Note: this post was written prior to Stata 17, which now allows Python to control Stata and vice versa. In Stata 16, Python could not control Stata but Stata could control Python. Because of this functionality, there’s a more streamlined approach to getting Jupyter to play nicely with Stata 17, detailed here. You’ll still need to install Python and Jupyter, so this post is still helpful.

Stata 16 now integrates with Python. I’m pretty stoked about using some of the Python figure packages. Getting it up and running has been a bit of a challenge. Here’s how I got it to work.

Of note, since I started this post, Stata’s blog has started a series on using Python, which you should check out here.

Install part 1:

Installing Anaconda (free for individuals, not for institutions)

Anaconda comes with many built-in statistical packages. The (free) individual version of Anaconda from here. Just make sure to check the “set as path” button during the Anaconda install!

Installing the (universally free) traditional Python distribution

As of July 2020, Python apparently has two versions that are commonly used, the 2.x version and the 3.x version. The end-of-life of 2.x versions is this year, so I wouldn’t recommend using it (current highest version is 2.7). Instead, use the 3.x version, currently the 3.8 version. You can find it at the Windows Python Download Page.

Make sure to install the version matching your Stata install! Stata comes as 32 bit or 64 bit. In Stata, type –about– to see what version you have. You’ll see that mine is running the 64-bit version of Stata. If you have a relatively modern computer, you are probably running the 64-bit version of Stata. Windows can actually run either 32-bit or 64-bit versions if you have a 64-bit processor, so do yourself a favor and just check. Type -about- in Stata to confirm your version.

Make sure that you install the corresponding version of Python. The highlighted one (x86-64) is the 64 bit. The other one (x86) is the 32-bit version. For this example, since I have the 64-bit version of stata, I installed the x86-64, 64-bit version of Python.

I had originally installed the 32-bit version of Python and Stata couldn’t load it. Installing the 64-bit version of Python solved that. There’s actually a big “download now” button on the main Python webpage that will give you the 32-bit version. Make sure to select the specific stable release in the picture above.

For the love of Pete, check this PATH box when you install it.

PATH is a list of commands that can be run from the Windows command line, and where their relative program exists.

See this check box right here? Select it. If you don’t, you’ll have a heck of a time getting anything to run from the command line. This should be checked on default, I have no idea why it’s not. If you forgot to check this box, uninstall Python and reinstall it after checking this box.

Also, notice that it says “64 bit” on the installer screen above. If it says “32 bit”, you probably downloaded the wrong version. Go back and try again!

What the heck did I just install?

There are two Python shell apps/programs that came along with the default Python setup. IDLE is a more user-friendly Python shell. It resembles the command line in Stata, but it has the syntax highlighting of the Do file editor.

The app called “Python 3.8 (64-bit)” is the shell without any markup. If you want to play around with Python, I recommend using IDLE.

Making your first program in IDLE

Anything you run in Python should be from a script, or a *.py file. Pop one open from within IDLE by hitting Ctrl+N. Enter the following:

print("Hello world!")

Then save it and run it (by pressing F5) and you’ll get the hello world!

How does that look in Stata?

Let’s do the same thing in a Stata do file. In order to open up the Python shell within Stata, you have to type –python– on its own line, your intended python code, then –end– on its own line. Here, I have entered:

python
print("hello world!")
end

Then just hit ctrl+d or the run button to get it to work in Stata!

Install Part 2: How do I get the Pandas, Matplotlib, SciPy, Sklearn, and NumPy libraries installed in Python?

Note: Anaconda comes with all of these except sklearn. For below, just complete the sklearn step.

Python by itself can do some stuff, but the heavy lifting for stats and visualization is from add-in libraries that aren’t included with the default Python and must be added in before doing much of anything else. (Note: Anaconda does come with those and is a Python installation geared towards science, but we’re doing the classic install here.) Installing these additional libraries can be done with the included pip library, which automates all downloads and installations. BUT pip it has to be called from the Windows command line, not in a Python shell (i.e., not in IDLE). You’ll know you’re in the shell if the line starts with this:

>>>

So if you type “pip install pandas” in the shell (after the “>>>”), you’ll get an error and you will not be getting pandas.

To pop up the Windows command line, hit the Start button then type “cmd” to open it up. Or hit windows key+r and type “cmd” to open it up. If you correctly checked the PATH checkbox in the install, you should get the version reported if you type the following in:

python --version

If you get some sort of error, it’s probably because you didn’t check the PATH box during the install. Uninstall Python then reinstall it and make for sure you check that stupid PATH box.

A note about the Windows 10 command line: If you type “Python” and hit enter, Windows pops up the Windows store and tries to get you to install the version of Python that they host. This is by far the dumbest Windows feature ever, and I have seen BOB. So, avoid ever typing the word “python” in the command line. Instead, use the handy “py” command, which does everything you’ll need it to do. Py is the python launcher.

To call pip, you want to type in “py” then “-m” then “pip” and its commands. the “-m” allows you to run library commands as a script. If it’s been a while since you updated pip, now’s a good time to update it in the command line to show how to call pip:

py -m pip install --upgrade pip

Now let’s install the core packages for Python:

py -m pip install pandas
py -m pip install matplotlib
py -m pip install numpy
py -m pip install scipy
py -m pip install sklearn

You’ll see a screen like this, and it’ll let

You only need to do this installation step once.

How do I use Pandas, Matplotlib, NumPy, Scikit-learn (sklearn), and SciPy in Stata?

Once the libraries are installed, you can then integrate them into your scripts. Each time you want to use them, you need to import them so you can call them. The convention is to import these using common so you don’t have to type “pandas” over and over again, you can just use “pd”. Ditto for other libraries:

python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import sklearn as sk
end

Install part 3: How do I install Jupyter Lab and get it to work with Stata? (This is the updated version of Jupyter Notebook)

Note: Jupyter lab comes installed with Anaconda, but node.js, npm, and stata_kernel still must be installed.

Jupyter is a super popular way to cleanly complete analyses. Its origins were in Python, but it now works in R and Stata. Details on installing Jupyter are here. Specific instructions for getting it to interface with Stata are here. Here’s how I got it installed:

First: install node.js (warning: this takes a long time and requires a reboot)

Make sure that you have the current version of Python installed! Node.js will install the current version and if your version is outdated, it’ll create a huge headache.

