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.
Este curso faz parte do Programa de cursos integrados Biostatistics in Public Health
oferecido por
Programa de cursos integrados Biostatistics in Public Health Universidade Johns HopkinsInformações sobre o curso
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Curso 3 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
Aprox. 14 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
O que você vai aprender
Practice simple regression methods to determine relationships between an outcome and a predictor
Recognize confounding in statistical analysis
Perform estimate adjustments
Habilidades que você terá
P-ValueProportional Hazards ModelConfoundingregression
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
Aprox. 14 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
Universidade Johns Hopkins
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.
Programa - O que você aprenderá com este curso
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 148 mín.), 2 leituras, 2 testes
8 videos
Introduction3min
Simple Regression: An Overview17min
Simple Linear Regression with a Binary (or Nominal Categorical) Predictor14min
Simple Linear Regression with a Categorical Predictor22min
Simple Linear Regression with a Continuous Predictor25min
Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates25min
Measuring the Strength of a Linear Association22min
Additional Examples17min
2 leituras
Solutions to Linear Regression Practice Quiz10min
Quiz 1 Solution10min
2 exercícios práticos
Practice Quiz: Simple Linear Regression
Simple Linear Regression30min
3 horas para concluir
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.
3 horas para concluir
7 vídeos (Total 114 mín.), 2 leituras, 2 testes
7 videos
Introduction2min
Simple Logistic Regression: An Overview9min
Simple Logistic Regression with a Binary (or Categorical) Predictor22min
Simple Logistic Regression with a Continuous Predictor25min
Simple Logistic Regression: Accounting for Uncertainty in the Estimates15min
Estimating Risk and Functions of Risk from Logistic Regression Results16min
Additional Examples21min
2 leituras
Solutions to Practice Quiz on Simple Logistic Regression10min
Quiz 2 Solutions10min
2 exercícios práticos
Practice Quiz: Simple Logistic Regression
Logistic Regression30min
3 horas para concluir
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.
3 horas para concluir
7 vídeos (Total 101 mín.), 2 leituras, 2 testes
7 videos
Introduction2min
Simple Cox Regression: An Overview17min
Simple Cox Regression with a Binary (or Categorical) Predictor17min
Simple Cox Regression with a Continuous Predictor23min
Simple Cox Regression: Accounting for Uncertainty in the Estimates14min
Estimating Survival Curves from Cox Regression Results11min
Additional Examples14min
2 leituras
Solutions for Practice Quiz on Simple Cox Regression10min
Solutions to Summative Quiz 310min
2 exercícios práticos
Practice Quiz: Simple Cox Proportional Hazards Regression
Simple Cox Regression30min
3 horas para concluir
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.
3 horas para concluir
10 vídeos (Total 117 mín.), 1 leitura, 2 testes
10 videos
Introduction57s
Confounding: A Formal Definition and Some Examples15min
Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding11min
Adjusted Estimates: The General Idea Behind the Computations8min
Additional Examples7min
Effect modification: Introduction with an analytic example17min
Effect modification: Tree damage and elevation example15min
Effect modification: Examples from the literature16min
Confounding versus effect modification: A review7min
Additional examples15min
1 leituras
Solutions to Summative Quiz 410min
2 exercícios práticos
Practice Quiz: Confounding and Effect Modification30min
Confounding and Effect Modification30min
2 horas para concluir
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.
2 horas para concluir
1 leitura
1 leituras
Biostatistical Consulting Project10min
1 exercício prático
Course Project Quiz2h
Avaliações
Principais avaliações do SIMPLE REGRESSION ANALYSIS IN PUBLIC HEALTH
por BV23 de Abr de 2020
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.
por MA13 de Jan de 2020
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.
por SR9 de Dez de 2020
Complex analyses clearly explained, with an emphasis on interpretation rather than on mechanics. Excellent examples from published literature used throughout. Highly recommended!
por ZA4 de Jun de 2020
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
Sobre Programa de cursos integrados Biostatistics in Public Health
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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