basic epidemiologic analysis with stata part ii biostatistics 212 lecture 6
TRANSCRIPT
Basic epidemiologic analysis with Stata
Part II
Biostatistics 212
Lecture 6
Housekeeping
• Questions on Lab 3, Excel
• Extra credit puzzler
• Lab 4 – last Lab before Final Project– Due November 8th
– Email DO file to Scott at [email protected]
• Final project– Due December 6th
Today...
• Adjusting for many things at once
• Logistic regression
• Testing for trends
• Extra time for Lab 4?
Last time
• Binge drinking appears to be associated with coronary calcium– Association partially due to confounding by
gender
• What about race? Age? SES? Smoking?
Multivariable adjustmentmanual stratification
# 2x2 tables
Crude association 1
Adjust for gender 2
Adjust for gender, race 4
Adjust for gender, race, age 68
Adjust for “” + income, education 816
Adjust for “” + “” + smoking 2448
Multivariable adjustmentcs 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 adjustmentcs 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 adjustmentmhodds 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 adjustmentmhodds 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 so thin!
Multivariable adjustmentlogistic command
• Assumes logit model– Await biostats class for details!– Coefficients estimated, no actual stratification– Continuous variables used as they are
Multivariable adjustmentlogistic command
Basic syntax:
logistic outcomevar [predictorvar1 predictorvar2 predictorvar3…]
Multivariable adjustmentlogistic 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 adjustmentlogistic 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 adjustmentlogistic 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 trendstabodds 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
• 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…
Reminder
• Bring your dataset (cleaned) in two weeks!