﻿2021年数学学院“吉大学子全球胜任力提升计划”研究生系列短课程（16）-吉林大学数学学院

# 2021年数学学院“吉大学子全球胜任力提升计划”研究生系列短课程（16）

Abstract: This lecture will be based on my own experience in biomedical research. I will review some commonly used statistical methods in biomedical studies and present related recent research development and topics. Topics include measures of agreement, item selection methods, time to event analysis for transplantation, semiparametric modeling approaches and current challenges and issues for the analysis of big data.

 授课日期Date of Lecture 课程名称（讲座题目）Name (Title) of Lecture 授课时间Duration (Beijing Time) 参与人数Number of Participants July 12, 2021 Measures of agreement 9:00-10:00 60 July 13, 2021 Item selection 9:00-10:00 60 July 15, 2021 Time to event analysis 9:00-10:00 60 July 16, 2021 Semiparametric   regression models for censored data 9:00-10:00 60 July 17, 2021 General semiparametric   regression models 9:00-10:00 60 July 18, 2021 Big data analysis 9:00-10:00 60

Lecture 1: Measure of agreement

In practice, it is important to examine agreement among measures obtained by different sources or methods. Several commonly used statistical methods for agreement will be reviewed, Bland Altman method, coefficient of variation, mean squared deviation, total deviation index, concordance probability, correlation coefficient (Pearson and Spearman), intraclass correlation coefficient.

Lecture 2: Item selection

Item selection arises in studies with questionnaire. I will discuss non-parametric approach for the selection of items in a scale for screening, with the score defined as the sum of item response indicators. Without specifying parametric models for binary classification probabilities, the item selection method evaluates the change in classification accuracy due to adding or deleting one item for a scale with k items.

Lecture 3: Time to event analysis

With the transplant examples, basic concepts for time to event analysis and commonly used statistical methods will be discussed.

Lecture 4: Semiparametric regression models for censored data

Semiparametric regression models for censored data will be reviewed along with challenges and available methods.

Lecture 5: General semiparametric regression models

General semiparametric models will be discussed along with inference procedures.

Lecture 6: Big data analysis

Types of commonly encountered big data, and available statistical approaches will be discussed.