Basic epidemiologic analysis with Stata Biostatistics 212 Session 4 Welcome to class! This is the first session ever of Biostatistics 212 – introduction to statistical computing in clinical research (or ISCCR, as Lee dubbed it).

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

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

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

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

2 x 2 Tables Example Coronary calcium + - + OR = 2.1 (1.6 – 2.7) + - + - 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

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

2 x 2 Tables First, stratify… 106 585 186 2165 89 374 118 801 17 211 CAC + - 106 585 186 2165 + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - 89 374 118 801 17 211 68 1364 (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

2 x 2 Tables …compare strata-specific estimates… (they’re about the same) In men In women CAC CAC + - + - 89 374 118 801 17 211 68 1364 (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

2 x 2 Tables …compare to the crude estimate 106 585 186 2165 89 374 CAC + - 106 585 186 2165 + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - 89 374 118 801 17 211 68 1364 (34%) (14%) + - + - Binge Binge (15%) (7%) RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)

2 x 2 Tables …and then adjust the summary estimate. 89 374 118 801 17 In men In women CAC CAC + - + - 89 374 118 801 17 211 68 1364 + - + - Binge Binge RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62) RRadj = 1.51 (1.21-1.89)

+ - 106 585 186 2165 + - RR = 1.94 (1.55-2.42) Binge In men In women CAC CAC + - + - 89 374 118 801 17 211 68 1364 (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)

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

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

2 x 2 Tables Example – demo using Stata cc cac binge

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)

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)

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

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)

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…

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.

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

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

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