Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
이 강좌는 Biostatistics in Public Health 전문 분야의 일부입니다.
Simple Regression Analysis in Public Health
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About this Course
최근 조회 9,151회
배울 내용
Practice simple regression methods to determine relationships between an outcome and a predictor
Recognize confounding in statistical analysis
Perform estimate adjustments
귀하가 습득할 기술
P-ValueProportional Hazards ModelConfoundingregression
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 3시간 필요
Simple Regression Methods
Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.
8개 동영상 (총 148분), 2 readings, 2 quizzes
8개의 동영상
Simple Regression: An Overview17m
Simple Linear Regression with a Binary (or Nominal Categorical) Predictor14m
Simple Linear Regression with a Categorical Predictor22m
Simple Linear Regression with a Continuous Predictor25m
Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates25m
Measuring the Strength of a Linear Association22m
Additional Examples17m
2개의 읽기 자료
Solutions to Linear Regression Practice Quiz10m
Quiz 1 Solution10m
2개 연습문제
Practice Quiz: Simple Linear Regression
Simple Linear Regression30m
완료하는 데 2시간 필요
Simple Logistic Regression
Within module two, we will look at logistic regression, create confidence intervals, and estimate p-values. You will have the opportunity to test your knowledge in both a practice quiz and a graded quiz.
6개 동영상 (총 89분), 2 readings, 2 quizzes
6개의 동영상
Simple Logistic Regression: An Overview9m
Simple Logistic Regression with a Binary (or Categorical) Predictor22m
Simple Logistic Regression: Accounting for Uncertainty in the Estimates15m
Estimating Risk and Functions of Risk from Logistic Regression Results16m
Additional Examples21m
2개의 읽기 자료
Solutions to Practice Quiz on Simple Logistic Regression10m
Quiz 2 Solutions10m
2개 연습문제
Practice Quiz: Simple Logistic Regression
Logistic Regression30m
주
2완료하는 데 3시간 필요
Simple Cox Proportional Hazards Regression
Module three focuses on Cox regression with different predictors. You will have the opportunity to test your knowledge first with the practice quiz and, then, with the graded quiz.
7개 동영상 (총 101분), 2 readings, 2 quizzes
7개의 동영상
Simple Cox Regression: An Overview17m
Simple Cox Regression with a Binary (or Categorical) Predictor17m
Simple Cox Regression with a Continuous Predictor23m
Simple Cox Regression: Accounting for Uncertainty in the Estimates14m
Estimating Survival Curves from Cox Regression Results11m
Additional Examples14m
2개의 읽기 자료
Solutions for Practice Quiz on Simple Cox Regression10m
Solutions to Summative Quiz 310m
2개 연습문제
Practice Quiz: Simple Cox Proportional Hazards Regression
Simple Cox Regression30m
주
3완료하는 데 3시간 필요
Confounding, Adjustment, and Effect Modification
Within module four, you will look at confounding and adjustment, and unadjusted and adjusted association estimates. Additionally, you will learn about effect modification. Similar to previous modules, you will first take a practice quiz before completing the graded quiz.
10개 동영상 (총 117분), 1 reading, 2 quizzes
10개의 동영상
Confounding: A Formal Definition and Some Examples15m
Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding11m
Adjusted Estimates: The General Idea Behind the Computations8m
Additional Examples7m
Effect modification: Introduction with an analytic example17m
Effect modification: Tree damage and elevation example15m
Effect modification: Examples from the literature16m
Confounding versus effect modification: A review7m
Additional examples15m
1개의 읽기 자료
Solutions to Summative Quiz 410m
2개 연습문제
Practice Quiz: Confounding and Effect Modification12m
Confounding and Effect Modification30m
주
4완료하는 데 2시간 필요
Course Project
During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about self-administration of injectable contraception and one about medical appointment scheduling in Brazil. The two research teams have asked you to help them interpret previously published results in order to inform the planning of their own studies. If you've already taken other courses in this specialization, then this scenario will be familiar.
1 reading, 1 quiz
1개의 읽기 자료
Biostatistical Consulting Project10m
1개 연습문제
Course Project Quiz2h
4.7
8개의 리뷰Simple Regression Analysis in Public Health 의 최상위 리뷰
Very practical course. The tutor makes complex concepts seem easy.
Good as always, hope to have more high level courses in future
강사
John McGready, PhD, MS
Associate Scientist, BiostatisticsBloomberg School of Public Health
존스홉킨스대학교 정보
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Biostatistics in Public Health 전문 분야 정보
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.
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