1. Stata 9, Summing up

2. Why Stata Pro Con Aimed at epidemiology Many methods, growing Graphics
Structured, Programable
Comming soon to a course near you
Con
Memory>file size
Copy table
Used by leading univ, and at many summer schools
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3. Use Import data Do files Full syntax DBMS-Copy
Highlight commands, Ctrl-D
Full syntax
[by varlist:] command [varlist] [if exp] [in range] [, opts]
list if age<50
list in 1/10
regress y x1 x2 if deltabeta<0.3
Show open do files, copy from menu
List if x<100, list in 1/10 may use both
Regress … if db<0.3
Options often advanced statistics
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4. Data check describe describe dataset summarize means ++
list x1 x2 in 1/10 list first 10 obs
gen id=_n generate id
numlabel x1 x2, add add value to label
tab x1 x2, mis x1 by x2 including missing
list id x1 if (x2==.)+(x3==.)==1 list if x1 or x2 is missing
egen miss=rowmiss(x1 x2 x3) number missing
tab miss from 0-3 missing
drop x1 if x1<0 drop negative
drop x1 if x1>100 & x1<. drop large
Numlabel: 3 cat, add coding to label
List if one of two variables is missing
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5. Graphics Explore data Plot means Plot means using aggregate and twoway
kdensity y distribution
scatter y x scatter
twoway (scatter y x)(lfit y x) scatter+line
Plot means
graph bar (mean) y1 y2 mean of y1 and y2
graph bar (mean) y, over(c) mean y for values of c
Plot means using aggregate and twoway
preserve
collapse (mean) ym=y, by(c) one line pr c value
line ym c lineplot mean(y) by c
restore
(mean) may use median, count, p25, …
Advanced:
Save results in macros, use in plot
(same as bar over(c) ) Aggregate data: collapse:means, contract:freq
May add sd and count to collapse to get CI of mean, se(mean)=sd/sqrt(count)
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6. Help General Examples help command search keyword findit keyword
help table
search GAM
findit GAM
findit key=search key,all
rc=return code
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7. Continuous symetrical data
Univariate
kdensity y distribution
summarize y means ++
Bivariate
sdtest y, by(sex) equal variance?
ttest y, by(sex) equal means?
oneway y parity3, tabulate equal means?
Multivariable
regress y x1 x2 linear regression
dfbeta
Bivar: 2 groups, 3+ groups
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8. Some options mean y mean+ci mean y, cluster(region)
mean y, standardize
mean y, bootstrap
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9. Continuous skewed data
Univariate
kdensity y distribution
summarize y, detail medians ++
Bivariate
table sex, c(median y) medians
ranksum y, by(sex) equal medians?
kwallis y, by(age3) equal medians?
Multivariable
regress y x1 x2 linear regression
dfbeta
2 groups. Medians+ci, cci (conservative ci, (broader)) not assuming normality
Mann-Whitney U test=Wilcoxon rank sum
3+ groups Kruskal-Wallis
Linear regression, but look at influence
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10. Categorical data Univariate Bivariate Multivariable
tabulate y freq table
proportion y prop with ci
Bivariate
tabulate y sex, col chi2 column %, chisquare
Multivariable
logistic y x1 x2 logistic regression
binreg y x1 x2, rd risk difference
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11. Survival data Set Univariate Bivariate Multivariable
stset time, failure(status==1)
Univariate
sts graph, fail gwood KM failure+ci
Bivariate
sts graph, fail by(x1) KM failure
sts test x1 log rank test
Multivariable
stcox x1 x2 cox regression
Analyse time to event. Not fully obsereved
Two variables: time and failure
st=survival time
Gwood=Greenwood ci
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12. Model building Estimate Compare Interaction
regress y exp exposure only
est store m1 store
regress y exp x1 x2 exposure +conf.
est store m2 store
Compare
est table m1 m2 confounding?
est stat m1 m2 model fit
Interaction
regress y exp x1 x1exp with interaction term
lincom exp+2*x1exp effect of exp for x1=2
lincom=linear combination with ci
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13. Model testing Assumptions Influence Independent errors discuss
Linear effects categorize, plot coefs
Constant error plot resid (linear mod)
Influence
Influential points plot delta-beta
Linear effect on g(y)
Prop hazards, plot schoenfeld resid
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14. Regression with simple error structure GLM
regress linear regression (also heteroschedastic errors)
nl non linear least squares
GLM
logistic logistic regression
poisson Poisson regression
binreg binary outcome, OR, RR, or RD effect measures
Conditional logistc
clogit for matched case-control data
Multiple outcome
mlogit multinomial logit (not ordered)
ologit ordered logit
Regression with complex error structure
xtmixed linear mixed models
xtlogit random effect logistic
Some of Stata’s regression commands
Glm for other link-distr combinations
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15. GLLAMM Generalized Linear Latent And Mixed Models Response types
continuous
ordered and unordered categories
counts
survival
Model types
Generalized Linear Models (GLM)
Structural Equation Models (SEM)
Mixed Models
Measurement Error models
SEM: intermediate variables
Mixed: hiararcical data, repeated mearsurements
Have special data, find appropriate tools in Stata or in added programs
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