First, install node.js. Download here. I checked the box in the node.js install to also install additional software. At the end of the install, it pops open Windows powershell window and installed a bunch of stuff, including Python if you do not have the current version. It also installs Chocolatey and a few other things. NOW STOP HERE AND REBOOT YOUR COMPUTER. Come back when it restarts.

Next: install other npm, jupyterlab, and Stata plugins (warning: this also takes a while)

First, it’s always good habit to make sure you have the newest version of pip!

py -m pip install --upgrade pip

Then, type the following in the Windows command line:

py -m pip install npm
py -m pip install jupyterlab
py -m pip install stata_kernel
py -m stata_kernel.install
jupyter labextension install jupyterlab-stata-highlight

When later trying to run jupyter lab, I got an error saying “Exception: Jupyter command jupyter-lab not found.” I updated pip then uninstalled and reinstalled npm, jupyterlab, and stata_kernel (just replace the word “install” with “uninstall” above) then installed everything again. This fixed things.

Finally, you need to do a last step described under “windows specific steps” here to get this to work in Windows. I found my Stata executable at “C:\Program Files\Stata16\StataSE-64.exe”, not Program Files (x86). You can delete the new Stata desktop shortcut once you run it as an administrator one time.

You might also need to configure Jupyter to work with your Stata install. Details are here. I found the configuration file named .stata_kernel.conf sitting in this folder: C:\Users\MYUSERNAME\.stata_kernel.conf

In reviewing the configuration file, it seems to have correctly identified my Stata SE 16 setup. I changed the graph format from svg to png, but left the rest unchanged.

Now that I have Jupyter Lab installed, how do I open and use it?

In Anaconda Navigator, just click the “Jupyter lab” button. For a traditional Python install, open the Windows command line, type:

jupyter lab

It should open up your web browser to a Jupyter page. Keep the Windows Command line terminal open in the background. If you close it, Jupyter will cease to work. (Note: This isn’t true for anaconda if you open Jupyter from the GUI.) Click the “Stata” notebook button to start.

It’ll open up a scripting page for your code. At the bottom it should say “stata idle”. That’s how you know you set it up correctly.

Now you can use traditional Stata code!

There are additional programs called “Magics” detailed here that help Stata integrate more seamlessly with Jupyter. Each of these commands begins with a % symbol. There are specific ways to modify these commands.

• %browse – lists the first 200 rows
• %head – first 10 rows
• %tail – last 10 rows
• %set – can change the graph_format, graph_scale, graph_width, and graph_height

How do I use SFI to interface Stata and Python?

Stata and Python talk to each other using the Stata function interface, or SFI. MORE TO COME ON THIS.

I love restricted cubic splines, made famous by Frank Harrell (see his approach starting on page 58 here). Dr. Harrell made a package for automating these in R. I’m not aware of an equivalent package for Stata. Here’s my approach to making this specific restricted cubic spline in Stata.

The model here is modified Poisson regression using the Zou 2004 method since the outcome is binary. Since it’s coded as a GLM, it’ll be relatively easy to swap out this one specific model for other models, like logistic regression using the appropriate link & family. It’s good habit to have the probability density of the outcome across the continuum of exposure, so that is plopped on the bottom here. It wouldn’t take much work to replace with a histogram.

This is for two groups (group1=blue, group2=red). It wouldn’t be too hard to make this for just one group, deleting everything having to do with group 2 or having the number 2 in it. Or, duplicate lines for a group 3. Also, sometimes folks like to present a kernel density plot for each outcome, so you’d just duplicate the kdensity lines and add some code to specify that one of each is for folks with outcome==0 then outcome==1.

One quirk is that the xbrcspline code depends on a list of all of the possible options for the exposure, which is brought in from listof to xbrcspline as a numlist. As you can see in –help limits–, numlists are capped at 2,500 numbers. If you get an error saying “values() invalid — invalid numlist has too many elements”, then you have too many individual options for your exposure. This might be because your exposure is an integer with a huge amount of digits after the decimal place, so your >2,500 observations all come with their own unique value for the exposure. Rounding can help reduce this count, if it analytically and clinically makes sense. (This is okay for BMI, for example, because there isn’t clinically relevant difference between a bmi of 27.0400558235 and 27.04). To generate a rounded value, plop —gen bmi_round=(bmi, 0.01)– in the first few lines, right after opening your data. Then use “bmi_round” as your exposure variable rather than “bmi”.

You also should consider centering your exposure variables at the mean. To do this, use the –sum– command for each variable then –gen [variable’s name]_center = [variable’s name]/r(mean)–. Then use the “[variable’s name]_center” as exposures variables.

This code is a mostly rewritten version of one that is used at the Welch Center at Hopkins, where it has been handed down through generations of doctoral students and post-docs.

I’ve recently needed to make these figures using pweights, so I put some comments throughout to simplify that process.

Note: One glitch with WordPress (the blog that hosts this) is that it can’t render certain code below unless it’s the final line in a block of code, so I’ve had to chop it up this script, which should be a single block of code, into several blocks of code. If you copy and paste this into a do file and try to run it, you’ll notice some blank lines in the “Make a Figure” section that you’ll need to delete.

Enjoy!

Code to make the figure above

*************************************************
***************load data!************************
****************and drop any open graphs*********
*************************************************
webuse fvex, clear
graph drop _all
macro drop _all
// if weighted, declare weighting here
*************************************************
**************define variables here**************
*************************************************
// Define the OUTCOME, = to 0 or 1, after the (first) word "outcome"
global outcome outcome // this database's outcome is called outcome...
// Define the EXPOSURE following the word "exposure"
global exposure age // this has to be a continuous variable
// Define the COVARIATES following word "covariate"
global covariates sex distance
// Define what makes GROUP 1 here after "thing1" (blue)
global thing1 if arm==1 
// Define what makes GROUP 2 here after "thing2" (red)
global thing2 if arm==2

*************************************************
************auto-generate subgroups and**********
*****************local macros needed*************
********************for spline*******************
*************************************************
// need to make the exposure by group for the kernel density plots. 
gen exposuresubgroup1 = ${exposure} ${thing1}
gen exposuresubgroup2 = ${exposure} ${thing2}

