I recently had to make some boxplots with Stata’s –graph box– command. I find this to be challenging to use since it varies from syntax from the –twoway– command set that I use all the time. I was using the –over– subcommand x2 and wanted to change the colors of each box & dots by group from the first –over– subcommand. I found some helpful details on the Statalist Forum here and here. Here’s what I did to accomplish this, using the help from the Statalist forum.

Some tweaks here: I wanted to show rotate some labels 45 degrees with –angle– and I also aggressively labeled variables and their values so I didn’t need to manually relabel the figure (which is done with the –relabel– subcommand if needed). It takes an extra 30 seconds to label variables and values, and will save you lots of headbanging fiddling with the –relabel– command, so just label your variables and values from the start.

This example uses fake data. Code follows the picture. Good luck!

clear all
set obs 1000 // blank dataset with 1000 observations
set seed 8675309 // jenny seed
gen group = round(runiform()) // make group that is 0 or 1
gen time = round(3*runiform()) // make 4 times, 0 through 3
replace time = time+1 // now time is 1 through 4
gen tommy2tone = 100*rbeta(3,10) // fake skewed data
// now apply labels to variables.
// technically you only need to label the 3rd one
// of these since categorical variable value labels
// are shown instead of the variable label itself,
// but might as well do all 3 in case you need them
// labeled somewhere else.
label variable group "Group"
label variable time "Time"
label variable tommy2tone "Jenny Lyrics remembered, %"
// now make value labels.
* group
label define grouplab 0 "Tommy" 1 "Tutone"
label values group grouplab //DON'T FORGET TO APPLY LABELS
* time
label define timelab 1 "Time 1" 2 "Time 2" 3 "Time 3" 4 "Time 4"
label values time timelab //DON'T FORGET TO APPLY LABELS
// code for boxplot
graph box tommy2tone ///
, ///
over(group, label(angle(45))) ///
over(time) ///
scale(1.3) /// embiggen labels & figure components
box(1, color(red)) marker(1, mcolor(red)) ///
box(2, color(blue)) marker(2, mcolor(blue)) ///
asyvars showyvars leg(off)

Frames were introduced in Stata 16 and are handy for (a) storing/manipulating multiple datasets simultaneously and (b) building datasets on the fly. I’ve had good luck making a table using frames. This strategy includes (1) making a new frame with as many columns as you need, specifying they are long strings (strL), and printing the top row, (2) running regressions or whatnot, (3) saving components of the regression as local macros that print as text, (4) making “display” macros for each column, (5) saving those “display” local macros to the new frame, repeating steps 2-5 as many times as needed, and (6) exporting the new frame as an Excel file.

Stata has recently introduced “tables” and “collect” features that allow you to do similar things. I find those features overly confusing, so I’ve developed this approach. I hope you find it useful! Here’s what we are trying to make:

Note: these details depend on use of concepts discussed on this other post (“return list” “ereturn list” “matrix list r(table)”, local macros, etc.). Make sure to familiarize yourself with that post.

Problems you might run into here:

This uses local macros, which disappear when a do file stops running. You need to run the entire do file from top to bottom every time, not line by line.

Stata’s -frames post- functionality is finicky. It expects exactly as many posted variables as there are variables in the new frame, and that each posted format matches the predefined format on the frame’s variable.

Three forward slashes (“///”) extends things across lines. Two forward slashes (“//”) allows commenting but does not continue to the next line. Check your double and triple forward slashes.

Stata can’t write to an open excel file. Close the excel file before re-running.

Code

* Reset frames and reload data
frames reset
sysuse auto, clear
*
* STEP 1 - Make a new frame with long strings
*
* Make new frame called "table" and
* define all columns/variables as being strings.
* Name all variables as col#, starting with col1.
* You can extend this list as long as you'd like.
frame create table /// <--TRIPLE SLASH
strL col1 /// name
strL col2 /// n
strL col3 /// beta
strL col4 /// 95% CI
strL col5 // <-- DOUBLE SLASH, P-value
* If you want to add more col#s, make sure you change
* the last double slashes to triple slashes, all new
* col#s should have triple slashes except the last,
* which should have double (or no) slashes
*
* Prove to yourself that you have made a new frame
* called "table" in addition to the "default" one
* with the auto dataset.
frames dir
* You could switch to the new table frame with
* the "cwf table" command, if interested. To switch
* back, type "cwf default".
*
* STEP 1B - print out your first row
*
frame post table /// <--TRIPLE SLASH
("Variable name") /// col1
("N") /// col2
("Beta") /// col3
("95% CI") /// col4
("P-value") // <--DOUBLE SLASH col5
*
* You can repeat this step as many times as you want
* to add as many rows of custom text as needed.
* Note that if you want a blank column, you need
* to still include the quotations within the parentheses.
* eg, if you didn't want the first column to have
* "variable name", you'd put this instead:
* strL ("") /// col1
*
* If you wanted to flip over to the table frame and
* see what's there, you'd type:
*cwf table
*bro
*
* ...and to flip back to the default frame, type:
*cwf default
*
* STEP 2 - run your regression and look where the
* coefficients of interest are stored
* and
* STEP 3 - saving components of regression as local
* macros
*
regress price weight
*
ereturn list
* The N is here under e(N), save that as a local macro
local n1_1 = e(N)
*
matrix list r(table)
* The betas is at [1,1], the 95% thresholds
* are at [5,1] and [6,1], the se is
* at [2,1], and the p-value is at [4,1].
* We weren't planning on using se in this table,
* but we'll grab it anyway in case we change our minds
local beta1_1 = r(table)[1,1]
local ll1_1 = r(table)[5,1]
local ul1_1 = r(table)[6,1]
local se1_1 = r(table)[2,1]
local p1_1 = r(table)[4,1]
*
* STEP 4 - Making "display" macros
*
* We are going to use local macros to grab things
* by column with a "display" commmand. Note that
* column 1 is name, which we are going to call "all" here.
* You could easily grab the variable name here with
* the "local lab:" command detailed here:
* https://www.stata.com/statalist/archive/2002-11/msg00087.html
* or:
*local label_name: variable label foreign
* ...then call `label_name' instead of "all"
*
* Now this is very important, we are not going to just
* capture these variables as local macros, we are going
* to capture a DISPLAY command followed by how we
* want the text to be rendered in the table.
* Immediately after defining the local macro, we are going
* to call the macro so we can see what it will render
* as in the table. This is what it will look like:
*local col1 di "All"
*`col1'
*
* IF YOU HAVE A BLANK CELL IN THIS ROW OF YOUR TABLE,
* USE TWO EMPTY QUOTATION MARKS AFTER THE "di" COMMAND, eg:
* local col1 di ""
*
* now we will grab all pieces of the first row,
* column-by-column, at the same time:
local col1 di "All" // name
`col1'
local col2 di `n1_1' // n
`col2'
local col3 di %4.2f `beta1_1' // betas
`col3'
local col4 di %4.2f `ll1_1' " to " %4.2f `ul1_1' // 95% ci
`col4'
local col5 di %4.3f `p1_1' // p-value
`col5'
*
* note for the P-value, you can get much fancier in
* formatting, see my script on how to do that here:
* https://blog.uvm.edu/tbplante/2022/10/26/formatting-p-values-for-stata-output/
*
* STEP 5 - Posting the display macros to the table frame
*
* Now post all columns row-by-row to the table frame
frame post table ///
("`: `col1''") ///
("`: `col2''") ///
("`: `col3''") ///
("`: `col4''") ///
("`: `col5''") // <-- DOUBLE SLASH
*
* Bonus! Insert a text row
*
frame post table /// <--TRIPLE SLASH
("By domestic vs. foreign status") /// col1
("") /// col2
("") /// col3
("") /// col4
("") // <--DOUBLE SLASH col5
* Now repeat by foreign status
* domestic
regress price weight if foreign ==0
ereturn list
local n1_1 = e(N)
*
matrix list r(table)
local beta1_1 = r(table)[1,1]
local ll1_1 = r(table)[5,1]
local ul1_1 = r(table)[6,1]
local se1_1 = r(table)[2,1]
local p1_1 = r(table)[4,1]
*
* note: you could automate col1 with the following command,
* which would grabe the label from the foreign==0 value of
* foreign:
*local label_name: label foreign 0
* ... then call `label_name' in the "local col1 di".
local col1 di " Domestic" // name, with 2 space indent
`col1'
local col2 di `n1_1' // n
`col2'
local col3 di %4.2f `beta1_1' // betas
`col3'
local col4 di %4.2f `ll1_1' " to " %4.2f `ul1_1' // 95% ci
`col4'
local col5 di %4.3f `p1_1' // p-value
`col5'
*
frame post table ///
("`: `col1''") ///
("`: `col2''") ///
("`: `col3''") ///
("`: `col4''") ///
("`: `col5''") // <-- DOUBLE SLASH
*
* foreign
regress price weight if foreign ==1
ereturn list
local n1_1 = e(N)
*
matrix list r(table)
local beta1_1 = r(table)[1,1]
local ll1_1 = r(table)[5,1]
local ul1_1 = r(table)[6,1]
local se1_1 = r(table)[2,1]
local p1_1 = r(table)[4,1]
*
local col1 di " Foreign" // name, with 2 space indent
`col1'
local col2 di `n1_1' // n
`col2'
local col3 di %4.2f `beta1_1' // betas
`col3'
local col4 di %4.2f `ll1_1' " to " %4.2f `ul1_1' // 95% ci
`col4'
local col5 di %4.3f `p1_1' // p-value
`col5'
*
frame post table ///
("`: `col1''") ///
("`: `col2''") ///
("`: `col3''") ///
("`: `col4''") ///
("`: `col5''") // <-- DOUBLE SLASH
*
* STEP 6 - export the table frame as an excel file
*
* This is the present working directory, where this excel
* file will be saved if you don't specify a directory:
pwd
*
* Switch to the table frame and take a look at it:
cwf table
bro
* Now export it to excel
export excel using "table.xlsx", replace
*
* That's it!

