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.
제공자:
이 강좌에 대하여
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
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
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
제공자:

존스홉킨스대학교
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.
강의 계획 - 이 강좌에서 배울 내용
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.
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.
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.
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.
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.
검토
SIMPLE REGRESSION ANALYSIS IN PUBLIC HEALTH 의 최상위 리뷰
This course covers all types of Simple Regressions. Instructor explained the complex topics in simple language. Relevant examples from clinical field and thorough explanation by the Instructor.
Really great course. Thank you for creating this. I'm an epidemiologist but haven't practiced biostatistics in years. This course series not only refreshed but also taught me new things.
Complex analyses clearly explained, with an emphasis on interpretation rather than on mechanics. Excellent examples from published literature used throughout. Highly recommended!
Thank you so much for a beautiful explanation and presentation of topics that a lot of physicians tend struggle with, by making it understandable and logical
Biostatistics in Public Health 특화 과정 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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