Epidemiology Correlation & Regression

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Blood pressure

This photo shows a nurse taking a woman’s blood pressure with a blood pressure cuff. The nurse is pumping the cuff with her right hand and holding a stethoscope on the patient’s arm with her left hand.
A proficiency in anatomy and physiology is fundamental to any career in the health professions. (credit: Bryan Mason/flickr)

Chapter objectives

After studying this chapter, you will be able to:

  • Distinguish between anatomy and physiology, and identify several branches of each
  • Describe the structure of the body, from simplest to most complex, in terms of the six levels of organization
  • Identify the functional characteristics of human life
  • Identify the four requirements for human survival
  • Define homeostasis and explain its importance to normal human functioning
  • Use appropriate anatomical terminology to identify key body structures, body regions, and directions in the body
  • Compare and contrast at least four medical imagining techniques in terms of their function and use in medicine

Though you may approach a course in anatomy and physiology strictly as a requirement for your field of study, the knowledge you gain in this course will serve you well in many aspects of your life. An understanding of anatomy and physiology is not only fundamental to any career in the health professions, but it can also benefit your own health. Familiarity with the human body can help you make healthful choices and prompt you to take appropriate action when signs of illness arise. Your knowledge in this field will help you understand news about nutrition, medications, medical devices, and procedures and help you understand genetic or infectious diseases. At some point, everyone will have a problem with some aspect of his or her body and your knowledge can help you to be a better parent, spouse, partner, friend, colleague, or caregiver.

This chapter begins with an overview of anatomy and physiology and a preview of the body regions and functions. It then covers the characteristics of life and how the body works to maintain stable conditions. It introduces a set of standard terms for body structures and for planes and positions in the body that will serve as a foundation for more comprehensive information covered later in the text. It ends with examples of medical imaging used to see inside the living body.

Lect 15: Epidemiology & Biostatistics Correlation and Regression

We will teach you how to read and critique medical journal articles using examples from some of the most widely-read medical journals. To critique the medical literature you will need to understand the fundamentals of epidemiologic study design, the sources of bias, and the role of chance. Every discipline has its own jargon. we will cover the terminology used in clinical research, including the basic statistical jargon. The most important concepts are in the lectures and small groups provide you with an opportunity to apply what you have learned from the lecture material to actual medical journal articles.

As future physicians you have an obligation to remain current in your field of practice and to treat patients according to generally accepted standards of care.

Quiz PDF eBook: 
Epidemiology Correlation & Regression
Download Epidemiology Lecture #15 Quiz PDF eBook
7 Pages
2014
English US
Educational Materials



Sample Questions from the Epidemiology Correlation & Regression Quiz

Question: For the multivariate linear regression of the outcome of weight gain (lbs) on the exposure of cupcake consumption (continuous), we find a beta, or regression coefficient of 0.76, with a 95% confidence interval of (0.46, 1.06). Which one of the following is a true statement?

Choices:

For each 1?-unit increase in average cupcake consumption, we expect, on average, an increase in weight of between 0.46 and 1.06 lbs. The best estimate is 0.76 lbs.

For each 1?-unit increase in average cupcake consumption, we expect an odds ratio of weight gain of between 0.46 and 1.06. The best estimate is 0.76.

For each 1?-unit increase in weight gain, we expect, on average, an increase in cupcake consumption of between 0.46 and 1.06. The best estimate is 0.76 cupcakes.

For each 1?-unit increase in weight gain, we expect an odds ratio of cupcake consumption of between 0.46 and 1.06. The best estimate is 0.76 cupcakes.

The p?-value is above 0.05.

Question: What is the distinction between Pearson correlation and Spearman correlation?

Choices:

Pearson correlation is used for continuous variables, whereas Spearman correlation is for dichotomous or binary variables.

Pearson correlation can range between ?-1 and 1, whereas Spearman correlation can range between 0 and positive infinity.

Pearson correlation measures the linear association between two variables, whereas Spearman correlation measures the quadratic association between two variables.

Pearson correlation is used for normally distributed variables, whereas Spearman correlation is based on the ranks of two variables.

Pearson correlation is used for discrete variables, whereas Spearman correlation is used for binary variables.

Question: Which is the best interpretation of a Pearson correlation coefficient of 0.7?

Choices:

For each 1?-unit increase in the exposure variable, we expect a 0.7?-unit increase in the outcome variable.

For each 1?-unit increase in the outcome variable, we expect a 0.7?-unit increase in the exposure variable.

For each 1?-unit increase in the exposure variable, we expect a multiplicative increase of 0.7 in the outcome variable.

For each 1?-unit increase in the outcome variable, we expect the exposure variable rank to increase by a factor of 0.7.

There is a moderately strong linear association between the exposure and outcome variables.

Question: If a Pearson correlation for two variables is positive and statistically significant (i.e. significantly different than zero), what do we know about the linear regression coefficient for the simple linear regression of those same variables?

Choices:

It will be negative and non?-significant.

It will be negative and statistically significant.

It will be positive and non?-significant.

It will be positive and statistically significant.

We know it will be positive, but we cannot determine if it will be statistically significant.

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Source:  Janet, E.A. Forrester, Kwan Ho Kenneth Chui, Steven Cohen, Michael D. Kneeland, Alice Tang, David Tybor. Epidemiology and Biostatistics 2010. (Tufts University OpenCourseWare), http://ocw.tufts.edu/Course/65/ (Accessed 3 May, 2014). License: Creative Commons BY-NC-SA
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