Stata Introduction, 3 h

Presented by Cecilie Dahl

Presentation, data and programs at:

https://www.med.uio.no/helsam/forskning/aktuelt/arrange

menter/andre/stata-course-uio.html

Stata introduction

•General use• Interface and menu•Do-files and syntax•Data handling

•Analysis•Descriptive•Graphs•Bivariate

Exercises

Why Stata

•Pro•Price•Aimed at epidemiology (and economy)•Many methods, growing•Graphics•Structured, Programmable

•Con•File size < Memory

Smart working

•Data (.dta)•Master file, safe•Working file for each project

•Syntax (.do)•Work in progress file•Manuscript file (Table 1…, Figure 1…, Supplement)

•Output (.smcl or .log)•Save or discard

INTERFACE

Interface Stata 12 (and 16)

Do

file

Data

edit

Menu

H.S. 7

Do-file example

8

New do-file: icon or Ctrl-9

Run: Mark, Ctrl-D

y

Syntax

•Examples•mean age•mean age if sex==1•bysort sex: summarize age•summarize age ,detail

9

command [varlist] [if exp] [in range] [, opts][bysort varlist:]

Syntax

DATA HANDLING

Export data from SPSS

•Using SPSS 14.0-• Save as, Stata Version 8 SE

11

Use and save data

•Open data•use “C:\Course\Myfile.dta”, clear

•Describe•describe describe all variables• list sex age in 1/20 list obs nr 1 to 20

•Save data• save “C:\Course\Myfile.dta” ,replace

12

Exercise 1•Start Stata

•Open the birth data (…birth1.sav)

•Open a new syntax file (Ctrl-9)

•Describe all variables: describe.

• List the 10 first observations of weight, sex and mother’s age (mage)

•Save the syntax file for later use

5-10 min

https://www.med.uio.no/helsam/forskning/aktuelt/arrangementer/andre/stata-course-uio.html

Descriptive• Continuous

• Categorical

summarize weight

summarize weight, details percentiles ++

tabulate bullied

tabulate bullied, nolab show coding

Other descriptives

15

tabstat mAge, stat( N min p50 mean max) by(parity)

Generate, replace

• Index (young men)

• generate index=0• replace index=1 if sex==1 & age<30

•Young/Old• generate old=(age>50) if age<.

•Serial numbers• generate id=_n

16

Recode

•Recode 1/2 into 0/1• recode sex (1=0) (2=1), gen(sex0)

•Alternative• generate sex0=sex-1

Dates

•From numeric to date (3 numeric variables into date variable)

ex: m=12, d=2, y=1987generate birth=mdy(m,d,y)format birth %td

•From string to date (1 string variable into date variable)

ex: bstr=“02.12.1987”generate birth=date(bstr,”DMY”)format birth %td

18

Exercise 2•Summarize mother’s age

•Tabulate sex

•Recode sex into sex0 with categories 0, 1

•Generate new gestational age in weeks (the old is in days)•Summarize the new variable

10 min

Missing

•Obs!!!• Represented as ”.”• Missing values are large numbers • age>30 will include missing.• age>30 if age<. will not.

•Test• replace age=0 if (age==.)

•Change• replace educ=. if educ==99

20

Describe missing• Summarize missing

• Missing in tables

21

tab bullied sex, missing

misstable summarize weight sex gest missing

Exercise 3

•Tabulate missing in gestational age (gest) with the misstable command

•Tabulate gest4 versus sex and include missing

•Summarize mage if gest is greater than 260 days•Will this include missing in gest?•Summarize mage if gest is greater than 260 days

excluding missing in gest

10 min

Help•General• help command• findit keyword search Stata+net

•Examples• help table• findit aflogit

23

Many videos on YouTube

Summing up

• Use do files• Run: Mark, Ctrl-D

• Syntax• command [varlist] [if exp] [in range] [, options]

•Missing• age>30 if age<.• generate old=(age>50) if age<.

•Help•help describe

Oct-19 24

GRAPHICS

Twoway plots

•Syntax• twoway (plot1, opts) (plot2, opts), opts

•One plot•kdensity bw

• scatter bw gest

Oct-19 26

0 2000 4000 6000Birth weight

kernel = epanechnikov, bandwidth = 102.3251

Kernel density estimate

0

200

04

00

06

00

0

Birth

we

igh

t

240 260 280 300 320 340Gestational age

Oct-19 27

twoway (scatter bw gest) (fpfitci bw gest) (lfit bw gest)

200

03

00

04

00

05

00

06

00

0g

ram

250 270 290 310days

Weight by gestational age

scatter smooth with CI line fit

Titles

Oct-19 28

1000

2000

3000

4000

5000

ytitle

240 260 280 300 320xtitle

note

subtitle

title

scatter bw gest, title("title") subtitle("subtitle") ///

xtitle("xtitle") ytitle("ytitle") note("note")

Exercise 4

•Make a density plot of birth weight (weight)

•Make a scatter plot of birth weight versus gestational age (gest)• Replace the outlier in gestational age (gest) with missing

• Restrict the plot to gestational age greater than 250 days (hint if gest>250)• Add a linear fit line to the scatter plot to see the trend• Add a smoothing curve with confidence interval to the plot

(fpfitci) to look for non-linear trend. The order of plots matters• Add a title, ytitle and xtitle to the plot

10 min

BIVARIATE ANALYSIS

Two independent samples

31

2000 3000 4000 5000 6000Birth weight

twoway ( kdensity weight if sex==1, lcolor(blue) ) ///

( kdensity weight if sex==2, lcolor(red) )

Equal means?

Equal variance?

Do boys and girls have the same mean birth weight?

Test of equal variance:robvar weight, by(sex)

Two independent samples test

32

ttest weight, by(sex) unequal

ttest w1 w2, paired

ttest weight, by(sex) 2-sample T-test

Crosstables

33

equal proportions?

Are boys bullied as much as

girls?

tabulate bullied sex, col chi2 nofreq

Exercise 5

• The variable “magegr2” contains mother’s age in two groups. Do tab magegr2 and tab magegr2, nolab to find the groups and the coding. An alternative to find coding is to list all labels: label list

• Make a plot of the birth weight distribution for each of the two groups of mother’s age.

• Do a ttest of weight by magegr2. Are the means different?

• Redo the ttest for weight>2000 to get more normal distributions.• Are the means different?

• Are the p-values different?

• Generate an indicator for high birth weight (>4500).

• Make a table of high birth weight by gestgr2 with columns percent and chi-square test

Extra (if you have time)

•Do a help tabstat and look at the statistics options

•Do a tabstat of weight showing N min p25 p50 p75 max, by magegr2

Summing up

•Descriptivesummarize weighttabulate sex

•Graphstwoway (plot1, opts) (plot2, opts),

opts

•Bivariate•ttest weight, by(sex)•tabulate bullied sex, chi2

36

EXTRA MATERIAL

Save output (Log results)

•Save a portion of the analysis as a .smcl file

log using “results.smcl”…log close

Keep plots during session

•Set “tabbed” graphics

•Give each plot a name

set autotabgraphs on, permanently

twoway …, name(“scatter”,replace)

Copy output

•Copy graphs to Word or PowerPoint

• Save graphs in many formats, or• Right-click on a graph to copy

•Copy tables to Excel

• Mark table, Ctrl-shift-C