Validity and Bias in Epidemiology from Coursera

What's inside

Syllabus

Module 1: Introduction to Validity and Bias

Every time you conduct a study, the most important questions to ask are whether your results are an accurate reflection of the truth both within your sample and in the broader population of interest. This is called validity of the study and more or less determines if your study is of any value. In this module we will discuss what validity actually means and we will describe the different types of systematic error, or bias that may undermine the validity of a study. You will learn how to identify and prevent selection bias and information bias and their variations.

MODULE 2: Confounding

Studies often focus on the association between two variables; for instance, between a risk factor and a disease. However, reality is usually complex and there are many other variables that may influence this association. Sometimes, the presence of a third variable can either exaggerate the association between the two variables we study or mask an underlying true association. This is called confounding and is any researcher’s nightmare. In this module, you will learn multiple methods to detect confounding in a study, so that you can prepare to deal with it. By the end of the module, you will be able to apply these methods to actual data and conclude whether there is confounding.

MODULE 3: Dealing with Confounding

This module is dedicated to dealing with confounding. Confounding can be addressed either at the design stage, before data is collected, or at the analysis stage. You will learn the main approaches to dealing with confounding and you will see practical examples on how to do this in your own studies. We will also briefly discuss about the Directed Acyclic Graphs, which is a novel way to detect bias and confounding and control for them.

MODULE 4: Effect Modification

This is the final module of the course. We start by discussing what happens when the effect of an exposure on an outcome differs across levels of another variable. This is called effect modification. We will discuss how to approach effect modification and we will highlight the distinction between confounding and effect modification. We will close the course by revisiting causal inference in epidemiology, discussing how we can go through all potential explanations of an association before deciding whether it is of causal nature.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces advanced concepts in epidemiology, which are useful for students of public health, medicine, statistics, and those interested in health research

Taught by a professor who is recognized for their work in epidemiology

Provides a strong foundation for beginners

Emphasizes identifying and controlling for bias, which is a challenge in epidemiological research

Covers the concept of effect modification, which is key to understanding and interpreting study results

Requires students to have some basic knowledge of statistics and epidemiology

Reviews summary

Validity and bias in epidemiology

Learners say that Validity and Bias in Epidemiology is an impressive course that provides valuable and engaging instruction in the core concepts of epidemiology including confounding, bias, and validity. Students note that the course is well-paced and well-organized, with clear, informative videos, and interesting exercises. They also highly recommend the course for those looking to advance their knowledge of epidemiology.

Exercises are practical and helpful

"It covers all necessary topics, the presentation was understandeble with many examples."

"The course project in week 2 was very helpful"

"this course is an impressive conclusion to the Coursera Specialization where it belongs"

Course instructors are knowledgeable and engaging

"Prof. Filippidis, your lectures are a thing to fall in love with."

"Excellent content and instructors"

"I liked the exercises that we did to cement the knowled"

Course content is comprehensive

"Another great course from ICL!"

"The course simple and clear provides efficient training for some aspects of statistics"

"The course was quite hard for me and challenging, but very useful."

Course is rigorous and may be difficult for beginners

"a little bit hard! but worth!"

"Course work can be challenging at times, but the course is well-organized and well-paced."

"The course was quite hard for me and challenging, but very useful."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Validity and Bias in Epidemiology with these activities:

Review Statistical Methods

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Refreshes statistical methods for learners who may need a stronger foundation, which can strengthen the course content.

Browse courses on Statistical Methods

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Review your notes from a previous statistics course.
Take practice quizzes on statistical methods.
Complete practice problems on statistical methods.

Review core concepts of epidemiology

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Solidify and recall concepts tied to core epidemiological practices.

Browse courses on Epidemiology

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Review previous work and coursework on epidemiology
Practice explaining basic terms of epidemiology
Identify strengths and weaknesses in epidemiologic research

Review Descriptive and Observational Studies

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Refreshes knowledge of types of epidemiological studies, which will help solidify knowledge for later units.

Browse courses on Observational Studies

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Review your notes from the previous epidemiology course on descriptive and observational studies.
Read the first chapter of your textbook on epidemiology.
Take practice quizzes on descriptive and observational studies.

13 other activities

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Develop a presentation on bias in epidemiological research

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Reinforce learning and improve communication skills by teaching bias in epidemiological research

Browse courses on Bias

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Research different types of bias in epidemiological research.
Develop a clear and concise presentation outline.
Create engaging presentation slides.
Practice presenting the material.
Deliver the presentation.

Explore online resources on effect modification

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Extend understanding of the concept of effect modification and its influence on epidemiological studies.

Browse courses on Effect Modification

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Read articles and watch videos on effect modification
Identify examples of effect modification in epidemiological studies
Discuss the implications of effect modification for the interpretation of study results

Design a study to investigate the effects of a new exposure on a health outcome

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Apply principles of study design to design a study that investigates the effects of an exposure on health

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Identify the research question and specific aims of the study.
Select a study design that is appropriate for the research question.
Develop a sampling strategy and sample size calculation.
Design the data collection instruments.
Obtain ethical approval for the study.
Conduct the study and collect data.
Analyze the data and interpret the results.
Write up the study findings in a scientific manuscript.

Practice Identifying Bias

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Gives practice identifying bias, preparing learners to better apply course concepts.

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Review the different types of bias.
Practice identifying bias in research articles.
Complete practice problems on bias.

Practice identifying and controlling for confounding variables

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Enhance your understanding of different methods for dealing with confounding.

Browse courses on Confounding Variables

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Identify confounding variables in hypothetical epidemiological studies
Apply different methods for controlling for confounding
Interpret the results of studies with and without confounding control

Follow Tutorials on Bias in Epidemiological Research

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Complements course content with further examples and exercises, reinforcing understanding.

