Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
About me
Posts
contact
people
research
software
COVID
This R package implements the two robust methods described in Lin et al. (2023+) to assess the totality of evidence for the treatment effects on the entire clinical course of a patient using daily clinical outcomes. In addition, we develop a SAS macro that fits marginal proportional hazards models for multiple events and combines the evidence of treatment effects using the Wei, Lin and Weissfeld (1989, JASA) method.
DOVE
This R package implements maximum likelihood methods described in Lin et al. (2021a; 2021b) for evaluating the durability of vaccine efficacy in a randomized, placebo-controlled clinical trial with staggered enrollment of participants and potential crossover of placebo recipients before the end of the trial. It has been released on CRAN.
DOVE3
This R package implements the methods described in Lin et al. (2022) for estimating the time-varying effectiveness of vaccination and prior infection against COVID-19 related outcomes. It is an extension of the previous DOVE package by estimating the effects of multiple vaccine products as well as prior infection simultaneously under a single multiplicative intensity model.
iDOVE
This R package implements a nonparametric maximum likelihood method described in Lin et al. (2021) for assessing potentially time-varying vaccine efficacy against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. It has been released on CRAN.
talks
teaching
Empirical Processes
, , 1900
Lecture 01. Introduction
Lecture 02. Preliminaries for Empirical Processes
Lecture 03. Stochastic Convergence
Lecture 04. Stochastic Convergence (Cont.)
Lecture 05. Maximal Inequalities
Lecture 06. Symmetrization and Measurability
Lecture 07. Glivenko-Cantelli and Donsker Results
Lecture 08. Vapnik-Cervonenkis Classes and Uniform Entropy
Lecture 09. BUEI Classes & Bracketing Entropy
Lecture 10. Preservation Results
Lecture 11. Bootstrapping Empirical Processes
Lecture 12. Bootstrapping Empirical Processes (Cont.)
Lecture 13. Functional Delta Method
Lecture 14. Z-Estimators
Lecture 15. M-Estimators
Lecture 16. Non-Regular Examples of M-Estimators
Lecture 17. Case Studies
STAT3655 Survival Analysis
, , 1900
Chapter 1. Overview
Chapter 2. Inference in Parametric Models
Chapter 3. Nonparametric Estimation and Testing
Chapter 4. Semiparametric Regression Models
Chapter 5. Analysis of Multivariate Failure Time Data