// The xbrcspine command below needs the middle value of the exposure 
// for each group. This is often times the median, but if there are 
// an even number of values for the exposure, the median will be an 
// average of the two middle values of the exposure variable. The following 
// code defines the median as a local macro (median1temp) then grabs the
// actual value for the exposure closest to that number (median_1). 
// _pctile can be used with pweight, if needed.
//
_pctile exposuresubgroup1, p(50)
// _pctile exposuresubgroup1 [pweight=samplingweight], p(50)
local median1temp = r(r1)
gen mediandiff1=abs(exposuresubgroup1-`median1temp')
gsort mediandiff1
local median_1 = ${exposure}[_n==1]
//
_pctile exposuresubgroup2, p(50)
// _pctile exposuresubgroup2 [pweight=samplingweight], p(50)
local median2temp = r(r1)
gen mediandiff2=abs(exposuresubgroup2-`median2temp')
gsort mediandiff2
local median_2 = ${exposure}[_n==1]
//
// get rid of these variables, since they are not needed anymore
drop mediandiff1 mediandiff2

*************************************************
*************make splines and view knots!********
*************************************************
// NOTE: look at the stata output here, the knots will be displayed
// you'll need to list these in the footer
// 
// Harrell's method recommends using the # of knots ('k') as 3, 4 or 5
// with n >=100 as 5 and n<30 as 3. It's just a guideline. 
// Details are on page 58 here: 
// https://hbiostat.org/doc/rms.pdf
mkspline ${exposure}_spline1=exposuresubgroup1, nknots(4) cubic displayknots
mat knots1=r(knots)
mkspline ${exposure}_spline2=exposuresubgroup2, nknots(4) cubic displayknots
mat knots2=r(knots)
// above won't work for pweight weighted data. instead, you need 
// to specifically define the percentiles from the table on pg 5 here:
// https://www.stata.com/manuals13/rmkspline.pdf
// But basically, 
// 3 knots, percentiles are at: 10 50 90
// 4 knots, percentiles are at: 5 35 65 95
// 5 knots, percentiles are at: 5 27.5 50 72.5 95
// 6 knots, percentiles are at: 5 23 41 59 77 95
// 7 knots, percentiles are at: 2.5 18.33 34.17 50 65.83 81.67 97.5
// the code for 4 knots follows in hidden code:
//
//_pctile exposuresubgroup1 [pweight=samplingweight], p(5 35 65 95) 
//return list
//local gr1knot1 = r(r1)
//local gr1knot2 = r(r2)
//local gr1knot3 = r(r3)
//local gr1knot4 = r(r4)
//di "Knots for group 1 are at " `gr1knot1' ", " `gr1knot2' ", " ///
//`gr1knot3' ", " `gr1knot4' "."
//
//
//mkspline ${exposure}_spline1=exposuresubgroup1,  ///
//knots(`gr1knot1' `gr1knot2' `gr1knot3' `gr1knot4' ) ///
//cubic displayknots
//
//mat knots1=r(knots)
//
//_pctile exposuresubgroup2 [pweight=samplingweight], p(5 35 65 95) 
//return list
//local gr2knot1 = r(r1)
//local gr2knot2 = r(r2)
//local gr2knot3 = r(r3)
//local gr2knot4 = r(r4)
//
//di "Knots for group 2 are at " `gr2knot1' ", " `gr2knot2' ", " ///
// `gr2knot3' ", " `gr2knot4' "."
//
//
//mkspline ${exposure}_spline2=exposuresubgroup2,  ///
//knots(`gr2knot1' `gr2knot2' `gr2knot3' `gr2knot4') ///
//cubic displayknots
//
//mat knots2=r(knots)
*************************************************
********Generate models to use in splines********
*************************************************
// model time!
// the models here are modified poisson regressions using sandwich variance estimators
// which is the Zou method from this classic paper:
// https://pubmed.ncbi.nlm.nih.gov/15033648/
// GLMs can be used with pweight, if needed.
//
// First group 
glm ${outcome} c.${exposure}_spline1* ${covariates}  ${thing1},  ///
fam(poisson) link(log) eform robust
// robust is required for modified poisson regression by zou's method 
// (i.e., modified poisson regression with sandwich variance estimators)
// for pweight or survey data, use:
// svy: glm ${outcome} c.${exposure}_spline1* ${covariates}  ${thing1},  ///
// fam(poisson) link(log) eform
levelsof(${exposure})  ${thing1}  // generates r(levels) for next line
xbrcspline ${exposure}_spline1, values(`r(levels)') ///
ref(`median_1') matknots(knots1) /// 
eform gen(lpnt1 hr1 lb1 ub1)

// Second group
glm ${outcome} c.${exposure}_spline2* ${covariates} ${thing2}, ///
fam(poisson) link(log) eform robust
// for pweight or survey data, use:
// svy: glm ${outcome} c.${exposure}_spline2* ${covariates}  ${thing2},  ///
// fam(poisson) link(log) eform
//
levelsof(${exposure}) ${thing2} // generates r(levels) for next line
xbrcspline ${exposure}_spline2, values(`r(levels)') ///
ref(`median_2') matknots(knots2) /// 
eform gen(lpnt2 hr2 lb2 ub2)

*************************************************
***********generate extremes to drop*************
*************************************************
// should drop the extremes, here the 0.5 and 99.5th percentiles
// but need to define these as local macros.
// _pctile can be used with pweight, if needed.
_pctile exposuresubgroup1, p(0.5 99.5)
return list
local cut_a1 = r(r1)
local cut_b1 = r(r2)

_pctile exposuresubgroup2, p(0.5 99.5)
return list
local cut_a2 = r(r1)
local cut_b2 = r(r2)

*************************************************
***************make the figure*******************
*************************************************
set scheme s1mono // my favorite scheme
twoway ///
/// spline for one group
(line  hr1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) lp(solid) lc(blue) lwidth(medthick) ) ///

(rarea lb1 ub1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) color(blue%5)) ///

(line  lb1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1', yaxis(1)  lp(dash) lc(blue) lwidth(thin) ) ///

