介紹各種型態之存活資料及其一般統計分析與應用。著重於臨床試驗或醫藥試驗之存活資料統計量之估計和檢定，其中包括Kaplan-Meier估計值, log-rank檢定, Cox等比例之危險模式、以及加速失敗模式等之統計分析及其應用之相關實務。Introduces various types of survival data and their general statistical analysis and applications. Focus on the estimation and testing of survival data statistics in clinical trials or medical trials, including statistical analysis and application of Kaplan-Meier estimates, log-rank tests, Cox proportional hazard models, accelerated failure models, etc. Practice.
課程基本描述:
存活分析是在研究樣本所觀察到的某一段存活時間或事件時間之分配，由於研究時間有限、病人失去追蹤或離開試驗等因素，使得病人的存活時間無法完全觀察到，收集到的時間稱為設限時間。針對這種不完整資料，利用母數方法及無母數方法估計病人的存活情形及風險。當資料伴隨其他共變數時，利用半參數方法進行分析，探討共變數對病人存活時間的影響。
課程在系所定位:
存活分析在大學部及碩士班皆開課，其中大學部是以實務走向，碩士班則以理論結合實務取向。
課程所教授的專業能力:
存活資料的解讀和分析、存活時間分配與相關統計量的推導、教導如何用SAS與R撰寫程式分析資料、對分析結果如何判讀和解釋。
授課內容: 1. Examples of Survival Data
2. Basic Quantities and Models
3. Censoring and Truncation
4. Nonparametric Estimation of Basic Quantities for Right-Censored and Left-Truncated Data
5. Topics in Univariate Estimation
6. Hypothesis Testing
7. Semiparametric Proportional Hazards Regression with Fixed Covariates
8. Refinements of the Semiparametric Proportional Hazards Model
9. Regression Diagnostics
10. Inference for Parametric Regression Models
教學目標：介紹各種型態之存活資料及其統計分析與應用。著重於臨床試驗或醫藥試驗之存活資料統計量之估計和檢定，其中包括Kaplan-Meier估計值、 log-rank檢定,、Cox比例風險模式、以及加速失敗模式等之統計分析及其應用之相關實務。
培養之能力:
大學部: 存活資料分析與收集之能力、簡單的計算和推導、結果的呈現和解釋。
碩士班: 存活資料分析與收集之能力、理論的推導和計算、程式的撰寫，結果的呈現和解釋。
Basic course description:
Survival analysis is the distribution of a certain period of survival time or event time observed in the research sample. Due to factors such as limited research time, patients losing follow-up or leaving the trial, the patient's survival time cannot be completely observed. The collected time is called Set a time limit. For this kind of incomplete data, the mother number method and the mother number-free method are used to estimate the patient's survival situation and risk. When the data is accompanied by other covariates, the semi-parametric method is used for analysis to explore the impact of the covariates on patient survival time.
Course positioning in the department:
Survival analysis is taught in both undergraduate and master's programs. The undergraduate program is oriented toward practice, while the master's program is oriented toward combining theory with practice.
Professional competencies taught in the course:
Interpretation and analysis of survival data, derivation of survival time distribution and related statistics, teaching how to use SAS and R to write programs to analyze data, and how to interpret and interpret the analysis results.
Teaching content: 1. Examples of Survival Data
2. Basic Quantities and Models
3. Censoring and Truncation
4. Nonparametric Estimation of Basic Quantities for Right-Censored and Left-Truncated Data
5. Topics in Univariate Estimation
6. Hypothesis Testing
7. Semiparametric Proportional Hazards Regression with Fixed Covariates
8. Refinements of the Semiparametric Proportional Hazards Model
9. Regression Diagnostics
10. Inference for Parametric Regression Models
Teaching objectives: To introduce various types of survival data and their statistical analysis and applications. Focus on the estimation and testing of survival data statistics in clinical trials or medical trials, including statistical analysis of Kaplan-Meier estimates, log-rank tests, Cox proportional hazards models, and accelerated failure models and related practices in their applications .
Ability to develop:
Undergraduate Department: Ability to analyze and collect survival data, simple calculations and derivation, presentation and interpretation of results.
Master's class: Ability to analyze and collect survival data, derivation and calculation of theories, writing of programs, presentation and interpretation of results.
1. Survival analysis: techniques for censored and truncated data, New York: Springer, c2003, by John P. Klein, Melvin L. Moeschberger, 滄海書局代理
2. Applied Survival Analysis: Regression Modeling of Time to Event Data,
Wiley: New York, 2008, by Hosmer DW, Lemeshow S and May, S.
3.Survival Analysis: A Self-Learning Text, 2nd (2005), Springer, by Kleinbaum and Klein
1. Survival analysis: techniques for censored and truncated data, New York: Springer, c2003, by John P. Klein, Melvin L. Moeschberger, agent of Canghai Book Company
2. Applied Survival Analysis: Regression Modeling of Time to Event Data,
Wiley: New York, 2008, by Hosmer DW, Lemeshow S and May, S.
3.Survival Analysis: A Self-Learning Text, 2nd (2005), Springer, by Kleinbaum and Klein
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 midterm exam |
35 | |
| 期末考期末考 final exam |
40 | |
| 平時作業平時作業 Daily homework |
25 |