I recently had a dataset with two groups (0 or 1), and a continuous variable. I wanted to show how the overall deciles of that continuous variable varied by group. Step 1 was to generate an overall decile variable with an –xtile– command. Step 2 was to make a frequency histogram. BUT! I wanted these histograms to overlap and not be side-by-side. Stata’s handy –histogram– is a quick and easy way to make histograms by groups using the –by– command, but it makes them side-by-side like this, and not overlapping. (Note: see how to use –twoway histogram– to make overlapping histograms at the end of this post.)

I instead used a collapse command to generate a count of # in each decile by group (using the transparent color command as color percent sign number), like this:

Here’s the code to make both:

clear all
// make fake data
set obs 1000
set seed 8675309
gen id=_n // ID of 1 though 100
gen var0or1 = round(runiform())
gen continuousvalue = 100*runiform()
// make overall deciles of continuousvalue
xtile decilesbygroup = continuousvalue, nq(10)
// now make a frequency histogram of those deciles
set scheme s1color // I like this scheme
hist decilesbygroup, by(var0or1) frequency bin(10)
// make a variable equal to 1 that we will sum in collapse
gen countbygroup = 1
// now sum that variable by the 0 or 1 indicator and deciles
collapse (sum) countbygroup, by(var0or1 decilesbygroup)
// now render the count from above as a bar graph:
set scheme s1color // I like this scheme
twoway ///
(bar countbygroup decilesbygroup if var0or1==0, vertical color(red%40)) ///
(bar countbygroup decilesbygroup if var0or1==1, vertical color(blue%40)) ///
, ///
legend(order(1 "var0or1==0" 2 "var0or1==1")) ///
title("Title!") ///
xtitle("Decile of continuousvalue") ///
xla(1(1)10) ///
yla(0(10)70, angle(0)) ///
ytitle("N in Decile")

You could also offset the deciles by the var0or1 and shrink the bar width a bit to get a frequency histogram where the bars are next to each other, like this:

clear all
// make fake data
set obs 1000
set seed 8675309
gen id=_n // ID of 1 though 100
gen var0or1 = round(runiform())
gen continuousvalue = 100*runiform()
// make overall deciles of continuousvalue
xtile decilesbygroup = continuousvalue, nq(10)
// now make a frequency histogram of those deciles
set scheme s1color // I like this scheme
hist decilesbygroup, by(var0or1) frequency bin(10)
// offset the decilesbygroup by var0or1 a bit:
replace decilesbygroup = decilesbygroup - 0.2 if var0or1==0
replace decilesbygroup = decilesbygroup + 0.2 if var0or1==1
// make a variable equal to 1 that we will sum in collapse
gen countbygroup = 1
// now sum that variable by the 0 or 1 indicator and deciles
collapse (sum) countbygroup, by(var0or1 decilesbygroup)
// now render the count from above as a bar graph:
set scheme s1color // I like this scheme
twoway ///
(bar countbygroup decilesbygroup if var0or1==0, vertical color(red%40) barwidth(0.4)) ///
(bar countbygroup decilesbygroup if var0or1==1, vertical color(blue%40) barwidth(0.4)) ///
, ///
legend(order(1 "var0or1==0" 2 "var0or1==1")) ///
title("Title!") ///
xtitle("Decile of continuousvalue") ///
xla(1(1)10) ///
yla(0(10)70, angle(0)) ///
ytitle("N in Decile")

A few quick notes here: The way that I am specifying the “bins” for the histograms here is different than how Stata specifies bins for histograms, since I’m forcing it to render by decile. If you were to generate a histogram of the “continuousvalue” instead of the above example using “decilebygroup”, you’ll notice that the resulting histograms looks a bit different from each other:

clear all
// make fake data
set obs 1000
set seed 8675309
gen id=_n // ID of 1 though 100
gen var0or1 = round(runiform())
gen continuousvalue = 100*runiform()
// make overall deciles of continuousvalue
xtile decilesbygroup = continuousvalue, nq(10)
// now make a frequency histogram of those deciles
set scheme s1color // I like this scheme
hist decilesbygroup, title("hist decilesbygroup") by(var0or1) frequency bin(10) name(a)
hist continuousvalue, title("hist continuousvalue") by(var0or1) frequency bin(10) name(b)

Also, this code will only render frequency histograms, not density histograms, which are the default in Stata. You can also use the –twoway hist– command to overlay two bar graphs, but these might not perfectly align with the deciles. But, using the –twoway hist– allows you to use density histograms instead. See the example that follows. I suspect that most people will get what they need with the –twoway hist– command in Stata.

clear all
// make fake data
set obs 1000
set seed 8675309
gen id=_n // ID of 1 though 100
gen var0or1 = round(runiform())
gen continuousvalue = 100*runiform()
set scheme s1color // I like this scheme
twoway ///
(hist continuousvalue if var0or1==0, bin(10) color(red%40) density) ///
(hist continuousvalue if var0or1==1, bin(10) color(blue%40) density) ///
, ///
legend(order(1 "var0or1==0" 2 "var0or1==1")) ///
title("Title!") ///
xtitle("Grouping in 10 Bins")

Stata has some handy built-in features to make boxplots. If you are trying to make a typical boxplot in Stata, go read up on the –graph box– command as described on this post.

I was asked to abstract a boxplot from an old paper and re-render it in Stata.

I used the excellent WebPlotDigitizer to abstract the points in these figure. It took me a few rounds of data abstraction to get all of the data. I generated the following variables

(1) “row” – rows 1-8 — the “A-D” labels on the x axis are offset at 1.5, 3.5, 5.5, and 7.5, respectively,

(2) “group” – an indicator for group 1 or 2,

(3-5) “boxlow” “boxmid” and “boxhigh” – the lower, mid, and upper bounds of the box,

(6-7) “bar1” and “bar2” – the low and upper end of the bars, and

(8) “dot” – extreme values.

I used “input” at the top of a do file to load these data. The colors were taken from ColorBrewer2. The “box” are just really wide pcspike lines with overlaying horizontal bars as floating text boxes to indicate the bottom, median, and top of the box. One problem is the placement of the horizontal lines on and above/below the box itself, since the “―” ascii character isn’t perfectly in the middle of the vertical space that is used to render it. To get around this, I included an offset value that could be modified to shift these lines up and down so they lined up with the boxes. The “whiskers” are an rcap. The extreme values are just scatterplot dots.

