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Making a figure with Stata or Excel

Making a figure with Stata or Excel. Biostatistics 212 Lecture 7. Housekeeping. Lab 5 cleanup Which p-value is which? Deciding when to “call” an interaction Final Project questions? Print and hand in to Olivia or Allison (5 th floor) by the end of the day on 9/19/06

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Making a figure with Stata or Excel

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  1. Making a figure with Stata or Excel Biostatistics 212 Lecture 7

  2. Housekeeping • Lab 5 cleanup • Which p-value is which? • Deciding when to “call” an interaction • Final Project questions? • Print and hand in to Olivia or Allison (5th floor) by the end of the day on 9/19/06 • 20 points docked for each 1 day late • Email or call for help! • PLEASE DO COURSE EVALUATIONS • You’ll get a link by email

  3. . cs dead anycac, by(ageover60) ageover60 | RR [95% Conf. Interval] M-H Weight -----------------+------------------------------------------------- 0 | 3.294296 2.124413 5.108418 11.78094 1 | 3.372508 1.922288 5.916809 9.848343 -----------------+------------------------------------------------- Crude | 4.763402 3.413478 6.64718 M-H combined | 3.329908 2.345418 4.727639 ------------------------------------------------------------------- Test of homogeneity (M-H) chi2(1) = 0.004 Pr>chi2 = 0.9479

  4. . mhodds dead anycac, by(ageover60) Maximum likelihood estimate of the odds ratio Comparing anycac==1 vs. anycac==0 by ageover60 ------------------------------------------------------------------------------- ageov~60 | Odds Ratio chi2(1) P>chi2 [95% Conf. Interval] ----------+-------------------------------------------------------------------- 0 | 3.343423 31.95 0.0000 2.14383 5.21426 1 | 3.537836 20.94 0.0000 1.98502 6.30536 ------------------------------------------------------------------------------- Mantel-Haenszel estimate controlling for ageover60 ---------------------------------------------------------------- Odds Ratio chi2(1) P>chi2 [95% Conf. Interval] ---------------------------------------------------------------- 3.429722 51.90 0.0000 2.400138 4.900967 ---------------------------------------------------------------- Test of homogeneity of ORs (approx): chi2(1) = 0.02 Pr>chi2 = 0.8776

  5. . xi: logistic dead i.anycac*i.ageover60 i.anycac _Ianycac_0-1 (naturally coded; _Ianycac_0 omitted) i.ageover60 _Iageover60_0-1 (naturally coded; _Iageover60_0 omitted) i.any~c*i.ag~60 _IanyXage_#_# (coded as above) Logistic regression Number of obs = 10372 LR chi2(3) = 188.43 Prob > chi2 = 0.0000 Log likelihood = -1065.5418 Pseudo R2 = 0.0812 ------------------------------------------------------------------------------ dead | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Ianycac_1 | 3.343423 .7564415 5.33 0.000 2.145898 5.209232 _Iageover6~1 | 3.075541 1.040157 3.32 0.001 1.585049 5.96761 _IanyXage_~1 | 1.058148 .3922776 0.15 0.879 .5116676 2.188289 ------------------------------------------------------------------------------

  6. . logistic dead anycac ageover60 Logistic regression Number of obs = 10372 LR chi2(2) = 188.41 Prob > chi2 = 0.0000 Log likelihood = -1065.5534 Pseudo R2 = 0.0812 ------------------------------------------------------------------------------ dead | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- anycac | 3.415374 .6105826 6.87 0.000 2.40583 4.848547 ageover60 | 3.223499 .4453269 8.47 0.000 2.458861 4.225919 ------------------------------------------------------------------------------

  7. . cs dead anycac, by(male) male | RR [95% Conf. Interval] M-H Weight -----------------+------------------------------------------------- 0 | 5.901622 3.668887 9.493106 8.3619 1 | 4.287304 2.679298 6.860369 11.74511 -----------------+------------------------------------------------- Crude | 4.763402 3.413478 6.64718 M-H combined | 4.95865 3.549869 6.926513 ------------------------------------------------------------------- Test of homogeneity (M-H) chi2(1) = 0.883 Pr>chi2 = 0.3473