(line  ub1 lpnt1 if lpnt1 > `cut_a1' & ///
lpnt1 < `cut_b1' , yaxis(1) lp(dash) lc(blue) lwidth(thin) ) ///

/// spline for other group
(line  hr2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(solid) lc(red) lwidth(medthick) ) ///

(rarea lb2 ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) color(red%5)) ///

(line  lb2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1)  lp(dash) lc(red) lwidth(thin) ) ///

(line  ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(dash) lc(red) lwidth(thin) ) ///

(line  ub2 lpnt2 if lpnt2 > `cut_a2' & ///
lpnt2 < `cut_b2' , yaxis(1) lp(dash) lc(red) lwidth(thin) ) ///

/// kernel density for one group
/// note: can alternatively install kdens and moremata to 
/// use pweighting in kernel density.
(kdensity exposuresubgroup1 if exposuresubgroup1 > `cut_a1' & ///
exposuresubgroup1 < `cut_b1' , yaxis(2) lp(shortdash) lcolor(blue)) ///

/// kernel density for other group
(kdensity exposuresubgroup2 if exposuresubgroup2 > `cut_a2' & ///
exposuresubgroup2 < `cut_b2' , yaxis(2) lp(shortdash) lcolor(red)) ///

, ///
yline(1, lpattern(solid) lcolor(black) axis(1)) ///
/// labels for the left axis, hide the following line if you aren't sure 
/// of what the range should be:
ylabel(0.25 "0.25" 0.5 "0.5" 1 "1" 2.5 "2.5" 5 "5", axis(1) angle(0)) ///
/// range for the left axis. the BOTTOM of this range has to be WAAAY lower 
/// than the bottom ylabel in the line above in order for it to sit 
/// on the way top of the figure. 
yscale(r(0.001 2) log axis(1)) /// log scale here
/// ditto for the right axis, but the TOP of the range on the yscale needs 
/// to be WAAAY higher than the top ylabel
ylabel(0 "0" 0.01 "0.01" 0.02 "0.02" 0.03 "0.03" 0.04 "0.04" 0.05 "0.05", ///
axis(2) labsize(vsmall) angle(0)) ///
yscale(r(0.0 .2) axis(2)) /// not log scale here
/// you'll notice that the x label doesn't span the entire 
/// range of the exposure because the local macro cuts above. 
///xlabel(20(5)60) in case you want to specify the x axis
/// for the titles, need to put a bunch of spaces so things align
ytitle("                {bf:Risk Ratio (95% CI)}", axis(1)) ///
ytitle("{bf:Probability Density}                                          ", ///
 justification(left) axis(2)) ///
xtitle("{bf:Exposure Here}") ///
title("{bf:The Title}") ///
legend(order(1 "Group 1" 5 "Group 2")) ///
name(mygraph1)
			
*************************************************
*****************Save figure!********************
*************************************************
// save as png, change to tif if needed for submission			
graph export "mygraph1.png", replace width(1000)

Twitter sizing

Twitter does this weird thing where it clips figures that aren’t the correct proportion. I came across this blog post that argues that 1100×628 px is the ‘optimal’ Twitter image size.

So, how do you output Stata figures to be 1100×628?

Output a Stata figure in Twitter size in 2 steps

Step 1: Force the width and height to be 15.3 x 9.0 inches

Stata allows you to use ‘xsize(##)’ and ‘ysize(##)’ to force the height and width of a figure. Assuming a 72 dpi resolution (the default resolution for monitors), that means that your width and height should be:

twoway ///
(scatter thing otherthing) ///
, ///
xsize(15.3) ysize(9.0)

…place above behind the comma of your graph

Step 2: Set the graph output to be 1100 pixels wide

In your graph export command, after the comma, place ‘width(1100)’. Or

graph export "figurename.png", replace width(1100)

That’s it!

Buried in the supplement of a recent paper is a variant of this figure that I’m rather proud of:

It shows the distribution of quartiles of BNP and NT proBNP at baseline on a log scale, by use of beta blockers (BB) at baseline. It also shows the midway point of the medians. It’s a nice figure that shows the increase of BNP with beta blocker administration. My colleagues jokingly called it the “Plante Plot” since I have had it included in several drafts of manuscripts, this is just the first one in which it was published.

The code for it is pretty complex and follows. Steps 1-3 pluck out the ranges of the quartiles and their midpoints for each group and saves them as a CSV file. Steps 4-8 render the figure. You may find it more simple to skip Steps 1-3 and manually enter the ranges of the quartiles and their medians into an Excel file and just open up that excel file in Step 4.

Alternatively, I have a do file that automates this a bit. Just type:

do https://www.uvm.edu/~tbplante/plante%20plot%20for%20distribution%20of%20quartiles%20v1_0.do
distplot [xaxis variable] [yaxis group name]

Good luck!

// Step 1: load the database
use "029b1b analytic datset.dta", clear

// Step 2: You need quartiles plus the intermediate points of
// each quartile, which is really 8-iles.
// Step 2a: 
// You need the maximum and minimum variables to draw the bounds
// of the bottom and top quartile
// this is by group, here's the first group:
sum baseline_bnp if efstratus50==1 & baselinebb0n1y==0, d
return list // see that r(min) and r(max) are the points needed
// save min and max as macros for 0th bound and 0th bound
local bnp8ile_nobb_0=r(min) // beginning of q1
local bnp8ile_nobb_8=r(max) // end of q4
// Step 2b: 
// now get intermediate bounds for the 8-iles
_pctile baseline_bnp  if efstratus50==1  & baselinebb0n1y==0, percentiles(12.5(12.5)87.5) // this gets bounds by 12.5iles
return list // there they are!
local bnp8ile_nobb_1=r(r1) // middle of q1
local bnp8ile_nobb_2=r(r2) // end of q1/beginning of q2
local bnp8ile_nobb_3=r(r3) // middle of q2
local bnp8ile_nobb_4=r(r4) // end of q2/beginning of q3
local bnp8ile_nobb_5=r(r5) // middle of q3
local bnp8ile_nobb_6=r(r6) // end of q3/beginning of q4
local bnp8ile_nobb_7=r(r7) // middle of q4
// now come up with a label to eventually apply to the figure
// don't use commas in this label since we'll save this
// output as a CSV file and commas will screw up the cell
// structure of a CSV (C=comma)
// step 2c: 
local label_bnp_nobb="Baseline BNP; -BB"