I was the analyst for the myPACE trial (published here in JAMA Cardiology), and needed to put together a subgroup analysis figure. I didn’t find any helpful stock code, so I wrote my own. The code uses the Frames feature that was introduced in Stata 16. It will (a) make a new frame with the required variables but no data, (b) generate a new dichotomous variable from continuous variables, (c) generate labels, (d) grab the Ns, point estimates, and 95% CI from a logistic regression, (e) grab the P-value for interaction for the primary exposure dependent variable*, (f) write the point estimate/95% CI/P-value for interaction to the new frame, then (g) switch to the new frame and make this figure. This script uses local macros so needs to be run all at once in a do file, not line by line.

This uses a stock Stata dataset called “catheter”. The outcome of interest/dependent variable is “infect”, the primary exposure/independent variable of interest is “time”, and the subgroups are age, sex, and patient number. This uses logistic regression, but you can easily swap this model out for any other model.

*You can get more complex code to format the P-values here.

Here’s the code!

frame reset // drop all existing frames and data
webuse catheter, clear // load analytical dataset
version 16 // need Stata version 16 or newer
*
* Make an empty frame with the variables we'll add later row by row
* The variables "rowname" and "pvalue" will be strings so when
* you add to these variables with the --frame post-- command,
* you need to use quotes.
frame create subgroup str30 rowname n beta low95 high95 str30 pval
*
*** Age
* Need to generate a dichotomous age variable
* You don't need to do this if the variable is already dichotomous,
* ordinal, or nominal
generate agesplit = (age>=50) if !missing(age) // below 50 is 0, 50 and above is 1
*
* Now generate a label for the overall grouping
local label_group "{bf:Age}"
*
* Group 0, below age 50
* Generate label for this subgroup
local label_subgroup_0 "Under 50y"
* Now run the model for this subgroup
logistic infect time if agesplit==0
* Now save the N, beta, and 95% CI as local macros.
* There's lots you can save after a regression, type --return list--,
* --ereturn list--, and --matrix list r(table)-- to see what's there
local n_0 = e(N)
local beta_0 = r(table)[1,1]
local low95_0 = r(table)[5,1]
local high95_0 = r(table)[6,1]
* print above local macros to prove you collected them correctly:
di "For `label_subgroup_0', n=" `n_0' ", beta (95% CI)=" %4.2f `beta_0' " (" %4.2f `low95_0' " to " %4.2f `high95_0' ")"
*
* Group 1, at least 50 years old
local label_subgroup_1 "At least 50y"
logistic infect time if agesplit==1
local n_1 = e(N)
local beta_1 = r(table)[1,1]
local low95_1 = r(table)[5,1]
local high95_1 = r(table)[6,1]
di "For `label_group', subgroup `label_subgroup_1', n=" `n_1' ", beta (95% CI)=" %4.2f `beta_1' " (" %4.2f `low95_1' " to " %4.2f `high95_1' ")"
*
* Now run the model with an interaction term between the primary exposure
* and the subgroup.
logistic infect c.time##i.agesplit
* Grab the p-value as a local macro saved as pval
local pval = r(table)[4,5]
* Print that local macro to see that you've grabbed it correctly
di "P-int = " %4.3f `pval'
* Format that P-value and save that as a new local macro called pvalue
local pvalue "P=`: display %3.2f `pval''"
di "`pvalue'"
*
* Now write your local macros to the the "subgroup" frame
* Each "frame post" command will add 1 additional row to the frame.
* We will graph these line by line.
* First line with overall group name a P-value for interaction:
frame post subgroup ("`label_group'") (.) (.) (.) (.) ("`pvalue'")
* Now each subgroup by itself:
frame post subgroup ("`label_subgroup_0'") (`n_0') (`beta_0') (`low95_0') (`high95_0') ("")
frame post subgroup ("`label_subgroup_1'") (`n_1') (`beta_1') (`low95_1') (`high95_1') ("")
* Optional blank line:
frame post subgroup ("") (.) (.) (.) (.) ("")
*
*** Female sex
* This is already dichotomous, so don't need to create a new variable
* like we did for age.
local label_group "{bf:Sex}"
*group 0, males
local label_subgroup_0 "Males"
logistic infect time if female==0
local n_0 = e(N)
local beta_0 = r(table)[1,1]
local low95_0 = r(table)[5,1]
local high95_0 = r(table)[6,1]
*group 1, females
local label_subgroup_1 "Females"
logistic infect time if female==1
local n_1 = e(N) // N
local beta_1 = r(table)[1,1]
local low95_1 = r(table)[5,1]
local high95_1 = r(table)[6,1]
*interaction P-value
logistic infect c.time##i.female
local pval = r(table)[4,5]
local pvalue "P=`: display %3.2f `pval''"
*write to subgroup frame
frame post subgroup ("`label_group'") (.) (.) (.) (.) ("`pvalue'")
frame post subgroup ("`label_subgroup_0'") (`n_0') (`beta_0') (`low95_0') (`high95_0') ("")
frame post subgroup ("`label_subgroup_1'") (`n_1') (`beta_1') (`low95_1') (`high95_1') ("")
frame post subgroup ("") (.) (.) (.) (.) ("")
*
*** patient
* need to generate a patient dichotomous variable
generate patientsplit = (patient>=20) if !missing(patient) // below 20 is 0, 20 and above is 1
local label_group "{bf:Patient}"
*group 0, below 20
local label_subgroup_0 "Under 20th patient"
logistic infect time if patientsplit==0
local n_0 = e(N)
local beta_0 = r(table)[1,1]
local low95_0 = r(table)[5,1]
local high95_0 = r(table)[6,1]
*group 1, 20 and above
local label_subgroup_1 "At least the 20th patient"
logistic infect time if patientsplit==1
local n_1 = e(N)
local beta_1 = r(table)[1,1]
local low95_1 = r(table)[5,1]
local high95_1 = r(table)[6,1]
*interaction P-value
logistic infect c.time##i.agesplit
local pval = r(table)[4,5]
local pvalue "P=`: display %3.2f `pval''"
*write to subgroup frame
frame post subgroup ("`label_group'") (.) (.) (.) (.) ("`pvalue'")
frame post subgroup ("`label_subgroup_0'") (`n_0') (`beta_0') (`low95_0') (`high95_0') ("")
frame post subgroup ("`label_subgroup_1'") (`n_1') (`beta_1') (`low95_1') (`high95_1') ("")
frame post subgroup ("") (.) (.) (.) (.) ("")
*
*** Now make the figure. You'll have to modify this so the number of rows
* in your subgroup frame matches the labels and whatnot
set scheme s1mono // I like this scheme
* Change frame to the subgroup frame
cwf subgroup
* Generate a row number by the current order of the data in this frame
gen row=_n
* Here's the code to make the figure
twoway ///
(scatter row beta, msymbol(d) mcolor(black) msize(medium)) ///
(rcap low95 high95 row, horizontal lcolor(black) lwidth(medlarge)) ///
, ///
legend(off) ///
xline(1, lcolor(red) lpattern(dash) lwidth(medium)) ///
title("Title") ///
yti("Y Title") ///
xti("X Title") ///
yscale(reverse) ///
yla( ///
1 "`=rowname[1]'" ///
2 "`=rowname[2]', n=`=n[2]'" ///
3 "`=rowname[3]', n=`=n[3]'" ///
4 " " /// blank since it's a blank row
5 "`=rowname[5]'" ///
6 "`=rowname[6]', n=`=n[6]'" ///
7 "`=rowname[7]', n=`=n[7]'" ///
8 " " /// blank since it's a blank row
9 "`=rowname[9]'" ///
10 "`=rowname[10]', n=`=n[10]'" ///
11 "`=rowname[11]', n=`=n[11]'" ///
12 " " /// blank since it's a blank row
, angle(0) labsize(small) noticks) ///
xla(0.8(.2)2.2) ///
text(1 1.1 "`=pval[1]'", placement(e) size(small)) /// these are the p-value labels
text(5 1.1 "`=pval[5]'", placement(e) size(small)) ///
text(9 1.1 "`=pval[9]'", placement(e) size(small))
*
* Now export your figure as a PNG file
graph export "myfigure.png", replace width(1000)