Browse courses on Bias

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Find tutorials on bias in epidemiological research.
Follow the tutorials and complete the exercises.
Summarize the main points of the tutorials.

Compile materials for later study

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Review resource materials to strengthen foundational understanding of research bias

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Gather notes and materials from the course to date.
Organize and sort the gathered materials.
Review for completeness and any gaps in understanding.

Discuss Confounding with Peers

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Deepens understanding of confounding through guided discussion.

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Meet with a peer who has similar interest in epidemiology.
Review the concept of confounding.
Discuss the different methods to control for confounding.
Apply the methods to a real-world example.

Apply methods to identify and control for confounding factors

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Gain proficiency in applying methods to control for confounding factors

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Identify the potential confounding factors.
Apply the appropriate method to control for the confounding factors.
Evaluate the effectiveness of the confounding adjustment.
Repeat steps 1-3 for multiple confounding factors.
Summarize and interpret the results.

Create a data visualization of confounding factors

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Solidify understanding of the effects of confounding factors on research outcomes

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Organize and identify the relevant confounding factors.
Gather and assess the data related to the confounding factors.
Select a visualization tool and design the data visualization.
Present and defend your findings.

Create a Tutorial on Effect Modification

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Improves understanding of effect modification and demonstrates ability to explain the concept.

Browse courses on Effect Modification

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Review the concept of effect modification.
Identify an example of effect modification from a research article or textbook.
Create a tutorial that explains the concept of effect modification, using the example you identified.

Explore causal inference models in epidemiology

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Deepen understanding of causal inference models and their application in epidemiology

Browse courses on Causal Inference

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Review the basic principles of causal inference.
Learn about different types of causal inference models.
Apply causal inference models to epidemiological data.
Evaluate the validity and assumptions of causal inference models.
Develop recommendations for using causal inference models in epidemiological research.

Review Causality in Epidemiology

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Provides an in-depth understanding of causality in epidemiology, an important concept as it relates to validity.

View Causal Inference on Amazon

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Read the book Causality in Epidemiology by Miguel Hernan and James Robins.
Summarize the main concepts of the book.
Apply the concepts to a real-world example.

Career center

Learners who complete Validity and Bias in Epidemiology will develop knowledge and skills that may be useful to these careers:

Epidemiologist

Epidemiologists investigate patterns and causes of diseases in human populations. By learning the skills of identifying and controlling bias in epidemiology, a person in this role may design studies with more accurate results. Understanding confounder variables can help improve the design of experiments to ensure that they are not influencing the results.

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Data Scientist

Data Scientists collect, analyze, and interpret data to solve business problems. Understanding how to prevent selection, bias, and confounding is critical to ensuring the validity of your models.

See salaries and explore the career path for Data Scientist

Public Health Researcher

Public Health Researchers study the determinants of health and disease in populations. A key component of their work is the ability to design and conduct valid studies. This course will provide you with the foundation you need to understand the principles of validity and bias and how to apply them to your own research.

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Statistician

Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and marketing. This course is designed to provide you with a strong foundation in the principles of validity and bias in epidemiology. This knowledge will be essential for success in this field, as you will often be responsible for designing and conducting studies, and interpreting the results of those studies.

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Biostatistician

Biostatisticians apply statistical methods to the design, conduct, and analysis of studies in the biomedical sciences. This course will equip you with the skills you need to identify and control for bias in your research, which is essential for ensuring the validity of your results.

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Health Policy Analyst

Health Policy Analysts develop and evaluate health policies and programs. They use data to identify and address health problems, and they work to improve the quality and efficiency of healthcare delivery. This course will help you to understand the principles of validity and bias in epidemiology, which are essential for designing and conducting studies to evaluate the effectiveness of health policies and programs.

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Clinical Research Associate

Clinical Research Associates manage the day-to-day operations of clinical trials, including patient recruitment, data collection, and regulatory compliance. This course will provide you with a strong foundation in the principles of validity and bias in epidemiology, which are essential for ensuring the quality and accuracy of clinical trial data.

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Medical Writer

Medical Writers create educational and promotional materials for the healthcare industry. A strong understanding of how to avoid selection, bias, and confounding will allow you to write accurate and reliable materials that are useful to healthcare professionals and patients.

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Health Educator

Health Educators develop and implement health promotion programs. This course will help you to understand the principles of validity and bias in epidemiology, which are essential for designing and evaluating effective health promotion programs.

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Research Scientist

Research Scientists conduct research to advance scientific knowledge. They work in a variety of fields, including medicine, engineering, and social sciences. This course will provide you with a strong foundation in the principles of validity and bias in epidemiology, which are essential for designing and conducting valid research studies.

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Science Writer

Science Writers communicate complex scientific information to a general audience. Understanding the principles of validity and bias is essential for accurately and effectively reporting on scientific research.

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Healthcare Consultant

Healthcare Consultants help healthcare organizations improve their performance. An understanding of bias in clinical research is critical for evaluating the validity of studies that support clinical guidelines and treatment decisions.

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Pharmacovigilance Associate

Pharmacovigilance Associates monitor the safety of drugs and medical devices after they have been approved for use. Understanding the principles of validity and bias is essential for designing and conducting studies to evaluate the safety of drugs and medical devices.

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Risk Manager

Risk Managers identify, assess, and control risks in organizations. The principles of validity and bias are essential for understanding the reliability of data used to assess risks.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. Understanding how to avoid selection, bias, and confounding is critical for actuaries when making decisions about insurance policies and other financial products.

See salaries and explore the career path for Actuary