// now repeat for the other groups
sum baseline_bnp if efstratus50==1 & baselinebb0n1y==1, d
return list
local bnp8ile_bb_0=r(min)
local bnp8ile_bb_8=r(max)
_pctile baseline_bnp  if efstratus50==1  & baselinebb0n1y==1, percentiles(12.5(12.5)87.5)
return list
local bnp8ile_bb_1=r(r1)
local bnp8ile_bb_2=r(r2)
local bnp8ile_bb_3=r(r3)
local bnp8ile_bb_4=r(r4)
local bnp8ile_bb_5=r(r5)
local bnp8ile_bb_6=r(r6)
local bnp8ile_bb_7=r(r7)
local label_bnp_bb="Baseline BNP; +BB"

sum baseline_ntprobnp if efstratus50==1 & baselinebb0n1y==0, d
return list
local ntprobnp8ile_nobb_0=r(min)
local ntprobnp8ile_nobb_8=r(max)
_pctile baseline_ntprobnp  if efstratus50==1  & baselinebb0n1y==0, percentiles(12.5(12.5)87.5)
return list
local ntprobnp8ile_nobb_1=r(r1)
local ntprobnp8ile_nobb_2=r(r2)
local ntprobnp8ile_nobb_3=r(r3)
local ntprobnp8ile_nobb_4=r(r4)
local ntprobnp8ile_nobb_5=r(r5)
local ntprobnp8ile_nobb_6=r(r6)
local ntprobnp8ile_nobb_7=r(r7)
local label_ntprobnp_nobb="Baseline NT proBNP; +BB"

sum baseline_ntprobnp if efstratus50==1 & baselinebb0n1y==1, d
return list
local ntprobnp8ile_bb_0=r(min)
local ntprobnp8ile_bb_8=r(max)
_pctile baseline_ntprobnp  if efstratus50==1  & baselinebb0n1y==1, percentiles(12.5(12.5)87.5)
return list
local ntprobnp8ile_bb_1=r(r1)
local ntprobnp8ile_bb_2=r(r2)
local ntprobnp8ile_bb_3=r(r3)
local ntprobnp8ile_bb_4=r(r4)
local ntprobnp8ile_bb_5=r(r5)
local ntprobnp8ile_bb_6=r(r6)
local ntprobnp8ile_bb_7=r(r7)
local label_ntprobnp_bb="Baseline NT proBNP; -BB"


// Step 3: save this to a csv file that we'll open up right away.
// Note: This code goes out of frame on my blog. copy and paste it 
// into a .do file and it'll all appear. 
quietly {
capture log close bnp 
log using "bnprangefigure.csv", replace text name(bnp)
// this is the row of headers:
noisily di "row,label,eight0,eight1,eight2,eight3,eight4,eight5,eight6,eight7,eight8" 
// row 1:
noisily di "1,`label_bnp_nobb',`bnp8ile_nobb_0',`bnp8ile_nobb_1',`bnp8ile_nobb_2',`bnp8ile_nobb_3',`bnp8ile_nobb_4',`bnp8ile_nobb_5',`bnp8ile_nobb_6',`bnp8ile_nobb_7',`bnp8ile_nobb_8'"
// row 2
noisily di "2,`label_bnp_bb',`bnp8ile_bb_0',`bnp8ile_bb_1',`bnp8ile_bb_2',`bnp8ile_bb_3',`bnp8ile_bb_4',`bnp8ile_bb_5',`bnp8ile_bb_6',`bnp8ile_bb_7',`bnp8ile_bb_8'"
// blank row 3:
noisily di "3"
// row 4:
noisily di "4,`label_ntprobnp_nobb',`ntprobnp8ile_nobb_0',`ntprobnp8ile_nobb_1',`ntprobnp8ile_nobb_2',`ntprobnp8ile_nobb_3',`ntprobnp8ile_nobb_4',`ntprobnp8ile_nobb_5',`ntprobnp8ile_nobb_6',`ntprobnp8ile_nobb_7',`ntprobnp8ile_nobb_8'"
// row 5:
noisily di "5,`label_ntprobnp_bb',`ntprobnp8ile_bb_0',`ntprobnp8ile_bb_1',`ntprobnp8ile_bb_2',`ntprobnp8ile_bb_3',`ntprobnp8ile_bb_4',`ntprobnp8ile_bb_5',`ntprobnp8ile_bb_6',`ntprobnp8ile_bb_7',`ntprobnp8ile_bb_8'"
log close bnp
}

// step 4: open CSV file as active database:
import delim using "bnprangefigure.csv", clear
// note, you may opt to skip steps 1-3 and manually compile the 
// ranges of each quartile and their median into an excel file.
// Use the -import excel- function to open that file up instead.
// IF YOU SKIP OVER STEPS 1-3, your excel file will need the 
/// following columns:
// row - each group, with a blank row 3 to match the figure
// label - title to go to the left of the figure
// eight0 through eight8 - the even numbers are ranges of the
//    quartiles and the odd numbers are the mid-ranges.
//    See my approach in steps 2a-2b on how to get these numbers.

// step 5: steal the labels. note skipping row 3 since it's blank
local label1=label[1]
local label2=label[2] 
local label4=label[4] // NO ROW 3!!
local label5=label[5]

// step 6: pluck the intermediate points of each quartile
// which are 8-iles 1, 3, 5 and 7
// and repeat for each row
local bar1row1=eight1[1]
local bar2row1=eight3[1]
local bar3row1=eight5[1]
local bar4row1=eight7[1]

local bar1row2=eight1[2]
local bar2row2=eight3[2]
local bar3row2=eight5[2]
local bar4row2=eight7[2]

// no row 3 in this figure

local bar1row4=eight1[4]
local bar2row4=eight3[4]
local bar3row4=eight5[4]
local bar4row4=eight7[4]

local bar1row5=eight1[5]
local bar2row5=eight3[5]
local bar3row5=eight5[5]
local bar4row5=eight7[5]

// step 7: pick a different scheme than the default stata one
// I like s1mono or s1color
set scheme s1mono