If you like automating your Stata output, you have probably struggled with how to format P-values so they display in a format that is common in journals, namely:

If P>0.05 and not close to 0.05, P has an equals sign and you round at the hundredth place

E.g., P=0.2777 becomes P=0.28

If P is close to 0.05 , P has an equals sign and you round at the ten thousandth place

E.g., P=0.05033259 becomes P=0.0503

E.g., P=0.0492823 become P=0.0493

If well below 0.05 but above 0.001, P has an equals sign and you round at the hundredth place

E.g., P=0.0028832 becomes P=0.003

if below 0.001, display as “P<0.001"

if below 0.0001, display as “P<0.0001"

Here’s a loop that will do that for you. This contains local macros so you need to run it all at once from a do file, not line by line. You’ll need to have grabbed the P-value as a from Stata as a local macro named pval. It will generate a new local macro called pvalue that you need to treat as a string.

local pval 0.05535235 // change me to play around with this
if `pval'>=0.056 {
local pvalue "P=`: display %3.2f `pval''"
}
if `pval'>=0.044 & `pval'<0.056 {
local pvalue "P=`: display %5.4f `pval''"
}
if `pval' <0.044 {
local pvalue "P=`: display %4.3f `pval''"
}
if `pval' <0.001 {
local pvalue "P<0.001"
}
if `pval' <0.0001 {
local pvalue "P<0.0001"
}
di "original P is " `pval' ", formatted is " "`pvalue'"

Here’s how you might use it to format the text of a regression coefficient that you put on a figure.

sysuse auto, clear
regress weight mpg
// let's grab the P-value for the mpg
matrix list r(table) //notice that it's at [4,1]
local pval = r(table)[4,1]
if `pval'>=0.056 {
local pvalue "P=`: display %3.2f `pval''"
}
if `pval'>=0.044 & `pval'<0.056 {
local pvalue "P=`: display %5.4f `pval''"
}
if `pval' <0.044 {
local pvalue "P=`: display %4.3f `pval''"
}
if `pval' <0.001 {
local pvalue "P<0.001"
}
if `pval' <0.0001 {
local pvalue "P<0.0001"
}
di "original P is " `pval' ", formatted is " "`pvalue'"
// now make a scatterplot and print out that formatted p-value
twoway ///
(scatter weight mpg) ///
(lfit weight mpg) ///
, ///
text(3500 35 "`pvalue'", size(large))

It’s handy to add text to your plots to orient your readers to specific observations in your figures. You might opt to highlight a datapoint, add percentages to bars, or say what part of a figure range is good vs. bad.

Adding text to Stata is relatively straightforward, once you get over the initial learning hump. Check out the added text help files by typing —help added_text_options— and —help textbox_options— for details.

Added text syntax

First, make your figure, then add your comma, then add your syntax. Like everything(?) in Stata, the location is specified as Y then X. Then add your text in quotes (or multiple quotes in a row if you want to insert a line break). You can bold or italicize by using the it: and bf: formatting and wrapping the text you want to apply it to in brackets. Then you drop a comma, specify placement as centered (c) or cardinal directions (e.g., ne, s, w) of that text box relative to the Y,X coordinates, justification of the text as left-aligned (left), centered (center), or right-aligned (right), and size of the text (vsmall, small, medsmall, medium, medlarge, large, vlarge, huge as shown in —help textsizestyle–).

Text box without outline, playing around with bolding and italicizing:

You can further customize your box by specifying options shown under —help textbox_options–. Here, I’m making a box with no fill, and a thick/dashed/red outline with fcolor (fill color, which can be “none” for blank/see-through), lcolor (outline color), lpattern (outline pattern), and lwidth (thickness of outline).

That looks pretty cool, but I’d like there to be more space between the text and the border. That’s specified with “margin”. There’s lots of complex options for this command you can read about in —help marginstyle–, but specifying “small” margin will add just a little buffer.

Here’s how you might use the above arrow. Here, I’ve changed the placement to “e” for “east” so the text is to the right and centered of the specified Y,X coordinates of this point (which I looked up, it’s 41,5397). I’ve dropped the box and it’s only one line. I need to manually specify the Y,X coordinates, so it’s a bit clunky still.

sysuse auto, clear
twoway ///
(scatter mpg price) ///
, ///
text(41 5397 "←look at this point!", placement(e) justification(left) size(large))

You’ll notice that the left pointing arrow is inside the point of interest. The left pointing arrow in Ariel text isn’t exactly vertically centered (that’s a quirk of Ariel). You can “nudge” the text box to the right and up by adding an offset to the Y and X coordinates. To do math on an x,y point and add an offset, you place the point in an opening tick (to the left of 1 on your keyboard) and an apostrophe, and putting an equal sign at the beginning. After a bit of trial and error, we want to add an offset of +0.3 to the Y coordinate and +200 to the X coordinate.

sysuse auto, clear
twoway ///
(scatter mpg price) ///
, ///
text(`=41+0.3' `=5397+200' "←look at this point!", placement(e) justification(left) size(large))

If we want to automate the extreme numbers on either axis, we can use the sum command to grab the maximum value aka r(max) of the Y axis and the corresponding x value for that extreme. We can make a local macro to grab that value. Since this uses local macros, you need to run all lines at once in a do file, not line by line.

sysuse auto, clear
// grab yaxis (mpg) extreme
sum mpg, d
// now look at the r-value scalars we can call.
// note that r(max) is 41 and is the maximum.
return list
// now save r(max) as a local macro
local ymax = r(max)
// now let's find the x value for that y value.
// remember that the x axis variable is "price"
sum price if mpg==`ymax', d
// now look at r-value scalars you can call.
// since there's only 1 value, the mean, min, max,
// and sum are all the same.
return list
// let's now grab the r(mean) of as a local macro
local ymax_x = r(mean)
// now you can prove that you grabbed the correct point
di "Y max =" `ymax' ", and that point is at X=" `ymax_x'
// now place those local macros in the Y and X, keeping
// the other offsets
twoway ///
(scatter mpg price) ///
, ///
text(`=`ymax'+0.3' `=`ymax_x'+200' "←look at this point!", placement(e) justification(center) size(large))

You can do the same for the x-axis extreme in the same script. for the X extreme, we’ll change the placement so it’s northwest and also tweak the Y,X coordinate offsets.

sysuse auto, clear
sum mpg, d
local ymax = r(max)
sum price if mpg==`ymax', d
local ymax_x = r(mean)
di "Y max =" `ymax' ", and that point is at X=" `ymax_x'
//
sum price, d
local xmax=r(max)
sum mpg if price==`xmax', d
local xmax_y=r(mean)
di "X max =" `xmax' ", and that point is at Y=" `xmax_y'
twoway ///
(scatter mpg price) ///
, ///
text(`=`ymax'+0.3' `=`ymax_x'+200' "←look at this point!", placement(e) justification(center) size(large)) ///
text(`=`xmax_y'+1' `=`xmax'+260' "look at this point!↓", placement(nw) justification(center) size(large))

What about adding text labels bar graphs?

There’s a simple –graph bar– command, which isn’t terribly customizable. Here, we’ll be using the –twoway bar– bar graphs and add some fancy error bars with –twoway rcap–. The simplest way to do this is to make a new “dataset” from summary statistics in a separate frame. Frames are new in Stata 16, so if you are using an older version, this won’t work.

After making a frame with a bunch of summary statistics by group (most of which we won’t use here, but you might opt to later so we’ll keep it all there), you get this figure of mean and +/- 1.98*SD error bars.