  8. Housekeeping • Lab 5 cleanup • Which p-value is which? • Deciding when to “call” an interaction • Final Project questions? • Print and hand in to Olivia or Allison (5th floor) by the end of the day on 9/19/06 • 20 points docked for each 1 day late • Email or call for help! • PLEASE DO COURSE EVALUATIONS • You’ll get a link by email

  9. Today... • Figure basics • Why make a figure? • Types of figures • Elements of a figure • Steps in making a figure • Stata versus Excel • The Final Project, grading

  10. Figures • Figures are GOOD for presenting overall effects • Figure are NOT GOOD for presenting specific measurements Browner, W. Publishing and Presenting Clinical Research

  11. Figures • “A picture is worth a thousand words”

  12. Figures • “A picture is worth a thousand words” How many words is this picture worth?

  13. Figures • “A picture is worth a thousand words” 48% of CARDIA participants consume alcohol moderately. How many words is this picture worth? Worth = 7 words

  14. Figures • “A picture is worth a thousand words” How many words is this picture worth?

  15. Figures • “A picture is worth a thousand words” White Black Drinks/day n=1935 n=1727 0 40% 57% 0.1-0.9 39% 26% 1-1.9 13% 9% 2+ 8% 8% How many words is this picture worth? Worth = 1 small table

  16. Figures • “A picture is worth a thousand words” How many words is this picture worth?

  17. Figures • “A picture is worth a thousand words” % with CAC Abstainer Mod drinker Black women .047 .036 White women .054 .049 Black men .068 .132 White men .180 .167 How many words is this picture worth?

  18. Figures • “A picture is worth a thousand words” How many words is this picture worth? Worth = “A thousand words”?

  19. Figures • “A picture is worth a thousand words” How many words is this picture worth?

  20. Figures • “A picture is worth a thousand words” How many words is this picture worth? Worth = 968 data points + lines > 1000 words?

  21. Figures • “A picture is worth a thousand words” • Guidelines • Figures should have a minimum of 4 data points • Convey important effects, or interactions Browner, W. Publishing and Presenting Clinical Research

  22. Figures • Types of figures • Photographs • Diagrams • Data representation Browner, W. Publishing and Presenting Clinical Research

  23. Figures • Types of data figures • Pie charts • Bar graphs • Line graphs • Scatter plots • Box plots Browner, W. Publishing and Presenting Clinical Research

  24. Figures • Elements of a figure • Graphics (non-text) • Labels (axes, lines/bars, etc), other text • Figure legend (Title, explanations, p-values) Browner, W. Publishing and Presenting Clinical Research

  25. Steps in making a Figure • In Excel: • Sketch the Figure, with title • Dummy data table in Excel • Write a do file to fill in table • Copy and paste the data in • Format so it makes sense and looks nice • Compose a figure legend • In Stata: • Sketch the Figure, with title • Write a do file • Format so it makes sense and looks nice • Compose a figure legend

  26. Steps in making a Figure • Sketch the Figure, with title • Try several versions • Point should be clear at a glance • Requires some artistic vision…

  27. Steps in making a Figure • Can I make this figure with Stata?

  28. Stata vs. Excel for Figures • Stata • Can create very customizable figures using 1 complex Stata command • Easy to recreate – simple do file • No error • Scatter plots are MUCH easier with Stata • But… • Harder to create the first time? - no point and click • A little less flexible?

  29. Stata vs. Excel for Figures • Excel • Flexible and intuitive point-and-click figures • Easy to create and modify • Flexible, more options, error bars, adjusted estimates, etc • But… • Requires an extra step – copy/pasting to Excel • Harder to reproduce • Much harder to do scatter plots

  30. Stata vs. Excel for Figures • Both Stata and Excel can produce very nice-looking figures.

  31. Steps in making a Figure • Write a do file • If making a figure with Stata, your do file might contain only 1 actual Stata command

