global quantitative tools of the FAP colorectal adenoma burden.

Design: A single-arm, phase II trial.

Patients: Twenty-seven patients with FAP.

Intervention: Treatment with celecoxib for 6 months, with before-treatment and after-treatment videos posted to an intranet with an interactive site for scoring.

Main Outcome Measurements: Global adenoma counts and sizes (grouped into categories: less than 2 mm, 2-4 mm, and greater than 4 mm) were …

Bayesian Nonparametric Regression And Density Estimation Using Integrated Nested Laplace Approximations, Xiaofeng Wang

Xiaofeng Wang

Integrated nested Laplace approximations (INLA) are a recently proposed approximate Bayesian approach to fit structured additive regression models with latent Gaussian field. INLA method, as an alternative to Markov chain Monte Carlo techniques, provides accurate approximations to estimate posterior marginals and avoid time-consuming sampling. We show here that two classical nonparametric smoothing problems, nonparametric regression and density estimation, can be achieved using INLA. Simulated examples and \texttt{R} functions are demonstrated to illustrate the use of the methods. Some discussions on potential applications of INLA are made in the paper.

Using Methods From The Data-Mining And Machine-Learning Literature For Disease Classification And Prediction: A Case Study Examining Classification Of Heart Failure Subtypes, Peter C. Austin

Peter Austin

OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine-learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines.

STUDY DESIGN AND SETTING: We compared the performance of these classification methods with that of conventional classification trees to classify patients with heart failure (HF) …

Predictive Accuracy Of Risk Factors And Markers: A Simulation Study Of The Effect Of Novel Markers On Different Performance Measures For Logistic Regression Models, Peter C. Austin

Peter Austin

The change in c-statistic is frequently used to summarize the change in predictive accuracy when a novel risk factor is added to an existing logistic regression model. We explored the relationship between the absolute change in the c-statistic, Brier score, generalized R(2) , and the discrimination slope when a risk factor was added to an existing model in an extensive set of Monte Carlo simulations. The increase in model accuracy due to the inclusion of a novel marker was proportional to both the prevalence of the marker and to the odds ratio relating the marker to the outcome but inversely …