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Survival Analysis
Study Course Description
Course Description Statuss:Approved
Course Description Version:4.00
Study Course Accepted:14.03.2024 11:39:41
| Study Course Information | |||||||||
| Course Code: | SL_118 | LQF level: | Level 7 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Life Science | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Andrejs Ivanovs | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | 23 Kapselu street, 2nd floor, Riga, statistika rsu[pnkts]lv, +371 67060897 | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 2 | Total Contact Hours of Lectures | 12 | ||||
| Classes (count) | 6 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 12 | ||||
| Total Contact Hours | 24 | ||||||||
| Part-Time - Semester No.1 | |||||||||
| Lectures (count) | 6 | Lecture Length (academic hours) | 1 | Total Contact Hours of Lectures | 6 | ||||
| Classes (count) | 6 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 12 | ||||
| Total Contact Hours | 18 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | • Familiarity with probability theory and mathematical statistics. • Basic knowledge in R software. • Basic knowledge of linear models and statistical estimation techniques (maximum likelihood). | ||||||||
| Objective: | The objective of this course is to give students the advanced knowledge of the methodology of the analysis of time to event data that occurs very frequently in the biomedical research (clinical trials, cohort studies). The aim is to provide students with the tools and most common methods used for such data, as well as a brief overview of more advanced and modern topics. The course will have a strong applied focus, although some details of the mathematical background and justification of the methodology will be provided as well. The software package R will be used for computer practical classes, where several real datasets will be analysed, so that the students would become confident in using the methodology for practical data analysis tasks. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to time to event data: censoring, time scales, survival and hazard functions. Common parametric distributions of survival time. | Lectures | 1.00 | auditorium | |||||
| 2 | Exploring time to event data and parametric survival distributions in R. | Classes | 1.00 | computer room | |||||
| 3 | Kaplan-Meier estimator of the survival function. | Lectures | 1.00 | auditorium | |||||
| 4 | Kaplan-Meier estimator and graphical displays of survival function in R. | Classes | 1.00 | computer room | |||||
| 5 | Models for survival data: proportional hazards and accelerated failure time models. Parametric modelling. | Lectures | 1.00 | auditorium | |||||
| 6 | Parametric survival models in R. | Classes | 1.00 | computer room | |||||
| 7 | The Cox proportional hazards model: fitting the model using the partial likelihood method. | Lectures | 1.00 | auditorium | |||||
| 8 | Fitting Cox models in R. | Classes | 1.00 | computer room | |||||
| 9 | The Cox proportional hazards model: diagnostics, residuals, predictions. Time-dependent covariates. | Lectures | 1.00 | auditorium | |||||
| 10 | Diagnostics and predictions for a Cox model in R. | Classes | 1.00 | computer room | |||||
| 11 | Overview of some extensions of the Cox model: models for competing risks, models for recurrent events, frailty models, joint modelling of longitudinal and time to event data. | Lectures | 1.00 | auditorium | |||||
| 12 | Estimation of survival and cumulative incidence functions in the presence of competing risks in R. Models with time-dependent covariates. | Classes | 1.00 | computer room | |||||
| Topic Layout (Part-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to time to event data: censoring, time scales, survival and hazard functions. Common parametric distributions of survival time. | Lectures | 1.00 | auditorium | |||||
| 2 | Exploring time to event data and parametric survival distributions in R. | Classes | 1.00 | computer room | |||||
| 3 | Kaplan-Meier estimator of the survival function. | Lectures | 1.00 | auditorium | |||||
| 4 | Kaplan-Meier estimator and graphical displays of survival function in R. | Classes | 1.00 | computer room | |||||
| 5 | Models for survival data: proportional hazards and accelerated failure time models. Parametric modelling. | Lectures | 1.00 | auditorium | |||||
| 6 | Parametric survival models in R. | Classes | 1.00 | computer room | |||||
| 7 | The Cox proportional hazards model: fitting the model using the partial likelihood method. | Lectures | 1.00 | auditorium | |||||
| 8 | Fitting Cox models in R. | Classes | 1.00 | computer room | |||||
| 9 | The Cox proportional hazards model: diagnostics, residuals, predictions. Time-dependent covariates. | Lectures | 1.00 | auditorium | |||||
| 10 | Diagnostics and predictions for a Cox model in R. | Classes | 1.00 | computer room | |||||
| 11 | Overview of some extensions of the Cox model: models for competing risks, models for recurrent events, frailty models, joint modelling of longitudinal and time to event data. | Lectures | 1.00 | auditorium | |||||
| 12 | Estimation of survival and cumulative incidence functions in the presence of competing risks in R. Models with time-dependent covariates. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | 1. Individual work with the course material in preparation to lectures according to plan. 2. Independent data analysis project to practice the tools learned in the practical classes. | ||||||||
| Assessment Criteria: | Assessment on the 10-point scale according to the RSU Educational Order: • Independent data analysis project (50%) and presentation of the project’s results (50%). | ||||||||
| Final Examination (Full-Time): | Exam (Oral) | ||||||||
| Final Examination (Part-Time): | Exam (Written) | ||||||||
| Learning Outcomes | |||||||||
| Knowledge: | On successful course completion students will recognize with the range of statistical analysis methodology available for time to event data. Students will have gained extensive knowledge on the classical methods, such as the Kaplan-Meier estimator and the Cox Proportional Hazards Model survival data, but they will also be aware of and understand more advanced topics: knowing in which situations they would need non-standard methods and what are the resources available to conduct the analysis. | ||||||||
| Skills: | • The students will be able to independently handle most common forms of survival data, doing the necessary conversions between date formats and using graphical visualization tools of the survival distributions. • Ability to fit Cox proportional hazards models, being aware of underlying assumptions and using appropriate tools for model diagnostics. • The students will also have skills to communicate the results and present them in a format that is appropriate for scientific presentations and publications. | ||||||||
| Competencies: | • After successful acquisition of the course, the student will be competent to select and critically read the scientific publications, which uses the methodology for survival analysis, as well as establish conclusions, gather scientific evidence. • The students will be able to plan data analysis for a follow-up study, using the methodology of survival analysis. • The students will propose a range of potential extensions of the standard methodology (competing risks, frailty models) and are able to work with available literature resources to develop a plan that satisfies their analysis needs. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Collett D. Modelling Survival Data in Medical Research (3rd Edition). Chapman and Hall/CRC, 2014. | ||||||||
| Additional Reading | |||||||||
| 1 | Andersen, P. K. and Keiding, N. Survival and event history analysis. Wiley, 2006. | ||||||||
