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Basic epidemiologic analysis with Stata

Basic epidemiologic analysis with Stata. Biostatistics 212 Session 4. Today. What’s the difference between epidemiologic and statistical analysis? 2 x 2 tables, OR’s and RR’s Interaction and confounding with 2 x 2’s Stata’s “Epitab” commands An introduction to logistic regression.

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Basic epidemiologic analysis with Stata

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  1. Basic epidemiologic analysis with Stata Biostatistics 212 Session 4

  2. Today... • What’s the difference between epidemiologic and statistical analysis? • 2 x 2 tables, OR’s and RR’s • Interaction and confounding with 2 x 2’s • Stata’s “Epitab” commands • An introduction to logistic regression

  3. Epi vs. Biostats • Epidemiologic analysis – Interpreting clinical research data in the context of scientific knowledge • Biostatistical analysis – Evaluating the role of chance

  4. Epi vs. Biostats • Epi –Confounding, interaction, and causal diagrams. • What to adjust for? • What do the adjusted estimates mean? C A B A C B

  5. 2 x 2 Tables • “Contingency tables” are the traditional analytic tool of the epidemiologist Outcome + - + - a b OR = (a/b) /(c/d) = ad/bc RR = a/(a+b) / c/(c+d) Exposure c d

  6. 2 x 2 Tables • Example Coronary calcium + - + - 106 585 691 OR = 2.1 (1.6 – 2.7) RR = 1.9 (1.6 – 2.4) Binge drinking 186 2165 2351 292 2750 3042

  7. 2 x 2 Tables • There is a statistically significant association, but is it causal? • Does male gender confound the association? Male Binge drinking Coronary calcium

  8. 2 x 2 Tables CAC • First, stratify… + - + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

  9. 2 x 2 Tables • …compare strata-specific estimates… • (they’re about the same) In men In women CAC CAC + - + - (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

  10. 2 x 2 Tables CAC • …compare to the crude estimate + - + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

  11. 2 x 2 Tables • …and then adjust the summary estimate. In men In women CAC CAC + - + - + - + - Binge Binge RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62) RRadj = 1.51 (1.21-1.89)

  12. + - + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62) RRadj = 1.51 (1.21-1.89)

  13. 2 x 2 Tables • Tabulate – output not exactly what we want. • The “epitab” commands • Stata’s answer to stratified analyses cs, cc, ir csi, cci, iri tabodds, mhodds

  14. 2 x 2 Tables • Example – demo using Stata cs cac binge cs cac binge, by(male) cs cac modalc cs cac modalc, by(racegender)

  15. 2 x 2 Tables • Example – demo using Stata cc cac binge

  16. 2 x 2 Tables • Epitab subtleties • ir command • Rate ratios, adjusted etc • Related to poisson regression • Intermediate commands – csi, cci, iri • No dataset required – just 2x2 cell frequencies csi a b c d csi 106 186 585 2165 (for cac binge)

  17. 2 x 2 Tables • Adjustment vs. stratification • cs command does both • But can’t adjust for other stuff simultaneously • Binge drinking and CAC, by male, adjusted for age and race? mhodds cac binge age black, by(male)

  18. 2 x 2 Tables • Testing for trend • tabodds • tabodds cac alccat • tabodds cac alccat, adjust(age male black)

  19. 2 x 2 Tables • A modern approach – logistic regression logistic cac binge logistic cac binge male xi: logistic cac modalc i.racegender (xi: allows you to use create “dummy” variables on the fly…) • Provides all OR’s in the model, but interactions more cumbersome xi: logistic cac i.racegender*modalc mhodds cac modalc, by(racegender)

  20. Summary • Epitab commands are a great way to explore your data • Emphasis on interaction • Logistic regression is a more general approach, ubiquitous, but testing for interactions is more difficult…

  21. Summary • Immediate commands (e.g. csi) are very useful – just watch out for the b  c switch! • You’ll get more practice with this is Epi Methods.

  22. Lab this week • Epidemiologic analysis of the coronary calcium – death dataset from Lab 1 • Moderately long

  23. To come… • Lecture 5 – Tables with Excel, Word • Lecture 6 – Figures with Stata, Excel And time to work on your final project.

  24. See you on Thursday! • Lab 4 due 11/16 • Bring a floppy disc to all labs!

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