1. 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).

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 + - + 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

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 First, stratify… 106 585 186 2165 89 374 118 801 17 211
CAC
106
585
186
2165 + -
RR = 1.94 ( )
Binge
In men
In women
CAC
CAC 89
374
118
801 17
211 68
1364
(34%)
(14%) + - + -
Binge
Binge
(15%)
(7%)
RR = 1.50 ( )
RR = 1.57 ( )

9. 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 ( )
RR = 1.57 ( )

10. 2 x 2 Tables …compare to the crude estimate 106 585 186 2165 89 374
CAC
106
585
186
2165 + -
RR = 1.94 ( )
Binge
In men
In women
CAC
CAC 89
374
118
801 17
211 68
1364
(34%)
(14%) + - + -
Binge
Binge
(15%)
(7%)
RR = 1.50 ( )
RR = 1.57 ( )

11. 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 ( )
RR = 1.57 ( )
RRadj = 1.51 ( )

12.
106
585
186
2165 + -
RR = 1.94 ( )
Binge
In men
In women
CAC
CAC 89
374
118
801 17
211 68
1364
(34%)
(14%) + - + -
Binge
Binge
(15%)
(7%)
RR = 1.50 ( )
RR = 1.57 ( )
RRadj = 1.51 ( )

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 (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!