  32. Steps in making a Figure • Write a do file • If making a figure with Stata, your do file might contain only 1 actual Stata command twoway (scatter dfev1 cumpy10 if menthol1==1, msymbol(plus) msize(small) mcolor(black)) /// (scatter dfev1 cumpy10 if menthol1==0, msymbol(circle_hollow)) /// (line m cumpy10 if menthol1==1, sort clcolor(black) clpat(dash) clwidth(thick)) /// (line nm cumpy10 if menthol1==0, sort clcolor(black) clpat(solid) clwidth(thick)) /// , ytitle(Change in FEV1 (milliliters), size(large)) yscale(titlegap(5)) /// xtitle(Pack-years of exposure to tobacco, size(large)) /// xscale(titlegap(3)) /// legend(order(1 "Menthol smokers" 2 "Non-menthol smokers" 3 "Menthol regression" /// 4 "Non-menthol regression")) /// scheme(s1mono) /// graphregion(fcolor(none) lcolor(none) ifcolor(none) ilcolor(none)) /// plotregion(fcolor(none) lcolor(none) ifcolor(none) ilcolor(none))

  33. Steps in making a Figure • Write a do file • If making a figure with Stata, your do file might contain only 1 actual Stata command • Compose using dialog box from menu

  34. Steps in making a Figure • Write a do file • If making a figure with Stata, your do file might contain only 1 actual Stata command • Compose using dialog box from menu • If making it with Excel, you’ll need to produce all the numbers with analysis • Paste into Excel from log file • Use Chart Wizard

  35. Steps in making a Figure • Write a do file • If making a figure with Stata, your do file might contain only 1 actual Stata command • Compose using dialog box from menu • If making it with Excel, you’ll need to produce all the numbers with analysis • Paste into Excel from log file • Use Chart Wizard • Either way, you may need additional Stata commands for p-values, figure legend, etc

  36. Steps in making a Figure • Format so it looks nice, and makes sense • With Stata: • Use dialog box • Submit, modify, submit again, etc • With Excel • Point, click, modify

  37. Steps in making a Figure • Compose a figure legend • Title, explanations, p-values, etc • Separate section in manuscript or at bottom of page – depends on journal

  38. Steps in making a Figure • Example – Excel • Example - Stata

  39. twoway (scatter dfev1 cumpy10 if menthol1==1, msymbol(plus) msize(small) mcolor(black)) /// (scatter dfev1 cumpy10 if menthol1==0, msymbol(circle_hollow)) /// (line m cumpy10 if menthol1==1, sort clcolor(black) clpat(dash) clwidth(thick)) /// (line nm cumpy10 if menthol1==0, sort clcolor(black) clpat(solid) clwidth(thick)) /// , ytitle(Change in FEV1 (milliliters), size(large)) yscale(titlegap(5)) /// xtitle(Pack-years of exposure to tobacco, size(large)) /// xscale(titlegap(3)) /// legend(order(1 "Menthol smokers" 2 "Non-menthol smokers" 3 "Menthol regression" /// 4 "Non-menthol regression")) /// scheme(s1mono) /// graphregion(fcolor(none) lcolor(none) ifcolor(none) ilcolor(none)) /// plotregion(fcolor(none) lcolor(none) ifcolor(none) ilcolor(none))

  40. graph bar (mean) cac /// , over(modalc) /// over(racegender) /// asyvars /// ytitle(Prevalence of coronary calcification) /// title("Prevalence of coronary calcification in moderate drinkers and abstainers", /// size(medium) span) /// subtitle("By race and gender", size(medsmall))

  41. Summary figure tips • Only use a Figure if: • There is an important message to convey • The visual will be more compelling and clear • Try using both Stata and Excel • Stata will be harder at first, but often worth it • Browner book, Stata book both helpful • Document, label, and be creative

  42. Final Project, grading • Grading • 80% (256 out of 319 possible) required to get a “Satisfactory” score in the class • Also need to turn in all the Labs, even if they are late

  43. Final Project, grading • Final Project will count for 150/319 points • Table – 75 points • 35 for do file log • Housekeeping commands: open/close log, use dataset, etc • Analysis: generate numbers in the Table • 40 for Table itself • Architecture • Documentation • Formatting/appearance

  44. Final Project, grading • Final Project will count for 150/319 points • Figure – 75 points • 35 for do file log • Housekeeping commands: open/close log, use dataset, etc • Analysis • 40 for Figure itself • Design • Documentation • Formatting/appearance

  45. Final Project, grading • Extra credit • 10 points extra credit and bragging rights for the most artistic, creative, and clear figure turned in

  46. Final Project, grading • Advice • Find a classmate, give them your Table and Figure, and get their critiques. • See if they can understand it without any verbal explanation

  47. That’s it! • Thanks for your active participation in the course.

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