// step 8: complex graph.
// NOTE: RUN THIS SCRIPT FROM THE TOP EVERY TIME because
// stata likes to drop the macros ("local" commands) and 
// the things inside of the ticks will be missing if you 
// just run starting at the "graph twoway" below
// 
// step 8a: rbar the ends of the quartiles, which is:
// 0 to 2, 2 to 4, 4 to 6, and 6 to 8
//
// step 8b: apply the labels
//
// step 8c: place a vertical bar at the midpoints of the
// quartiles, which are at: 1, 3, 5, and 7. A bug in Stata
// is that a centered label (placement(c)) is actually a smidge
// south still, so the rows are offset by 0.13. You'll notice
// the Y label in the text box is row value minus 0.13 (0.87, etc.)
// to account for that.
//
// step 8d: adjust the aspect ratio to get the bar character ("|") 
// to fit within the width of the the bar itself.
//

graph twoway /// step 8a:
(rbar eight0 eight2 row , horizontal) /// 
(rbar eight2 eight4 row , horizontal) ///
(rbar eight4 eight6 row , horizontal) ///
(rbar eight6 eight8 row , horizontal) ///
, ///
yscale(reverse) ///
xscale(log) ///
t2title("Quartiles of BNP or NT-proBNP, EF ≥50%", justification(center)) /// 
xla(1 "1" 10 "10" 100 "100" 1000 "1000" 10000 "10000" 20000 "20000", angle(45)) /// step 8b:
yla(1 "`label1'" 2 "`label2'" 4 "`label4'" 5 "`label5'", angle(horizontal)) ///
ytitle(" ") ///
xtitle("BNP/NTproBNP Range (Log Scale)") ///
legend(off) ///
/// step 8c:
text(0.87 `bar1row1' "|", color(white) placement(c)) ///
text(0.87 `bar2row1' "|", color(white) placement(c)) ///
text(0.87 `bar3row1' "|", color(white) placement(c)) ///
text(0.87 `bar4row1' "|", color(white) placement(c)) ///
///
text(1.87 `bar1row2' "|", color(white) placement(c)) ///
text(1.87 `bar2row2' "|", color(white) placement(c)) ///
text(1.87 `bar3row2' "|", color(white) placement(c)) ///
text(1.87 `bar4row2' "|", color(white) placement(c)) ///
///
text(3.87 `bar1row4' "|", color(white) placement(c)) ///
text(3.87 `bar2row4' "|", color(white) placement(c)) ///
text(3.87 `bar3row4' "|", color(white) placement(c)) ///
text(3.87 `bar4row4' "|", color(white) placement(c)) ///
///
text(4.87 `bar1row5' "|", color(white) placement(c)) ///
text(4.87 `bar2row5' "|", color(white) placement(c)) ///
text(4.87 `bar3row5' "|", color(white) placement(c)) ///
text(4.87 `bar4row5' "|", color(white) placement(c)) ///
/// step 8d:
aspect(0.23)

This post is a bit dated but is a nice primer on KM curves. For a more recent one that includes printing the HR results on the KM curve, see this post.

In the early Winter of 2019, we had a paper published in JAMA: Network Open using the TOPCAT trial dataset looking at association between beta-blocker use at baseline and incident heart failure admissions. We obtained the data from the NIH/NHLBI BioLINCC repository. This is an incredible resource for datasets. With a quick application, IRB, and DUA, you can get world-class datasets from landmark clinical trials like ACCORD, SPRINT, TOPCAT, etc..

In this analysis we needed to put together a Kaplan-Meier plot for Figure 2 (sometimes called a survival plot). Well, technically it’s a cumulative incidence plot since the line starts a 0% and creeps up as events happen rather than starting at 100% and dropping down as events happen.

Features of this plot that are somewhat unique:

• Lines are not only different colors, one is dashed and one is solid. This is key to avoiding color publication costs in some journals as it can be made greyscale and folks can still figure out which line is which. Even if there wasn’t an issue with color costs, including differing line patterns helps folks who may be colorblind. This is done with the -plot1opts- and -plot2opts- commands.
• Text label for Log-Rank!
• Bonus example of how to use italics in text. It’s {it:YOUR TEXT}. This could also have been bold using {bf:YOUR TEXT} instead.
• Survival table that lines up with the years on the x axis!
• Pretty legend on 2 rows with custom labels.

Of course, the JAMA folks remade my figure for the actual publication. Womp, womp. I still like how it came out.

What the figure looks like

Code to make this figure

// STEP 1: tell stata that it's time to event data. 
// Use the -stset- command. Syntax:
// stset [time], failure([thing that is 0 or 1 for the event of outcome]) ...
// id([participant id, useful if doing time varying covariates]) ...
// scale([time scale, here 365.25 days=1 year)

stset days_hfhosp, f(hfhosp) id(id) scale(365.25)

// STEP 2: Set the color scheme.
// I don't like the default stata color scheme.
// Try s1color or s1mono

set scheme s1color 

// STEP 3: Here's the actual figure!

sts graph if efstratus50==1 ///
, /// this was a subset of patients with EF >=50%
fail /// this makes the line starts at the bottom of the Y axis instead of the top
by(baselinebb0n1y) /// this is the variable that defines the two groups, bb use at baseline or none
title("Cumulative Heart Failure Hospitalizations") /// Title!
t1title("Among TOPCAT Participants with EF ≥50%") /// subtitle!
xtitle("Year of Follow-Up") /// x label!
ytitle("Cumulative Incidence") /// y label!
yla(0 "0%" .25 "25%" .5 "50%" .75 "75%" 1 "100%", angle(0)) /// Y-label values! Angle(0) rotates them.
xla(0(1)6) /// X-label values! From 0 to 6 years with 1 year intervals in between
text(0.63 3 "Log-Rank {it:p}<0.001", placement(e) size(medium)) /// floating label with italics!
legend(order(1 "No Beta-Blocker" 2 "Beta-Blocker") rows(2)) /// Legend, forced on two rows
plot1opts(lpattern(dash) lcolor(red)) /// this forces the first line to be dashed and red
plot2opts(lpattern(solid) lcolor(blue)) /// this forces the second line to be solid and blue
risktable(0(1)6 , size(small) order(1 "No Beta-Blocker" 2 "Beta-Blocker")) // the numbers under the X axis

// STEP 4: Export the graph as a tiny PNG file for the draft and 
// tif file to upload with the manuscript submission. 
graph export "Figure 2.png", replace width(2000)
graph export "Figure 2.tif", replace width(2000)

If you make your own Stata programs and loops, you have discovered the wonders of automating output of analyses to tables. Extracting the results from regressions in Stata can be a bit cumbersome. Here’s one step-by-step approach that you might find helpful.

