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Understanding Epidemiologic Analysis with Stata: Housekeeping, Concepts, and 2x2 Tables

In this lecture, we cover essential housekeeping topics, including the submission of lab assignments and management of Stata windows. Key discussions focus on the distinction between epidemiologic and biostatistical analysis, examining concepts like confounding, interaction, and causal diagrams. We explore 2x2 contingency tables, using real-world examples such as binge drinking and coronary calcium to highlight significant associations and their implications. Students are prompted to consider their final projects, emphasizing data cleaning and analysis planning, and are introduced to Stata's Epitab commands for effective data exploration.

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Understanding Epidemiologic Analysis with Stata: Housekeeping, Concepts, and 2x2 Tables

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  1. Basic epidemiologic analysis with Stata Biostatistics 212 Lecture 5

  2. Housekeeping • Turning in Lab assignments: • “PletcherMark_Lab2.do” • “Window management” in Stata 9 • Questions about Lab 2? • Lab 3: do today, due 10/25/05 • Lab 4 now available

  3. Housekeeping • Time to start thinking about Final Projects! • What data will you use? • Start cleaning, exploring, planning tables and figures

  4. Today... • What’s the difference between epidemiologic and statistical analysis? • Interaction and confounding with 2 x 2’s • Stata’s “Epitab” commands

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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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)

  11. 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)

  12. 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)

  13. 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)

  14. + - + - 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)

  15. 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

  16. 2 x 2 Tables • Example – demo using Stata cs cac binge cs cac binge, by(male) cs cac modalc cs cac modalc, by(racegender)

  17. 2 x 2 Tables • Example – demo using Stata cc cac binge

  18. 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)

  19. Summary • Stare at stratified 2x2 analyses until you get it! • Epitab commands are a great way to explore your data • Emphasis on interaction • Immediate commands (e.g. csi) are very useful – just watch out for the b  c switch!

  20. Next week • Testing for trend • Adjusting for many things at once • Logistic regression • Lab 4 • Epi analysis of coronary calcium dataset • More practice with Do files • Moderately long

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