이 강좌에 대하여

최근 조회 47,038
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

완료하는 데 약 11시간 필요
영어
자막: 영어

배울 내용

  • Run Kaplan-Meier plots and Cox regression in R and interpret the output

  • Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis

  • Describe and compare some common ways to choose a multiple regression model

귀하가 습득할 기술

Understand common ways to choose what predictors go into a regression modelRun and interpret Kaplan-Meier curves in RConstruct a Cox regression model in R
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계

We advise that you first take the previous courses in the series, particularly Introduction to Statistics, though this is not essential.

완료하는 데 약 11시간 필요
영어
자막: 영어

제공자:

임페리얼 칼리지 런던 로고

임페리얼 칼리지 런던

석사 학위 취득 시작

This 강좌 is part of the 100% online Global Master of Public Health from 임페리얼 칼리지 런던. If you are admitted to the full program, your courses count towards your degree learning.

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 4시간 필요

The Kaplan-Meier Plot

완료하는 데 4시간 필요
4개 동영상 (총 16분), 11 개의 읽기 자료, 3 개의 테스트
4개의 동영상
What is Survival Analysis?4m
The KM plot and Log-rank test4m
What is Heart Failure and How to run a KM plot in R4m
11개의 읽기 자료
About Imperial College & the team10m
How to be successful in this course10m
Grading policy10m
Data set and glossary10m
Additional Readings10m
Life tables20m
Feedback: Life Tables10m
The Course Data Set20m
Feedback: Running a KM plot and log-rank test3m
Practice in R: Run another KM Plot and log-rank test10m
Feedback: Running another KM plot and log-rank test10m
3개 연습문제
Survival Analysis Variables30m
Life tables30m
Practice in R: Running a KM plot and log-rank test20m
2

2

완료하는 데 2시간 필요

The Cox Model

완료하는 데 2시간 필요
3개 동영상 (총 18분), 4 개의 읽기 자료, 2 개의 테스트
3개의 동영상
How to run Simple Cox model in R7m
Introduction to Missing Data5m
4개의 읽기 자료
Hazard Function and Risk Set20m
Practice in R: Simple Cox Model30m
Feedback: Simple Cox Model10m
Further Reading20m
2개 연습문제
Hazard function and Ratio5m
Simple Cox Model15m
3

3

완료하는 데 2시간 필요

The Multiple Cox Model

완료하는 데 2시간 필요
1개 동영상 (총 6분), 7 개의 읽기 자료, 1 개의 테스트
7개의 읽기 자료
Introduction to Running Descriptives10m
Practice in R: Getting to know your data30m
Feedback: Getting to know your data10m
How to run multiple Cox model in R20m
Introduction to Non-convergence10m
Practice: Fixing the problem of non-convergence10m
Feedback on fixing a non-converging model15m
1개 연습문제
Multiple Cox Model10m
4

4

완료하는 데 3시간 필요

The Proportionality Assumption

완료하는 데 3시간 필요
3개 동영상 (총 11분), 7 개의 읽기 자료, 3 개의 테스트
3개의 동영상
Cox proportional hazards assumption4m
Summary of Course2m
7개의 읽기 자료
Checking the proportionality assumption10m
Feedback on Practice Quiz10m
What to do if the proportionality assumption is not met20m
How to choose predictors for a regression model20m
Practice in R: Running a Multiple Cox Model
Results of the exercise on model selection and backwards elimination10m
Final Code10m
3개 연습문제
Assessing the proportionality assumption in practice5m
Testing the proportionality assumption with another variable15m
End-of-Module Assessment20m

검토

SURVIVAL ANALYSIS IN R FOR PUBLIC HEALTH의 최상위 리뷰

모든 리뷰 보기

공중 보건학을 위한 R을 통한 통계 분석 특화 과정 정보

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....
공중 보건학을 위한 R을 통한 통계 분석

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