The set-up

Let’s use the classic 1978 auto dataset that comes with Stata. We want to regress MPG (Y) on weight (x) overall and by strata of domestic vs. foreign to complete the following table:

	Weight Beta (95% CI)	P-value
All
Domestic
Foreign

In Stata you’ll run three regressions to fill out the three rows:

sysuse auto, clear
regress mpg weight
regress mpg weight if foreign==0
regress mpg weight if foreign==1

You can either copy the output manually, or automate it! Let’s learn how to automate this process.

Let’s get familiar with the ‘guts’ and ‘brains’ behind Stata’s regression functions.

When you run a regression, Stata saves relevant bits of these regressions in scalars and matrices saved in different r() and e() levels. You can view the r() ‘guts’ with -return list- and e() ‘brains’ with -ereturn list-. These have different uses.

• return list – This will give the ‘guts’ of the regression, namely the r() level bits, as you see it in the Stata output. Importantly, the r() level contains the r(table) matrix, which holds all of the raw numbers used to generate the output of your regression as you see it in Stata’s output. These are what you will use to fill out the above blank table.
  • Type -matrix list r(table)- to see the structured output of this matrix.
• ereturn list – this will let you see the ‘brains’ behind the regression, namely the e() level bits, which are useful in post-estimation commands. We actually don’t need this to fill out the above table. Just pointing out that they’re here.

Let’s take a look at the regression output below and how they exist in the r() level r(table), I have bolded/underlined the output of interest. Our goal is to:

1. Load the sysuse auto dataset.
2. Run the regression.
3. Take a look at the -return list- to see that the r(table) is hiding there (without actually viewing the contents of r(table))
4. Actually view the r(table) matrix in order to verify that all of the data points of interest are hiding there.

. sysuse auto, clear
. regress mpg weight

Source |       SS           df       MS      Number of obs   =        74
-------+----------------------------------   F(1, 72)        =    134.62
 Model |   1591.9902         1   1591.9902   Prob > F        =    0.0000
Residu |  851.469256        72  11.8259619   R-squared       =    0.6515
-------+----------------------------------   Adj R-squared   =    0.6467
 Total |  2443.45946        73  33.4720474   Root MSE        =    3.4389

-------------------------------------------------------------------
   mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------+----------------------------------------------------------------     
weight |  -.0060087   .0005179   -11.60   0.000    -.0070411   -.0049763
 _cons |   39.44028   1.614003    24.44   0.000     36.22283    42.65774
----------------------------------------------------------------------

. return list

scalars:
              r(level) =  95
matrices:
              r(table) :  9 x 2

. matrix list r(table)

r(table)[9,2]
            weight       _cons
     b  -.00600869   39.440284
    se   .00051788   1.6140031
     t   -11.60251   24.436312
pvalue   3.798e-18   1.385e-36
    ll  -.00704106   36.222827
    ul  -.00497632    42.65774
    df          72          72
  crit   1.9934636   1.9934636
 eform           0           0

r(table) is a matrix, but an atypical matrix. Copy it to a custom ‘typical’ matrix before doing anything else!

Matrices are basically small spreadsheets saved in the memory that can be accessed by referencing a [row,column] cell reference. These exist separate from the dataset, which is also basically a big spreadsheet. You’ll note above (after the -matrix list r(table)- command) that Stata tells you that the r(table) matrix has 9 rows and 2 columns, or [9,2].

For various reasons that you can read about here, r(table) is not a usual matrix and Stata will do funny things if you try to run matrix commands on it. Make sure to save the r(table) matrix as custom matrix before going any further. Since we actually need to save 3 separate r(table) matrices to fill out the blank table (one for each row), you should do this anyway to help facilitate completing the table. Use the -matrix- command to copy the contents of the r(table) to a custom matrix. Here we’ll:

1. Load the sysuse auto dataset
2. Run three regressions, one for each row, and
3. Save the r(table) matrix for each regression to a custom named matrix. We’ll specifically call them “row1”, “row2”, and “row3”.
4. Then, we will confirm that each row is saved by plopping the command to view the matrices at the end.

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
* prove that all three saved
matrix list row1
matrix list row2
matrix list row3

The stata output for the last three lines should look like the output below. Note that the beta coefficient is at [1,1], the 95% confidence interval bounds are at [5,1] and [6,1], and the p-value is at [4,1]. I bolded/underlined the first to highlight this.

. matrix list row1

row1[9,2]
            weight       _cons
     b  -.00600869   39.440284
    se   .00051788   1.6140031
     t   -11.60251   24.436312
pvalue   3.798e-18   1.385e-36
    ll  -.00704106   36.222827
    ul  -.00497632    42.65774
    df          72          72
  crit   1.9934636   1.9934636
 eform           0           0

. 
. matrix list row2

row2[9,2]
            weight       _cons
     b  -.00597508   39.646965
    se   .00046538   1.5766224
     t  -12.839251   25.146772
pvalue   1.890e-17   4.898e-30
    ll  -.00690982   36.480225
    ul  -.00504035   42.813704
    df          50          50
  crit   2.0085591   2.0085591
 eform           0           0

. 
. matrix list row3

row3[9,2]
            weight       _cons
     b  -.01042596   48.918297
    se   .00249417   5.8718507
     t  -4.1801326   8.3309845
pvalue   .00046167   6.196e-08
    ll   -.0156287   36.669831
    ul  -.00522321   61.166763
    df          20          20
  crit   2.0859634   2.0859634
 eform           0           0

Let’s extract the matrix components of interest as macros!

