basic epidemiologic analysis with stata biostatistics 212 session 4
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![Page 1: Basic epidemiologic analysis with Stata Biostatistics 212 Session 4](https://reader035.vdocuments.us/reader035/viewer/2022062313/56649d545503460f94a30d36/html5/thumbnails/1.jpg)
Basic epidemiologic analysis with Stata
Biostatistics 212
Session 4
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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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Epi vs. Biostats
• Epidemiologic analysis – Interpreting clinical research data in the context of scientific knowledge
• Biostatistical analysis – Evaluating the role of chance
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Epi vs. Biostats
• Epi –Confounding, interaction, and causal diagrams.– What to adjust for?– What do the adjusted estimates mean?
A B
C
A BC
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2 x 2 Tables
• “Contingency tables” are the traditional analytic tool of the epidemiologist
Outcome
Exposure
+ -
+
-
a b
c d
OR = (a/b) /(c/d) = ad/bc
RR = a/(a+b) / c/(c+d)
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2 x 2 Tables
• Example
Coronary calcium
Binge drinking
+ -
+
-
106 585
186 2165
OR = 2.1 (1.6 – 2.7)
RR = 1.9 (1.6 – 2.4)
292 2750
2351
691
3042
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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
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2 x 2 Tables• First, stratify…
106 585
186 2165
CAC
Binge
+ -
+
-
89 374
118 801
CAC
Binge
+ -
+
-
17 211
68 1364
CAC
Binge
+ -
+
-
In men In 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)
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2 x 2 Tables• …compare strata-specific estimates…
• (they’re about the same)
89 374
118 801
CAC
Binge
+ -
+
-
17 211
68 1364
CAC
Binge
+ -
+
-
In men In women
(34%) (14%)
(15%) (7%)
RR = 1.57 (0.94-2.62)RR = 1.50 (1.16-1.93)
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2 x 2 Tables• …compare to the crude estimate
106 585
186 2165
CAC
Binge
+ -
+
-
89 374
118 801
CAC
Binge
+ -
+
-
17 211
68 1364
CAC
Binge
+ -
+
-
In men In 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)
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2 x 2 Tables• …and then adjust the summary estimate.
89 374
118 801
CAC
Binge
+ -
+
-
17 211
68 1364
CAC
Binge
+ -
+
-
In men In women
RR = 1.50 (1.16-1.93) RR = 1.57 (0.94-2.62)
RRadj = 1.51 (1.21-1.89)
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106 585
186 2165Binge
+ -
+
-
89 374
118 801
CAC
Binge
+ -
+
-
17 211
68 1364
CAC
Binge
+ -
+
-
In men In 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)
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2 x 2 Tables
• Tabulate – output not exactly what we want.
• The “epitab” commands– Stata’s answer to stratified analyses
cs, cc, ircsi, cci, iritabodds, mhodds
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2 x 2 Tables
• Example – demo using Statacs cac binge
cs cac binge, by(male)
cs cac modalc
cs cac modalc, by(racegender)
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2 x 2 Tables
• Example – demo using Statacc cac binge
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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)
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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)
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2 x 2 Tables
• Testing for trend– tabodds
– tabodds cac alccat– tabodds cac alccat, adjust(age male black)
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2 x 2 Tables
• A modern approach – logistic regressionlogistic cac bingelogistic 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 cumbersomexi: logistic cac i.racegender*modalcmhodds cac modalc, by(racegender)
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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…
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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.
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Lab this week
• Epidemiologic analysis of the coronary calcium – death dataset from Lab 1
• Moderately long
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To come…
• Lecture 5 – Tables with Excel, Word
• Lecture 6 – Figures with Stata, Excel
And time to work on your final project.
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See you on Thursday!
• Lab 4 due 11/16
• Bring a floppy disc to all labs!