Statistical Models Commons^™

Nbr2 Errata And Comments, Joseph Hilbe

Joseph M Hilbe

Errata and Comments for Negative Binomial Regression, 2nd edition

International Astrostatistics Association, Joseph Hilbe

Joseph M Hilbe

Overview of the history, purpose, Council and officers of the International Astrostatistics Association (IAA)

諸外国のデータエディティング及び混淆正規分布モデルによる多変量外れ値検出法についての研究(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.

Glme3_Ado_Do_Files, Joseph Hilbe

Joseph M Hilbe

GLME3 ado and do files (116 in total)

Glme3 Data And Adodo Files, Joseph Hilbe

Joseph M Hilbe

A listing of Data Sets and Stata software commands and do files in GLME3 book

Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris

Jeffrey S. Morris

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …

Comparing The Cohort Design And The Nested Case-Control Design In The Presence Of Both Time-Invariant And Time-Dependent Treatment And Competing Risks: Bias And Precision, Peter C. Austin

Peter Austin

Purpose: Observational studies using electronic administrative health care databases are often used to estimate the effects of treatments and exposures. Traditionally, a cohort design has been used to estimate these effects, but increasingly studies are using a nested case-control (NCC) design. The relative statistical efficiency of these two designs has not been examined in detail.

Methods: We used Monte Carlo simulations to compare these two designs in terms of the bias and precision of effect estimates. We examined three different settings: (A): treatment occurred at baseline and there was a single outcome of interest; (B): treatment was time-varying and there …

Using Ensemble-Based Methods For Directly Estimating Causal Effects: An Investigation Of Tree-Based G-Computation, Peter C. Austin

Peter Austin

Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to the production of high-quality evidence is the ability to reduce or minimize the confounding that frequently occurs in observational studies. When using the potential outcome framework to define causal treatment effects, one requires the potential outcome under each possible treatment. However, only the outcome under the actual treatment received is observed, whereas the potential outcomes under the other treatments are considered missing data. Some authors have proposed that parametric regression models be used to estimate potential outcomes. In this study, we examined the use of …

Regression Trees For Predicting Mortality In Patients With Cardiovascular Disease: What Improvement Is Achieved By Using Ensemble-Based Methods?, Peter C. Austin

Peter Austin

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1991-2001 and …

Generating Survival Times To Simulate Cox Proportional Hazards Models With Time-Varying Covariates., Peter C. Austin

Peter Austin

Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data-generating process: one must be able to simulate data from a specified statistical model. We describe data-generating processes for the Cox proportional hazards model with time-varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time-varying covariates: first, a dichotomous time-varying covariate that can …