part18 - Public Health 6450 Fall 2011 Lynn Eberly and Andy...

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Unformatted text preview: Public Health 6450 Fall 2011 Lynn Eberly and Andy Mugglin Division of Biostatistics School of Public Health University of Minnesota [email protected] Part 18 Review Summarizing Relationships Between Quantitative Variables Where are we going? Previously: I one, two, and paired sample comparisons for binary data I one, two, and paired sample comparisons for quantitative data Current topics: I Overview of Analysis Methods (so far, and a few yet to come) I Describing Relationships Between Quantitative Variables (Ch. 2): Scatterplots, Correlation Next topic: I Simple Linear Regression (Ch. 2) Eberly and Mugglin PubH 6450 Fall 2011 Part 18 2 / 36 Review Summarizing Relationships Between Quantitative Variables Identify the variables of interest In scientific research, we are often interested in the relationship between two variables. I Comparing the effectiveness of two blood pressure lowering drugs: drug type and blood pressure. I Evaluate the effect smoking on lung cancer: smoking status and the presence (or not) of lung cancer. I Education level ( ≤ high school vs. ≥ college) and income. Most often, one of these variables is an outcome of interest (what we have often called a ‘disease’ variable) and the other is a variable we think may explain that outcome (an ‘exposure’ variable). Eberly and Mugglin PubH 6450 Fall 2011 Part 18 3 / 36 Review Summarizing Relationships Between Quantitative Variables Two-sample t-test When the ‘exposure’ variable is binary (drug 1 vs drug 2; ≤ high school vs. ≥ college education) and the ‘disease’ variable is quantitative (blood pressure; yearly income), we can: I Randomize participants to receive drug 1 or 2 and compare their mean blood pressure after some period of follow-up. I Identify the two exposure populations ( ≤ HS vs. ≥ college), draw a random sample from each, and compare their mean incomes. Analysis method (when sample size is large enough) I Estimate: difference of the sample means, with CI. I Hypothesis test: two-sample t-test. Eberly and Mugglin PubH 6450 Fall 2011 Part 18 4 / 36 Review Summarizing Relationships Between Quantitative Variables Two-by-Two (or Larger) Tables: Cohort Study or Clinical Trial When both variables are binary (smoking yes/no and lung cancer yes/no), we can: I Identify the exposure populations (smokers/nonsmokers), draw a random sample from each, and compare the proportions of lung cancer patients. Analysis method (when sample size is large enough) I Estimate: conditional probabilities (given exposure status) in the sample, risk difference, RR, or OR, with CI. I Hypothesis test: z-test of difference in proportions, chi-square test for independence, or chi-square test of homogeneity of proportions....
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This note was uploaded on 11/21/2011 for the course PUBH 6450 taught by Professor Andymugglin during the Fall '10 term at Minnesota.

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part18 - Public Health 6450 Fall 2011 Lynn Eberly and Andy...

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