Stata Comparing Two Continuous Groups in on Variable
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Chapter 7: Comparing two groups using STATA
Figure 7.1 Histogram of two groups on one graph
Figure 7.1 Code
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use "C:\Projects\Books\Presenting\data\stataData\ghana.dta", clear label variable rural "type of village" label define rural 1 "Rural" 0 "Semi-urban" label values rural rural histogram bmi if sex==1, width(2) percent ytitle(Percentage of women) /// ylabel(0(5)25) xtitle(Body mass index kg/m2) /// by(, title(Body mass index in 338 semi-rural and 290 urban women in Ghana, size(medium) span) /// note(, color(none) nobox)) by(, legend(off)) by(rural, style(combine) imargin(zero) colfirst)
Figure 7.2 Two pannel or overlapping density plots for two groups
Figure 7.2 Code
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use "C:\Projects\Books\Presenting\data\stataData\ghana.dta", clear twoway kdensity bmi if rural, xtitle("BMI (kg/m2)") ytitle(Density) /// title(Body mass index in 338 semi-rural and 290 urban women in Ghana, size(medium) span) /// subtitle(Kernel density plots, size(medium small) span) /// color(blue*.5) lcolor(blue) lwidth(medthick) || /// kdensity bmi if !rural , /// color(red*.1) lcolor(red) lpattern(dash) lwidth(medthick) /// legend(order(1 "semi-rural women" 2 "rural women") col(1) pos(1) ring(0)) Figure 7.3 Box and whisker plot for a continuous variable in two groups
Figure 7.3 Code
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use "C:\Projects\Books\Presenting\data\stataData\ghana.dta", clear label variable rural "type of village" label define rural 1 "Rural" 0 "Semi-urban" label values rural rural graph box bmi, over(rural) ytitle(Body mass index (kg/m2)) /// title(Body mass index in 338 semi-rural and 290 urban women in Ghana, size(medium) span) /// subtitle(Box and whisker plots, size(medium)) legend(off)
Figure 7.4 Violin plot for a continuous variable comparing two groups
Figure 7.4 Code
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** type 'help vioplot' to see if this package is installed ** if not follow instructions to install into c:\ado\plus ** ssc install vioplot use "C:\Projects\Books\Presenting\data\stataData\ghana.dta", clear label define rural_label 0 "Semi-urban" 1 "Rural" label values rural rural_label vioplot bmi, over( rural) title("BMI in 338 semi-urban, 290 rural women in Ghana") /// ytitle("Body Mass Index Kg/sq metre") Figure 7.5 Dot plot for a continuous variable comparing two groups
Figure 7.5 Code
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use "C:\Projects\Books\Presenting\data\stataData\electricity.dta", clear * first collapse the data set to give % with electricty by village collapse (mean) elec36 rural, by(village) label values rural rural label variable rural "Locality" label variable elec36 "% Villages with electricity" dotplot elec36, over(rural) center /// ylabel(0 20 40 60 80 100) /// title("Proportion of subjects in each of 12 villages with electricity n=1013", size(mediumsmall) span) Figure 7.6 Histogram of a variable to check for normality
Figure 7.6 Code
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use "C:\Projects\Books\Presenting\data\stataData\pvd.dta", clear histogram sbp, width(15) start(70) frequency normal xlabel(70(20)240) /// title(Histogram of systolic blood pressure with a Normal distrbution curve (n=121), size(small) span)
Figure 7.7 Output for an unpaired t-test
Figure 7.7 Code
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use "C:\Projects\Books\Presenting\data\stataData\pvd.dta", clear recode outcome 0=1 1=0 label define outcome 1 "Alive" 0 "Dead", replace label values outcome outcome ttest sbp , by(outcome)
Figure 7.8 Histograms of a skewed variable before and after log transformation
Figure 7.8 Code
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use "C:\Projects\Books\Presenting\data\stataData\pvd.dta", clear label variable cr "Serum creatinine" label variable lcr "Log serum creatinine" **1st create each graph separately histogram cr, width(20) start(50) frequency normal xlabel(50(50)500) title(, size(small) span) graph save Graph "C:\blah\fig7.8a.gph" // replace needed if the graphic exists histogram lcr, width(.15) start(4) frequency normal /// xlabel(4(.5)6) title(, size(small) span) graph save Graph "C:\blah\fig7.8b.gph" // replace needed if the graphic exists ** now combine two graphs graph combine "C:\blah\fig7.8a.gph" "C:\blah\fig7.8b.gph", /// title(Histograms of serum creatinine before and after log transformation with Normal distribution curves (n=121), size(small) span)
Figure 7.9 Output for back transforming t-test data
Box 7.4 Presenting the findings of a t-test on log-transformed data
Figure 7.9 Code and Box 7.4
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use "C:\Projects\Books\Presenting\data\stataData\pvd.dta", clear ttest lcr, by(outcome)
Figure 7.10 Table of data for and output from a Mann-Whitney U test
Figure 7.10 Code
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use "C:\Projects\Books\Presenting\data\stataData\eat.dta", clear tabulate frandveg smokeas ranksum frandveg, by(smokeas)
Figure 7.11 Output for chi-square test relative risk and risk difference
Figure 7.11 Code
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use "C:\Projects\Books\Presenting\data\stataData\eps.dta", clear label define bv 0 "No" 1 "Yes", replace label values bv bv label define age 0 ">=25" 1 "<25", replace label values age age tabulate bv age, col chi2 cs bv age
Figure 7.12 Output for odds ratio
Figure 7.12 Code
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use "C:\Projects\Books\Presenting\data\stataData\crash.dta", clear label define sleep 0 ">5 Hours" 1 "<= Hours", replace label values sleep sleep label define case 0 "Control" 1 "Case", replace label values case case tabulate case sleep, row chi2 cc case sleep
Table 7.1 Presenting ordered proportions
Table 7.1 Code
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** the chisquared test for trend isn't in Stata but a user package ptrend can be installed ** to find the pacakge, type 'findit ptrend' ssc install ptrend ** we use the immediate form here for ease ** note these can be calculated from Table 7.1 as 845, 1021-845 etc ** the x1, x2 etc indicate trhe weighting for the categories. we will use 1,2,3 ptrendi 845 176 1\246 90 2\108 46 3
Source: http://presenting.ccs.miami.edu/Chapter7/stataCode/index.html
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