// this has local macros so need to run from top to bottom in a do file!
// clear all frames
version 16
frames reset
sysuse auto, clear
// make a new frame called "bardata" that will store the values for bars.
frame create bardata str20 rowname n low95 high95 mean sd median iqrlow iqrhigh bar_order
//
// now grab summary statistics for foreign==0
// grab 2.5th and 95th %ile and save as local macros
// in case you might want them ever
_pctile mpg if foreign==0, percentiles(2.5 97.5)
local low95 = r(r1)
local high95= r(r2)
// now sum with detail one row
sum mpg if foreign==0, d
// post these to the new frame/bardata
// note the final row, which will be the x axis value that will correspond with bar placement
frame post bardata ("Foreign==0") (r(N)) (r(mean)) (`low95') (`high95') (r(sd)) (r(p50)) (r(p25)) (r(p75)) (1)
//
// now repeat for foreign==1
_pctile mpg if foreign==1, percentiles(2.5 97.5)
local low95 = r(r1)
local high95= r(r2)
sum mpg if foreign==1, d
// note the final row is now 3, leaving a gap of between the prior row
frame post bardata ("Foreign==1") (r(N)) (r(mean)) (`low95') (`high95') (r(sd)) (r(p50)) (r(p25)) (r(p75)) (3)
// now change to the frame with the summary statistics
cwf bardata
// let's make a new variable that's the mean +/- 1.98*sd
gen sd198low = mean-(sd*1.98)
gen sd198high = mean+(sd*1.98)
// now graph those data
twoway ///
(bar mean bar_order) ///
(rcap sd198low sd198high bar_order, vert lcolor(black)) ///
, ///
yla(0(10)50) ///
xla(1 "Domestic" 3 "Foreign") ///
ytitle("MPG")

Let’s make things a bit more fancy with labels, and place a text box with the mean and SD immediately above/north of the bars, which themselves are means of MPG by group. One quirk of Stata is that when formatting printed text from stored values, you need to place the label in an opening and closing tick, put a colon, type display, then apply the formatting (e.g., %3.1f for 1 digit after the decimal) then the value you are trying to show. You’ll notice that formatting in the last two lines of the following code.

// clear all frames
version 16
frames reset
sysuse auto, clear
// make a new frame called "bardata" that will store the values for bars.
frame create bardata str20 rowname n mean low95 high95 sd median iqrlow iqrhigh bar_order
//
// now grab summary statisticis for foreign==0
// grab 2.5th and 95th %ile and save as local macros
_pctile mpg if foreign==0, percentiles(2.5 97.5)
local low95 = r(r1)
local high95= r(r2)
// now sum with detail one row
sum mpg if foreign==0, d
// post these to the new frame/bardata
// note the final row, which will be the x axis value that will correspond with bar placement
frame post bardata ("Foreign==0") (r(N)) (r(mean)) (`low95') (`high95') (r(sd)) (r(p50)) (r(p25)) (r(p75)) (1)
//
// now repeat for foreign==1
_pctile mpg if foreign==1, percentiles(2.5 97.5)
local low95 = r(r1)
local high95= r(r2)
sum mpg if foreign==1, d
// note the final row is now 3, leaving a gap of between the prior row
frame post bardata ("Foreign==1") (r(N)) (r(mean)) (`low95') (`high95') (r(sd)) (r(p50)) (r(p25)) (r(p75)) (3)
// now change to the frame with the summary statistics
cwf bardata
// let's make a new variable that's the mean +/- 1.98*sd
gen sd198low = mean-(sd*1.98)
gen sd198high = mean+(sd*1.98)
// let's grab the mean and SD for each group. This frame has only two
// rows of data, so we'll use the row notatition to generate a local from
// the first and second rows, with row number specified in brackets
local group0_mean = mean[1]
local group0_sd =sd[1]
local group0_n = n[1]
// print to check your work
di "for group 0, mean =" `group0_mean' ", SD=" `group0_sd' ", n=" `group0_n'
// do the other group
local group1_mean = mean[2]
local group1_sd =sd[2]
local group1_n = n[2]
// print to check your work
di "for group 1, mean =" `group1_mean' ", SD=" `group1_sd' ", n=" `group1_n'
// now graph those data
// we'll add labels for the mean and SD and also
// label the N in the xlabels
twoway ///
(bar mean bar_order) ///
(rcap sd198low sd198high bar_order, vert lcolor(black)) ///
, ///
yla(0(10)50) ///
xla(1 "Domestic, n=`group0_n'" 3 "Foreign, n=`group1_n'") ///
ytitle("MPG") ///
text(`group0_mean' 1 "Mean (SD)= `: display %3.1f `group0_mean'' (`: display %3.1f `group0_sd'')", placement(n)) ///
text(`group1_mean' 3 "Mean (SD)= `: display %3.1f `group1_mean'' (`: display %3.1f `group1_sd'')", placement(n))

I recently made these two figures with a Stata do file that (A) generates some fake data, (B) plots the data overall with a fitted line, (C) plots each participant individually with fitted line, and (D) merges these four individual plots into one overall plot. One tricky piece of this was to get the –graph combine– command to get the four figures to be square, I had to fiddle with the –ysize– to get them to be square. I had originally tried using the –aspect– option, but it builds in lots of white space to between the figures. this way seemed to work.

Here’s the Stata code to make the above figures.

************************
*****clear data/graphs**
****set graph schemes***
************************
clear
graph drop _all
set scheme s1mono
************************
*****generate data******
************************
set seed 8675309
// generate 40 random values
set obs 40
//...called x and y that range from - to +9
gen y = runiform(-9,9)
gen x = runiform(-9,9)
// now make an indicator for each grouping of 10
gen n =.
replace n= 1 if _n>=0 & _n<=10

replace n= 2 if _n>10 & _n<=20

replace n= 3 if _n>20 & _n<=30

replace n= 4 if _n>30 & _n<=40

// now for each successive 10 rows, add 100, 120, 140, and 160
replace y = y+100 if n==1
replace x = x+100 if n==1
replace y = y+120 if n==2
replace x= x+120 if n==2
replace y= y+140 if n==3
replace x= x+140 if n==3
replace y= y+160 if n==4
replace x= x+160 if n==4

************************
*****single figure******
************************
twoway ///
/// line of unity (45 degree line):
(function y = x, ra(90 170) clpat(solid) clwidth(medium) clcolor(black)) ///
/// line of fit:
(lfit y x, lcolor(red) lpattern(dash) lwidth(thick)) ///
/// dots for each group, using colors from colorbrewer2
(scatter y x if n==1, mcolor("230 97 1") msymbol(O) mlcolor(black)) ///
(scatter y x if n==2, mcolor("253 184 99") msymbol(O) mlcolor(black)) ///
(scatter y x if n==3, mcolor("178 171 210") msymbol(O) mlcolor(black)) ///
(scatter y x if n==4, mcolor("94 60 153") msymbol(O) mlcolor(black)) ///
, ///
/// force size:
ysize(4) xsize(4) ///
/// axis titles and rows:
xtitle("Reference BP, mm Hg") ///
ytitle("Cuffless BP, mm Hg") ///
xla(90(20)170) ///
yla(90(20)170) ///
legend(off) ///
/// labels:
text(102 109 "←Participant A", placement(e)) ///
text(119 127 "←Participant B", placement(e)) ///
text(142 135 "Participant C→", placement(w)) ///
text(160 154 "Participant D→", placement(w))
/// export figure:
graph export figure1av2.png, replace width(1000)
************************
***four-piece figures***
*****to merge***********
************************
twoway ///
(function y = x if n==1, ra(90 110) clpat(solid) clwidth(medium) clcolor(black)) ///
(lfit y x if n==1, lcolor(red) lpattern(dash) lwidth(thick)) ///
(scatter y x if n==1, mcolor("230 97 1") msymbol(O) mlcolor(black)) ///
, ///
title("Participant A") ///
xtitle(" ") ///
ytitle(" ") ///
yla(90(5)110) ///
xla(90(5)110) ///
legend(off) ///
name(figure1b1)
twoway ///
(function y = x if n==2, ra(110 130) clpat(solid) clwidth(medium) clcolor(black)) ///
(lfit y x if n==2, lcolor(red) lpattern(dash) lwidth(thick)) ///
(scatter y x if n==2, mcolor("253 184 99") msymbol(O) mlcolor(black)) ///
, ///
title("Participant B") ///
xtitle(" ") ///
ytitle(" ") ///
yla(110(5)130) ///
xla(110(5)130) ///
legend(off) ///
name(figure1b2)
twoway ///
(function y = x if n==3, ra(130 150) clpat(solid) clwidth(medium) clcolor(black)) ///
(lfit y x if n==3, lcolor(red) lpattern(dash) lwidth(thick)) ///
(scatter y x if n==3, mcolor("178 171 210") msymbol(O) mlcolor(black)) ///
, ///
title("Participant C") ///
xtitle(" ") ///
ytitle(" ") ///
yla(130(5)150) ///
xla(130(5)150) ///
legend(off) ///
name(figure1b3)
twoway ///
(function y = x if n==4, ra(150 170) clpat(solid) clwidth(medium) clcolor(black)) ///
(lfit y x if n==4, lcolor(red) lpattern(dash) lwidth(thick)) ///
(scatter y x if n==4, mcolor("94 60 153") msymbol(O) mlcolor(black)) ///
, ///
title("Participant D") ///
xtitle(" ") ///
ytitle(" ") ///
yla(150(5)170) ///
xla(150(5)170) ///
legend(off) ///
name(figure1b4)
***********************
***combine figures*****
***********************
graph combine figure1b1 figure1b2 figure1b3 figure1b4 /*figure1b5*/, ///
b1title("Reference BP, mm Hg") ///
l1title("Cuffless BP, mm Hg") ///
/// this forces the four scatterplots to be the same height/width
/// details here:
/// https://www.stata.com/statalist/archive/2013-07/msg00842.html
cols(2) iscale(.7273) ysize(5.9) graphregion(margin(zero))
/// export to figure
graph export figure1bv2.png, replace width(1000)

