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12-Confounding

12-Confounding - Principles of Epidemiology for Public...

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4/5/2011 Confounding 1 Confounding Principles of Epidemiology for Public Health (EPID600) Victor J. Schoenbach, PhD www.unc.edu/~vschoenb/ Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill www.unc.edu/epid600/
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“Brain Cramps” "interesting" comments by well-known people (Received from Natasha Jamison, EPID160 graduate)
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That’s what can be further from the truth! “I was provided with additional input that was radically different from the truth. I assisted in furthering that version.” – Colonel Oliver North, from his Iran-Contra testimony.
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Equal opportunity employer “We don't necessarily discriminate. We simply exclude certain types of people.” – Colonel Gerald Wellman, ROTC Instructor.
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Quite a high risk, I’d say “If we don't succeed, we run the risk of failure.” – President Bill Clinton
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Why I’m glad no one is taping me “We are ready for an unforeseen event that may or may not occur.” – Vice President Al Gore
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Confounding 7 We are here [Now leaving] Sources of error Confounding [Now entering] Data analysis and interpretation Causal inference
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Confounding 8 Setting the scene “The data speak for themselves.” versus “Our data say nothing at all.” (Epidemiology guru Sander Greenland, Congress of Epidemiology 2001, Toronto)
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Confounding 9 Setting the scene Logically sound inferences involve (1) data + (2) assumptions No assumptions no inference So always need a conceptual model Sander Greenland, Congress of Epidemiology 2001, Toronto
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Confounding 10 Causal inference in everyday living Does exercise make me feel better? Try getting exercise – how do I feel? Try not getting exercise – how do I feel? Try getting exercise again – do I feel better?
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Confounding 11 Causal inference in everyday living Does getting too little sleep make me irritable? Try sleeping too little – ask my partner Try sleeping enough – ask my partner Try sleeping too little – ask my partner
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Confounding 12 Desirable attributes of crossover experiments Exposure is under investigator’s control Comparison condition is a true control Can go back and forth, providing some control for secular changes
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Confounding 13 Constraints on cross-over experiments Exposures may be harmful or not under our control Effects may not be quickly reversible Experimental subjects or the environment may have changed
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Confounding 14 Key attribute of crossover experiments Can compare what happens to people who are exposed to what happens to the same people when they are not exposed – almost at the same time
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Confounding 15 People     
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