Macros are little ‘codewords’ that represent another variable or string. You can pluck a cell of a matrix and store it as a macro. Later on, use can use that ‘codeword’ associated with the macro to make Stata blurt out the stored cell result. We just need to point the macro at the right matrix cell in order to extract the cell’s results. It sounds confusing but it’s not. Remember the [row,column] numbers from above? We’ll use those numbers to extract the matrix cell results into macros. Next steps:

1. Load the sysuse auto dataset
2. Run a regression for the first three rows of our table, saving the r(table) matrix for each regression as our custom matrix (row1-3)
3. Use macros to extract the [1,1] as beta coefficient, [5,1] and [6,1] as the 95% confidence intervals, and [4,1] as the p-value for each row.
4. View each macro with the -display- opening tick (`), to the left of the number 1 on your keyboard, the macro name, and a closing apostrophe (‘).
5. You can use number formatting like %3.2f (e.g., 0.56) or %4.3f (0.558) to limit the number of digits following the decimal. This will also round. (Note: If you want to use super fancy formatting of a P-value, see this post.)

Code to do this:

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
****
*now save the beta, 95% ci, and P as macros
*row 1
local beta1=row1[1,1]
local low951=row1[5,1]
local high951=row1[6,1]
local pval1=row1[4,1]
*row 2
local beta2=row2[1,1]
local low952=row2[5,1]
local high952=row2[6,1]
local pval2=row2[4,1]
*row 3
local beta3=row3[1,1]
local low953=row3[5,1]
local high953=row3[6,1]
local pval3=row3[4,1]
*now view these
*row 1
di "row1 beta is " %3.2f `beta1'
di "row1 95% CI is " %3.2f `low951' " to " %3.2f `high951'
di "row1 P-val is " %4.3f `pval1'
*row 2
di "row2 beta is " %3.2f `beta2'
di "row2 95% CI is " %3.2f `low952' " to " %3.2f `high952'
di "row2 P-val is " %4.3f `pval2'
*row 3
di "row3 beta is " %3.2f `beta3'
di "row3 95% CI is " %3.2f `low953' " to " %3.2f `high953'
di "row3 P-val is " %4.3f `pval3'

Hide in -quietly- curly brackets and output a CSV file using -noisily- commands and a log!

Here’s my code to run the three regression, store the r(table) matrices, extract the data of interest, and output as a .csv file! Run this from a .do file as it includes the -quietly- command, which confuses Stata if it’s run from the command line.

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
****
*view the custom matrices to prove they worked
matrix list row1
matrix list row2
matrix list row3
****
*now save the beta, 95% ci, and P as macros
*here's a loop that automates this a bit
foreach x in 1 2 3 {
local beta`x'=row`x'[1,1]
local low95`x'=row`x'[5,1]
local high95`x'=row`x'[6,1]
local pval`x'=row`x'[4,1]
}

pwd // this is where your CSV file is saved

quietly {
capture log close table // always good to close any open logs
log using "regressiontable.csv", replace text name(table)
*header
noisily di ",Weight beta (95% CI),P-value"
*row 1
noisily di "All," %3.2f `beta1' " ("  %3.2f `low951' " to " %3.2f `high951' "),"  %4.3f `pval1'
*row 2
noisily di "Domestic," %3.2f `beta2' " ("  %3.2f `low952' " to " %3.2f `high952' "),"  %4.3f `pval2'
*row 3
noisily di "Foreign," %3.2f `beta3' " ("  %3.2f `low953' " to " %3.2f `high953' "),"  %4.3f `pval3'
log close table
}

Boom! you’ll get a CSV file that looks like this, which should be simple to import in Excel!

,Weight beta (95% CI),P-value
All,-0.01 (-0.01 to -0.00),0.000
Domestic,-0.01 (-0.01 to -0.01),0.000
Foreign,-0.01 (-0.02 to -0.01),0.000

You’ll notice that these numbers are small, so you may want to use %4.3f instead of %3.2f to get 3 digits past the decimal place for the beta and 95% CIs.

Bonus: Generating a CSV file with pretty P-values

Here’s the same code, but using the “pretty” p-value code documented in this separate post.

sysuse auto, clear 
* first row
regress mpg weight
* save as a custom matrix
matrix row1=r(table)
* second row
regress mpg weight if foreign==0
* save as a custom matrix
matrix row2=r(table)
* third row
regress mpg weight if foreign==1
* save as a custom matrix
matrix row3=r(table)
****
*view the custom matrices to prove they worked
matrix list row1
matrix list row2
matrix list row3
****
*now save the beta, 95% ci, and P as macros
*here's a loop that automates this a bit
foreach x in 1 2 3 {
local beta`x'=row`x'[1,1]
local low95`x'=row`x'[5,1]
local high95`x'=row`x'[6,1]
local pval`x'=row`x'[4,1]
local pval = r(table)[4,1] 

if `pval`x''>=0.056 {
	local pvalue`x' "P=`: display %3.2f `pval`x'''"
}
if `pval`x''>=0.044 & `pval'<0.056 {
	local pvalue`x' "P=`: display %5.4f `pval`x'''"
}
if `pval`x'' <0.044 {
	local pvalue`x' "P=`: display %4.3f `pval`x'''"
}
if `pval`x'' <0.001 {
	local pvalue`x' "P<0.001"
}
if `pval`x'' <0.0001 {
	local pvalue`x' "P<0.0001"
}
di "original P is " `pval`x'' ", formatted is " "`pvalue`x''"
}

pwd // this is where your CSV file is saved

quietly {
capture log close table // always good to close any open logs
log using "regressiontable.csv", replace text name(table)
*header
noisily di ",Weight beta (95% CI),P-value"
*row 1
noisily di "All," %3.2f `beta1' " ("  %3.2f `low951' " to " %3.2f `high951' "),"  "`pvalue1'"
*row 2
noisily di "Domestic," %3.2f `beta2' " ("  %3.2f `low952' " to " %3.2f `high952' ")," "`pvalue2'"
*row 3
noisily di "Foreign," %3.2f `beta3' " ("  %3.2f `low953' " to " %3.2f `high953' "),"   "`pvalue3'"
log close table
}

Here’s the output:

,Weight beta (95% CI),P-value
All,-0.01 (-0.01 to -0.00),P<0.0001
Domestic,-0.01 (-0.01 to -0.01),P<0.0001
Foreign,-0.01 (-0.02 to -0.01),P<0.001