Basic epidemiologic analysis with Stata Biostatistics 212 Lecture 5

Housekeeping Questions about Lab 4? Lab 3 issues –Categorizing continuous variables (21-30 v 20-29) –Include p-values when appropriate –Don’t forget the missing values! Check your work with a cross tabulation, i.e. tab genderp female, missing Next week we’ll start with the Final Project! –What data will you use? –Explore and clean your data –Start planning tables and figures

Today... What’s the difference between epidemiologic and statistical analysis? Interaction and confounding with 2 x 2’s Stata’s “Epitab” commands Adjusting for many things at once Logistic regression Testing for trends

Epi vs. Biostats Epidemiologic analysis – Analyzing and 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? A B C ABC

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)

2 x 2 Tables Example Coronary calcium Binge drinking OR = 2.1 (1.6 – 2.7) RR = 1.9 (1.6 – 2.4)

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

2 x 2 Tables Men more likely to binge –34% of men, 14% of women Men have more coronary calcium –15% of men, 7% of women

2 x 2 Tables But what does confounding look like in a 2x2 table? And how do you adjust for it?

2 x 2 Tables First, stratify… CAC Binge CAC Binge CAC Binge In menIn women RR = 1.94 ( ) (34%)(14%) (15%)(7%) RR = 1.57 ( )RR = 1.50 ( )

2 x 2 Tables …compare strata-specific estimates… (they’re about the same) CAC Binge CAC Binge In menIn women (34%)(14%) (15%)(7%) RR = 1.57 ( )RR = 1.50 ( )

2 x 2 Tables …compare to the crude estimate CAC Binge CAC Binge CAC Binge In menIn women RR = 1.94 ( ) (34%)(14%) (15%)(7%) RR = 1.57 ( )RR = 1.50 ( )

2 x 2 Tables …and then adjust the summary estimate CAC Binge CAC Binge In menIn women RR = 1.50 ( )RR = 1.57 ( ) RRadj = 1.51 ( )

Binge CAC Binge CAC Binge In menIn women (34%)(14%) (15%)(7%) RR = 1.57 ( )RR = 1.50 ( ) RR = 1.94 ( ) RRadj = 1.51 ( )

2 x 2 Tables How do we do this with Stata? –Tabulate – output not exactly what we want. –The “epitab” commands Stata’s answer to stratified analyses cs, cc csi, cci 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) cc cac binge

2 x 2 Tables Intermediate commands –csi, cci –No dataset required – just 2x2 cell frequencies csi a b c d csi (for cac binge)

Multivariable adjustment Binge drinking appears to be associated with coronary calcium –Association partially due to confounding by gender What about race? Age? SES? Smoking?

Multivariable adjustment manual stratification # 2x2 tables Crude association1 Adjust for gender2 Adjust for gender, race4 Adjust for gender, race, age68 Adjust for “” + income, education816 Adjust for “” + “” + smoking2448

Multivariable adjustment cs command cs command –Does manual stratification for you Lists results from every strata Tests for overall homogeneity Adjusted and crude results –Demo cs cac binge, by(male black age)

Multivariable adjustment cs command cs command –Does manual stratification for you Lists results from every strata Tests for overall homogeneity Adjusted and crude results –Demo cs cac binge, by(male black age) –Can’t interpret interactions!

Multivariable adjustment mhodds command mhodds allows you to look at specific interactions, adjusted for multiple covariates –Does same stratification for you –Adjusted results for each interaction variable –P-value for specific interaction (homogeneity) –Summary adjusted result Demo mhodds cac binge age, by(racegender)

Multivariable adjustment mhodds command mhodds allows you to look at specific interactions, adjusted for multiple covariates –Does same stratification for you –Adjusted results for each interaction variable –P-value for specific interaction (homogeneity) –Summary adjusted result Demo mhodds cac binge age, by(racegender) But strata get thin!

Multivariable adjustment logistic command Assumes logit model –Await biostats class for details! –Coefficients estimated, no actual stratification –Continuous variables used as they are

Multivariable adjustment logistic command Basic syntax: logistic outcomevar [predictorvar1 predictorvar2 predictorvar3…]

Multivariable adjustment logistic command If using any categorical predictors: xi: logistic outcomevar [i.catvar var2…] Creates “dummy variables” on the fly If you forget, Stata won’t know they are categorical, and you’ll get the wrong answer!

Multivariable adjustment logistic command Demo logistic cac binge logistic cac binge male logistic cac binge male black logistic cac binge male black age xi: logistic cac binge male black age i.smoke

Multivariable adjustment logistic command Pro’s –Provides all OR’s in the model –Accepted approach –Can deal with continuous variables –Better estimation for large models? Con’s –Interaction testing more cumbersome, less automatic –More assumptions –Harder to test for trends

Testing for trend Alcohol consumption can be a lot or a little –Does association increase with larger amounts of consumption? –(no j-shaped curve) Test of trend? –Look through epitab suite

Testing for trends tabodds command chi2 test of trend –tabodds cac alccat –Look at output Adjustment for multiple variables possible –tabodds cac alccat, adjust(age male black)

Approaching your analysis Number of potential models/analyses is daunting –Where do you start? How do you finish? My suggestion –Explore –Plan definitive analysis, make dummy tables/figures –Do analysis (do/log files), fill in tables/figures –Show to collaborators, reiterate prn –Write paper

Summary Make sure you understand confounding and interaction with 2x2 tables in Stata 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 and trends is more difficult

In lab today… Lab 5 –Epi analysis of coronary calcium dataset –Walks you through evaluation of confounding and interaction Judgment calls – often no right answer, just focus on reasoning.