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? A B C ABC

7. 2 x 2 Tables “Contingency tables” are the traditional analytic tool of the epidemiologist Outcome Exposure + - +-+- ab cd OR = (a/b) /(c/d) = ad/bc RR = a/(a+b) / c/(c+d)

8. 2 x 2 Tables Example Coronary calcium Binge drinking + - +-+- 106585 1862165 OR = 2.1 (1.6 – 2.7) RR = 1.9 (1.6 – 2.4) 2922750 2351 691 3042

9. 2 x 2 Tables There is a statistically significant association, but is it causal? Does male gender confound the association? Binge drinking Coronary calcium Male

10. 2 x 2 Tables First, stratify… 106585 1862165 CAC Binge + - +-+- 89374 118801 CAC Binge + - +-+- 17211 681364 CAC Binge + - +-+- In menIn women RR = 1.94 (1.55-2.42) (34%)(14%) (15%)(7%) RR = 1.57 (0.94-2.62)RR = 1.50 (1.16-1.93)

11. 2 x 2 Tables …compare strata-specific estimates… (they’re about the same) 89374 118801 CAC Binge + - +-+- 17211 681364 CAC Binge + - +-+- In menIn women (34%)(14%) (15%)(7%) RR = 1.57 (0.94-2.62)RR = 1.50 (1.16-1.93)

12. 2 x 2 Tables …compare to the crude estimate 106585 1862165 CAC Binge + - +-+- 89374 118801 CAC Binge + - +-+- 17211 681364 CAC Binge + - +-+- In menIn women RR = 1.94 (1.55-2.42) (34%)(14%) (15%)(7%) RR = 1.57 (0.94-2.62)RR = 1.50 (1.16-1.93)

13. 2 x 2 Tables …and then adjust the summary estimate. 89374 118801 CAC Binge + - +-+- 17211 681364 CAC Binge + - +-+- In menIn women RR = 1.50 (1.16-1.93)RR = 1.57 (0.94-2.62) RRadj = 1.51 (1.21-1.89)

14. 106585 1862165 Binge + - +-+- 89374 118801 CAC Binge + - +-+- 17211 681364 CAC Binge + - +-+- In menIn women (34%)(14%) (15%)(7%) RR = 1.57 (0.94-2.62)RR = 1.50 (1.16-1.93) RR = 1.94 (1.55-2.42) 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