Stata 16 introduced the new Frames functionality, which allows multiple datasets to be stored in memory, with each dataset stored in its own “Frame”. This allows for dynamic manipulation of multiple datasets across multiple Frames. Stata is still simplest to use when manipulating a single dataset (or, frame). So, Stata users will probably be interested in building a single dataset/Frame for a specific analysis that is built from variables taken from multiple datasets/Frames.

One handy application of Frames is to import non-Stata datasets as separate frames and combine them (really, merge) into a single analytical dataset/Frame. Before using Frames, I had previously imported non-Stata datasets, saved them locally, then merged them 1 by 1. With Frames, you just import each dataset into its own Frame, and “merge” them directly, skipping the intermediate “save as Stata dta file” step.

Here’s my approach to building a single analytical dataset from multiple imported datasets, with frames. We’ll do this with NHANES data. This is a modification of the code on this post.

Step 1: Drop (reset) all frames, create new ones to import the new datasets, and run commands within each new frame to import NHANES/SAS datasets.

// Here's the NHANES website, FYI:
// https://wwwn.cdc.gov/nchs/nhanes/default.aspx
//
// drop all frames from memory. This will delete all unsaved data so be careful!!
frames reset
//
// make a blank frame called "DEMO_F"
// you could type "frame create" or the brief synonym "mkf" for
// "make frame"
mkf DEMO_F
// ...Then run the sas import command within it, grabbing it from the CDC website.
// Since this command needs to be run from within the DEMO_F frame,
// we can tell Stata to run the command from that frame without
// actually changing to it using the "frame [name]:" prefix
frame DEMO_F: import sasxport5 "https://wwwn.cdc.gov/Nchs/Nhanes/2009-2010/DEMO_F.XPT", clear
//
// ditto for the "BPQ_F" and "KIQ_U_F" datasets.
mkf BPQ_F
frame BPQ_F: import sasxport5 "https://wwwn.cdc.gov/Nchs/Nhanes/2009-2010/BPQ_F.XPT", clear
//
mkf KIQ_U_F
frame KIQ_U_F: import sasxport5 "https://wwwn.cdc.gov/Nchs/Nhanes/2009-2010/KIQ_U_F.XPT", clear
//
// let's see a list of current frames:
frames dir
//
// Which frame are you using though?
// pwf is present working frame, or the current one in use.
pwf
// You'll see that the pwf if "default".

Step 2: Create an “analytical” frame that will contain the data you need to complete your analysis, and copy the variable that links all of your data to this frame. Also switch to that new analytical frame.

// The "linking" variable in this dataset is called "seqn", which
// we will copy ("put") from the DEMO_F frame.
// (Your file might have a linking variable called "id".)
// This creates a new frame called "analytical" and also moves the "seqn"
// variable from DEMO_F to the new analytical frame in one line.
frame DEMO_F: frame put seqn, into(analytical)
//
// see current list of frames and present working frame:
frames dir
pwf
// Now change from the default frame to the new analytical one.
// You can change frames with "cwf" for "change working frame".
cwf analytical

Step 3: Now that you are are in the new analytical frame, link all of your frames using the “linking” variable (“seqn” here).

// use the "frlink" command to link frames. This can be 1:1 linking or 1:m.
//
// remember that your cwf should be analytical right now.
//
frlink 1:1 seqn, frame(DEMO_F)
frlink 1:1 seqn, frame(BPQ_F)
frlink 1:1 seqn, frame(KIQ_U_F)
//
// You are still within the "analytical" frame, but now your frames are all
// linked or connected to each other.
// if you look at your dataset with the --browse-- command, you'll see there
// are now new DEMO_F, BPQ_F, and KIQ_U_F variables. These are the "rows" for
// linked IDs in those other frames, so Stata knows where to look for
// variables in those other frames.

Step 4: “Get” the specific variables you want from each frame. This is how you merge individual variables from multiple frames.

// from my NHANES post, we need to grab weighting variables from DEMO_F
// and a BP variable from BPQ_F. Just for kicks, we'll also grab self-
// reported "weak kidneys" from KIQ_U_F.
//
// remember that your cwf should be analytical right now.
//
frget wtint2yr wtmec2yr sdmvpsu sdmvstra, from(DEMO_F)
frget bpq020, from(BPQ_F)
frget kiq022, from(KIQ_U_F)
//
// now you have a nice merged database!

Step 5 (optional): If you are satisfied with your analytical dataset and no longer need the other frames, you can now drop the “linking” variables from the analytical dataset, save your analytical dataset, clear your frames, and reopen your analytical dataset

// You can save this analytical frame as a Stata
// dataset now if you are done manipulating the other frames.
//
// You might opt to drop the new linking variables prior to saving
// for simplicity.
drop DEMO_F BPQ_F KIQ_U_F
//
// Stata's "save" command won't save other frames as FYI, just the pwf.
// But in this example, we are done with other frames.
save analytical.dta, replace
//
// Drop all other frames so you don't get an annoying pop-up
// about unsaved frames in memory.
// Be careful! This will drop all data from memory!!
frames reset
//
// now reopen your previously saved analytical dataset.
use analytical.dta, clear

Bonus: Appending frames

You might need to append frames. There are some details here about how to do this. I’m using the –fframeappend– command by Jürgen Wiemers.

// install fframeappend, only need to do once:
ssc install fframeappend
// change to the frame you want to append all of your data to,
// in this example it's called "appended"
// you'll have to use "mkf appended" if you don't already have one
// called that.
cwf appended
//now append your frame "a" to the current open frame
fframeappend, using(a)
// now repeat using frame "b" to the current open frame
fframeappend, using(b)

Bonus: Here’s steps 1-4 in a single loop using global macros

For advanced users: Here’s a loop and some global macros that is adaptable to downloading several years. NHANES uses different letters for files from different years, the 2009-2010 one uses “F”.

frames reset
global url "https://wwwn.cdc.gov/Nchs/Nhanes/"
global F "2009-2010/"
global files DEMO BPQ KIQ_U
foreach x in F {
foreach y in $files {
mkf `y'_`x'
frame `y'_`x': import sasxport5 "${url}${`x'}`y'_`x'.xpt"
}
frame DEMO_`x': frame put seqn, into(analytical_`x')
cwf analytical_`x'
foreach y in $files {
frlink 1:1 seqn, frame(`y'_`x')
}
frget wtint2yr wtmec2yr sdmvpsu sdmvstra, from(DEMO_`x')
frget bpq020, from(BPQ_`x')
frget kiq022, from(KIQ_U_`x')
}
frames dir
pwf

Here’s the same as above, but just for a single year.

frames reset
global dir "https://wwwn.cdc.gov/Nchs/Nhanes/2009-2010/"
global files DEMO_F BPQ_F KIQ_U_F
foreach y in $files {
mkf `y'
frame `y': import sasxport5 "${dir}`y'.xpt"
}
frame DEMO_F: frame put seqn, into(analytical)
cwf analytical
foreach y in $files {
frlink 1:1 seqn, frame(`y')
}
frget wtint2yr wtmec2yr sdmvpsu sdmvstra, from(DEMO_F)
frget bpq020, from(BPQ_F)
frget kiq022, from(KIQ_U_F)
frames dir
pwf

Mediation is a commonly-used tool in epidemiology. Inverse odds ratio-weighted (IORW) mediation was described in 2013 by Eric J. Tchetgen Tchetgen in this publication. It’s a robust mediation technique that can be used in many sorts of analyses, including logistic regression, modified Poisson regression, etc. It is also considered valid if there is an observed exposure*mediator interaction on the outcome.

