1. A statistical package for epidemiologists
Stata
A statistical package for epidemiologists
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2. Packages compared
Bruker-terskel
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3. Packages compared
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4. Packages compared
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5. Packages compared
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6. Why Stata Pro Con Aimed at epidemiology Many methods, growing Graphics
Structured, Programable
Comming soon to a course near you
Con
Memory>file size
Used by leading univ, and at many summer schools
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7. Syntax Full syntax Examples
[by varlist:] command [varlist] [if] [in] [, options]
Examples
mean age
mean age if sex==1
by sex, sort: summarize age
summarize age ,detail
Same strukcture for all commands, easy to learn
But many commands
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8. Ways of working Testing Estimation p-values
Estimate with confidence interval
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9. Bivariate
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10. Mean with CI Advanced features Standarization Clustering Bootstrap
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11. Median and percentiles with CI
Cci=concervative confidence interval=binomial excact, may ask for normal
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12. Compare means 22.09.2018 H.S. Compare means by T-test
Girls lighter than boys, approx same variance
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13. Compare means, T-test ttest weight, by(sex) unequal uneq. var.
Show distribution plot first
Lot of information, simple to understand
Test for equal variance (enter)
Paired test (enter)
ttest weight, by(sex) unequal uneq. var.
ttest var1=var2 paired test
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14. Graphics
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15. Plottypes
Plot at start of analysis: scatter, density, box (raw data)
Plot results: bar, dot (derived data)
Pie: poor, parts of a whole
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16. Legend with extra information
Compare distr, not possible with histogram
Extra freq information in plot
Placement of legend
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17. Density with min, max and fractiles
Min, max, median, p25, p75, p5, p95 N=
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18. Scatter with fitline + extra point
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19. Bar with labels inside 22.09.2018 H.S. Long names difficult on x-axis
Better on yaxis (horizontal plot)
Even better inside bars
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20. Regression results 22.09.2018 H.S. Program not automatic, not show
May use log axis
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21. Regression
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22. Purpose of regression Prediction Estimation
Use an estimated model to predict the outcome given covariates in a new dataset
Estimation
Estimate association between outcome and covariates adjusted for the other covariates
Fit of the models matters in the first
Counfounding matter in the last
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23. Linear regression, exposure only
Gest2=0 for gest=243 days
At 243 days the weight is 2978
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24. Add confounders and compare
If prediction: goodnes of fit
If association: confounding
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25. Assumtions and influence
Test of assumptions
Independent errors
Linear effects
Constant error variance
Influence, robustness
Both ready plots and tests for assumtions
Ready plot for influence
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26. Influence
Beta changes from 6 to 16 when removing influential outlier
Back to interaction!
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27. Binary regression Odds ratio, OR Risk ratio, RR Risk difference, RD
binreg y x1 x2, or Link: logit
Risk ratio, RR
binreg y x1 x2, rr Link: log (ln)
Risk difference, RD
binreg y x1 x2, rd Link: identity
Binary outcome.
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28. Odds ratio, Relative risk, Risk Diff
Problems: convergence
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29. Help
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30. Search for help General Examples My home page help command
findit keyword search the net
Examples
help table
findit GAM
My home page
findit key=search key,all
rc=return code
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31. Books A visual guide to Stata graphics by M.N. Mitchell
Data Analysis Using Stata by Ulrich Kohler and Frauke Kreuter
Statistics with Stata (Updated for Version 9) by Lawrence C. Hamilton
Multilevel and longitudinal modeling using Stata by S. Rabe-Hesketh, A. Skrondal
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