There have been a handful of publications that describe the implementation of IORW (and its cousin inverse odds weighting, or IOW) in Stata, including this 2015 AJE paper by Quynh C Nguyen and this 2019 BMJ open paper by Muhammad Zakir Hossin (see the supplements of each for actual code). I recently had to implement this in a REGARDS project using a binary mediation variable and wanted to post my code here to simplify things. Check out the Nguyen paper above if you need to modify the following code to run IOW instead of IOWR, or if you are using a continuous mediation variable, rather than a binary one.

A huge thank you to Charlie Nicoli MD, Leann Long PhD, and Boyi Guo (almost) PhD who helped clarify implementation pieces. Also, Charlie wrote about 90% of this code so it’s really his work. I mostly cleaned it up, and clarified the approach as integrated in the examples from Drs. Nguyen and Hossin from the papers above.

IORW using pweight data (see below for unweighted version)

The particular analysis I was running uses pweighting. This code won’t work in data that doesn’t use weighting. This uses modified Poisson regression implemented as GLMs. These can be swapped out for other models as needed. You will have to modify this script if you are using 1. a continuous exposure, 2. more than 1 mediator, 3. a different weighting scheme, or 4. IOW instead of IORW. See the supplement in the above Nguyen paper on how to modify this code for those changes.

*************************************************
// this HEADER is all you should have to change
// to get this to run as weighted data with binary
// exposure using IORW. (Although you'll probably
// have to adjust the svyset commands in the
// program below to work in your dataset, in all
// actuality)
*************************************************
// BEGIN HEADER
//
// Directory of your dataset. You might need to
// include the entire file location (eg "c:/user/ ...")
// My file is located in my working directory so I just list
// a name here. Alternatively, can put URL for public datasets.
global file "myfile.dta"
//
// Pick a title for the table that will output at the end.
// This is just to help you stay organized if you are running
// a few of these in a row.
global title "my cool analysis, model 1"
//
// Components of the regression model. Outcome is binary,
// the exposure (sometimes called "dependent variable" or
// "treatment") is also binary. This code would need to be modified
// for a continuous exposure variable. See details in Step 2.
global outcome mi_incident
global exposure smoking
global covariates age sex
global mediator c.biomarker
global ifstatement if mi_prevalent==0 & biomarker<. & mi_incident <.
//
// Components of weighting to go into "svyset" command.
// You might have
global samplingweight mysamplingweightvar
global id id_num // ID for your participants
global strata mystratavar
//
// Now pick number of bootstraps. Aim for 1000 when you are actually
// running this, but when debugging, start with 50.
global bootcount 50
// and set a seed.
global seed 8675309
// END HEADER
*************************************************
//
//
//
// Load your dataset.
use "${file}", clear
//
// Drop then make a program.
capture program drop iorw_weighted
program iorw_weighted, rclass
// drop all variables that will be generated below.
capture drop predprob
capture drop inverseoddsratio
capture drop weight_iorw
//
*Step 1: svyset data since your dataset is weighted. If your dataset
// does NOT require weighting for its analysis, do not use this program.
svyset ${id}, strata(${strata}) weight(${samplingweight}) vce(linearized) singleunit(certainty)
//
*Step 2: Run the exposure model, which regresses the exposure on the
// mediator & covariates. In this example, the exposure is binary so we are
// using logistic regression (logit). If the exposure is a normally-distributed
// continuous variable, use linear regression instead.
svy linearized, subpop(${ifstatement}): logit ${exposure} ${mediator} ${covariates}
//
// Now grab the beta coefficient for mediator. We'll need that to generate
// the IORW weights.
scalar med_beta=e(b)[1,1]
//
*Step 3: Generate predicted probability and create inverse odds ratio and its
// weight.
predict predprob, p
gen inverseoddsratio = 1/(exp(med_beta*${mediator}))
//
// Calculate inverse odds ratio weights. Since our dataset uses sampling
// weights, we need to multiply the dataset's weights times the IORW for the
// exposure group. This step is fundamentally different for non-weighted
// datasets.
// Also note that this is for binary exposures, need to revise
// this code for continuous exposures.
gen weight_iorw = ${samplingweight} if ${exposure}==0
replace weight_iorw = inverseoddsratio*${samplingweight} if ${exposure}==1
//
*Step 4: TOTAL EFFECTS (ie no mediator) without IORW weighting yet.
// (same as direct effect, but without the IORW)
svyset ${id}, strata(${strata}) weight(${samplingweight}) vce(linearized) singleunit(certainty)
svy linearized, subpop(${ifstatement}): glm ${outcome} ${exposure} ${covariates}, family(poisson) link(log)
matrix bb_total= e(b)
scalar b_total=bb_total[1,1]
return scalar b_total=bb_total[1,1]
//
*Step 5: DIRECT EFFECTS; using IORW weights instead of the weighting that
// is used typically in your analysis.
svyset ${id}, strata(${strata}) weight(weight_iorw) vce(linearized) singleunit(certainty)
svy linearized, subpop(${ifstatement}): glm ${outcome} ${exposure} ${covariates}, family(poisson) link(log)
matrix bb_direct=e(b)
scalar b_direct=bb_direct[1,1]
return scalar b_direct=bb_direct[1,1]
//
*Step 6: INDIRECT EFFECTS
// indirect effect = total effect - direct effects
scalar b_indirect=b_total-b_direct
return scalar b_indirect=b_total-b_direct
//scalar expb_indirect=exp(scalar(b_indirect))
//return scalar expb_indirect=exp(scalar(b_indirect))
//
*Step 7: calculate % mediation
scalar define percmed = ((b_total-b_direct)/b_total)*100
// since indirect is total minus direct, above is the same as saying:
// scalar define percmed = (b_indirect)/(b_total)*100
return scalar percmed = ((b_total-b_direct)/b_total)*100
//
// now end the program.
end
//
*Step 8: Now run the above bootstraping program
bootstrap r(b_total) r(b_direct) r(b_indirect) r(percmed), seed(${seed}) reps(${bootcount}): iorw_weighted
matrix rtablebeta=r(table) // this is the beta coefficients
matrix rtableci=e(ci_percentile) // this is the 95% confidence intervals using the "percentiles" (ie 2.5th and 97.5th percentiles) rather than the default normal distribution method in stata. The rational for using percentiles rather than normal distribution is found in the 4th paragraph of the "analyses" section here (by refs 37 & 38): https://bmjopen.bmj.com/content/9/6/e026258.long
//
// Just in case you are curious, here are the the ranges of the 95% CI,
// realize that _bs_1 through _bs_3 need to be exponentiated:
estat bootstrap, all // percentiles is "(P)", normal is "(N)"
//
// Here's a script that will display your beta coefficients in
// a clean manner, exponentiating them when required.
quietly {
noisily di "${title} (bootstrap count=" e(N_reps) ")*"
noisily di _col(15) "Beta" _col(25) "95% low" _col(35) "95% high" _col(50) "Together"
local beta1 = exp(rtablebeta[1,1])
local low951 = exp(rtableci[1,1])
local high951 = exp(rtableci[2,1])
noisily di "Total" _col(15) %4.2f `beta1' _col(25) %4.2f `low951' _col(35) %4.2f `high951' _col(50) %4.2f `beta1' " (" %4.2f `low951' ", " %4.2f `high951' ")"
local beta2 = exp(rtablebeta[1,2])
local low952 = exp(rtableci[1,2])
local high952 = exp(rtableci[2,2])
noisily di "Direct" _col(15) %4.2f `beta2' _col(25) %4.2f `low952' _col(35) %4.2f `high952' _col(50) %4.2f `beta2' " (" %4.2f `low952' ", " %4.2f `high952' ")"
local beta3 = exp(rtablebeta[1,3])
local low953 = exp(rtableci[1,3])
local high953 = exp(rtableci[2,3])
noisily di "Indirect" _col(15) %4.2f `beta3' _col(25) %4.2f `low953' _col(35) %4.2f `high953' _col(50) %4.2f `beta3' " (" %4.2f `low953' ", " %4.2f `high953' ")"
local beta4 = (rtablebeta[1,4])
local low954 = (rtableci[1,4])
local high954 = (rtableci[2,4])
noisily di "% mediation" _col(15) %4.2f `beta4' "%" _col(25) %4.2f `low954' "%"_col(35) %4.2f `high954' "%" _col(50) %4.2f `beta4' "% (" %4.2f `low954' "%, " %4.2f `high954' "%)"
noisily di "*Confidence intervals use 2.5th and 97.5th percentiles"
noisily di " rather than default normal distribution in Stata."
noisily di " "
}
// the end.

IORW for datasets that don’t use weighting (probably the one you are looking for)

Here is the code above, except without consideration of weighting in the overall dataset. (Obviously, IORW uses weighting itself.) This uses modified Poisson regression implemented as GLMs. These can be swapped out for other models as needed. You will have to modify this script if you are using 1. a continuous exposure, 2. more than 1 mediator, 3. a different weighting scheme, or 4. IOW instead of IORW. See the supplement in the above Nguyen paper on how to modify this code for those changes.

*************************************************
// this HEADER is all you should have to change
// to get this to run as weighted data with binary
// exposure using IORW.
*************************************************
// BEGIN HEADER
//
// Directory of your dataset. You might need to
// include the entire file location (eg "c:/user/ ...")
// My file is located in my working directory so I just list
// a name here. Alternatively, can put URL for public datasets.
global file "myfile.dta"
//
// Pick a title for the table that will output at the end.
// This is just to help you stay organized if you are running
// a few of these in a row.
global title "my cool analysis, model 1"
// Components of the regression model. Outcome is binary,
// the exposure (sometimes called "dependent variable" or
// "treatment") is also binary. This code would need to be modified
// for a continuous exposure variable. See details in Step 1.
global outcome mi_incident
global exposure smoking
global covariates age sex
global mediator c.biomarker
global ifstatement if mi_prevalent==0 & biomarker<. & mi_incident <.
//
// Now pick number of bootstraps. Aim for 1000 when you are actually
// running this, but when debugging, start with 50.
global bootcount 50
// and set a seed.
global seed 8675309
// END HEADER
*************************************************
//
//
//
// Load your dataset.
use "${file}", clear
//
// Drop then make a program.
capture program drop iorw
program iorw, rclass
// drop all variables that will be generated below.
capture drop predprob
capture drop inverseoddsratio
capture drop weight_iorw
//
//
*Step 1: Run the exposure model, which regresses the exposure on the
// mediator & covariates. In this example, the exposure is binary so we are
// using logistic regression (logit). If the exposure is a normally-distributed
// continuous variable, use linear regression instead.
logit ${exposure} ${mediator} ${covariates} ${ifstatement}
//
// Now grab the beta coefficient for mediator. We'll need that to generate
// the IORW weights.
scalar med_beta=e(b)[1,1]
//
*Step 2: Generate predicted probability and create inverse odds ratio and its
// weight.
predict predprob, p
gen inverseoddsratio = 1/(exp(med_beta*${mediator}))
//
// Calculate inverse odds ratio weights.
// Also note that this is for binary exposures, need to revise
// this code for continuous exposures.
gen weight_iorw = 1 if ${exposure}==0
replace weight_iorw = inverseoddsratio if ${exposure}==1
//
*Step 3: TOTAL EFFECTS (ie no mediator) without IORW weighting yet. (same as direct effect, but without the IORW)
glm ${outcome} ${exposure} ${covariates} ${ifstatement}, family(poisson) link(log) vce(robust)
matrix bb_total= e(b)
scalar b_total=bb_total[1,1]
return scalar b_total=bb_total[1,1]
//
*Step 4: DIRECT EFFECTS; using IORW weights
glm ${outcome} ${exposure} ${covariates} ${ifstatement} [pweight=weight_iorw], family(poisson) link(log) vce(robust)
matrix bb_direct=e(b)
scalar b_direct=bb_direct[1,1]
return scalar b_direct=bb_direct[1,1]
//
*Step 5: INDIRECT EFFECTS
// indirect effect = total effect - direct effects
scalar b_indirect=b_total-b_direct
return scalar b_indirect=b_total-b_direct
//scalar expb_indirect=exp(scalar(b_indirect))
//return scalar expb_indirect=exp(scalar(b_indirect))
//
*Step 6: calculate % mediation
scalar define percmed = ((b_total-b_direct)/b_total)*100
// since indirect is total minus direct, above is the same as saying:
// scalar define percmed = (b_indirect)/(b_total)*100
return scalar percmed = ((b_total-b_direct)/b_total)*100
//
// now end the program.
end
//
*Step 7: Now run the above bootstraping program
bootstrap r(b_total) r(b_direct) r(b_indirect) r(percmed), seed(${seed}) reps(${bootcount}): iorw
matrix rtablebeta=r(table) // this is the beta coefficients
matrix rtableci=e(ci_percentile) // this is the 95% confidence intervals using the "percentiles" (ie 2.5th and 97.5th percentiles) rather than the default normal distribution method in stata. The rational for using percentiles rather than normal distribution is found in the 4th paragraph of the "analyses" section here (by refs 37 & 38): https://bmjopen.bmj.com/content/9/6/e026258.long
//
// Just in case you are curious, here are the the ranges of the 95% CI,
// realize that _bs_1 through _bs_3 need to be exponentiated:
estat bootstrap, all // percentiles is "(P)", normal is "(N)"
//
// Here's a script that will display your beta coefficients in
// a clean manner, exponentiating them when required.
quietly {
noisily di "${title} (bootstrap count=" e(N_reps) ")*"
noisily di _col(15) "Beta" _col(25) "95% low" _col(35) "95% high" _col(50) "Together"
local beta1 = exp(rtablebeta[1,1])
local low951 = exp(rtableci[1,1])
local high951 = exp(rtableci[2,1])
noisily di "Total" _col(15) %4.2f `beta1' _col(25) %4.2f `low951' _col(35) %4.2f `high951' _col(50) %4.2f `beta1' " (" %4.2f `low951' ", " %4.2f `high951' ")"
local beta2 = exp(rtablebeta[1,2])
local low952 = exp(rtableci[1,2])
local high952 = exp(rtableci[2,2])
noisily di "Direct" _col(15) %4.2f `beta2' _col(25) %4.2f `low952' _col(35) %4.2f `high952' _col(50) %4.2f `beta2' " (" %4.2f `low952' ", " %4.2f `high952' ")"
local beta3 = exp(rtablebeta[1,3])
local low953 = exp(rtableci[1,3])
local high953 = exp(rtableci[2,3])
noisily di "Indirect" _col(15) %4.2f `beta3' _col(25) %4.2f `low953' _col(35) %4.2f `high953' _col(50) %4.2f `beta3' " (" %4.2f `low953' ", " %4.2f `high953' ")"
local beta4 = (rtablebeta[1,4])
local low954 = (rtableci[1,4])
local high954 = (rtableci[2,4])
noisily di "% mediation" _col(15) %4.2f `beta4' "%" _col(25) %4.2f `low954' "%"_col(35) %4.2f `high954' "%" _col(50) %4.2f `beta4' " (" %4.2f `low954' ", " %4.2f `high954' ")"
noisily di "*Confidence intervals use 2.5th and 97.5th percentiles"
noisily di " rather than default normal distribution in Stata."
noisily di " "
}
// the end.