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## How can I combine results other than coefficients in e(b) with multiply imputed data?

 Title Combining results other than coefficients in e(b) with multiply imputed data Authors Isabel Canette and Yulia Marchenko, StataCorp

mi estimate estimates parameters from multiply imputed data and adjusts these estimates and their respective standard errors for the imputation uncertainty using Rubin’s combination rules. mi estimate is designed to work with Stata estimation commands. As such, it combines the estimates of coefficients, which are stored in matrix e(b), and their respective variance–covariance estimates (VCE), stored in matrix e(V). You may also want to combine results other than e(b). The aim of this tutorial is to show you how to do that.

This entry is organized as follows:

### Finding out where your results are saved

First, you need to know where your results are saved. Most Stata commands save results either in e() or r(). You can type

. ereturn list

or

. return list

to find out where your value of interest is saved after running a command. Alternatively, you can look at the Stored results section in the help file or documentation for the command.

### Combining point estimates only

In some situations, we are interested in a point estimate, such as the R-squared (R2) value from a regression, not in its variance. According to Rubin's rules, the estimate of the value of interest should be computed for each imputation, and the overall value will be the mean of these estimates. We can do this manually, taking advantage of mi xeq, which allows you to run sequences of commands of interest on each individual imputation.

In the following example, results are combined for the R2 value from a linear regression. We use this example strictly for the purpose of illustration. See the unofficial command mibeta (type . search mibeta to locate and install this command), which automatically provides R2, adjusted R2, and standardized coefficients after regression for imputed data.

We will use the dataset from the example of house resale prices in [MI] mi estimate. Our first step is to identify where our result of interest is stored. The regress command is an e-class command and it stores R2 in e(r2):

. webuse mhouses1993s30
(Albuquerque home prices Feb15–Apr30, 1993)

. mi xeq 0: regress price tax sqft age nfeatures ne custom corner

m=0 data:
-> regress price tax sqft age nfeatures ne custom corner

 Source SS df MS Number of obs = 66 F(7, 58) = 51.86 Model 9164658.61 7 1309236.94 Prob > F = 0.0000 Residual 1464105.15 58 25243.1922 R-squared = 0.8623 Adj R-squared = 0.8456 Total 10628763.8 65 163519.442 Root MSE = 158.88
 price Coefficient Std. err. t P>|t| [95% conf. interval] tax .4988701 .1584853 3.15 0.003 .1816273 .8161128 sqft .3522184 .0957476 3.68 0.001 .1605588 .5438779 age -.5650817 2.002529 -0.28 0.779 -4.57358 3.443416 nfeatures 4.389607 18.55499 0.24 0.814 -32.75223 41.53145 ne -17.38534 47.27462 -0.37 0.714 -112.0158 77.2451 custom 174.9411 53.72371 3.26 0.002 67.40139 282.4808 corner -73.58234 49.13007 -1.50 0.140 -171.9269 24.76218 _cons 92.7448 101.607 0.91 0.365 -110.6438 296.1334
. ereturn list scalars: e(N) = 66 e(df_m) = 7 e(df_r) = 58 e(F) = 51.86495180785406 e(r2) = .8622506644760125 (output omitted)

We used mi xeq 0: in the command line above to execute the regress command on the original data m=0.

Now we want to compute the average of the R2 values. To do so, we can use mi xeq: to run regressions on each imputed dataset and collect individual R2 values:

. mi query
data mi set mlong, M = 30

. local M = r(M)

.  scalar r2 = 0

. mi xeq 1/M': regress price tax sqft age nfeatures ne custom corner;
> scalar r2 = r2 + e(r2)

m=1 data:
-> regress price tax sqft age nfeatures ne custom corner

 Source SS df MS Number of obs = 117 F(7, 109) = 74.79 Model 13895850.5 7 1985121.5 Prob > F = 0.0000 Residual 2893096.28 109 26542.1677 R-squared = 0.8277 Adj R-squared = 0.8166 Total 16788946.8 116 144732.3 Root MSE = 162.92
 price Coefficient Std. err. t P>|t| [95% conf. interval] tax .676841 .1140902 5.93 0.000 .450718 .9029641 sqft .2091065 .065971 3.17 0.002 .0783542 .3398589 age -.2350972 1.472105 -0.16 0.873 -3.152762 2.682568 nfeatures 4.17071 12.572 0.33 0.741 -20.74658 29.088 ne 14.4082 33.3729 0.43 0.667 -51.73581 80.55221 custom 137.1073 41.98151 3.27 0.001 53.90127 220.3132 corner -75.24024 39.1843 -1.92 0.057 -152.9023 2.421776 _cons 149.6721 66.01572 2.27 0.025 18.83105 280.5131
-> scalar r2 = r2 + e(r2) ... (output omitted) ... m=30 data: -> regress price tax sqft age nfeatures ne custom corner
 Source SS df MS Number of obs = 117 F(7, 109) = 71.60 Model 13790079.1 7 1970011.3 Prob > F = 0.0000 Residual 2998867.71 109 27512.5478 R-squared = 0.8214 Adj R-squared = 0.8099 Total 16788946.8 116 144732.3 Root MSE = 165.87
 price Coefficient Std. err. t P>|t| [95% conf. interval] tax .6938792 .1261562 5.50 0.000 .4438417 .9439168 sqft .2044596 .0710267 2.88 0.005 .0636869 .3452322 age .3372971 1.591275 0.21 0.833 -2.816558 3.491152 nfeatures 6.940631 13.04224 0.53 0.596 -18.90865 32.78992 ne 7.280361 36.79407 0.20 0.844 -65.6443 80.20502 custom 132.975 43.24393 3.07 0.003 47.26696 218.6831 corner -70.26249 39.74756 -1.77 0.080 -149.0409 8.515876 _cons 128.2627 67.20399 1.91 0.059 -4.933393 261.4588
-> scalar r2 = r2 + e(r2) . scalar r2 = r2/M' . di as txt "R2 over imputed data = " as res r2 R2 over imputed data = .82300853

We used mi query above to identify the number of imputations and to store it in the local macro M. Then we used mi xeq 1/M': to run regressions on the imputed data and accumulate the R2 values in the r2 scalar. We used a semicolon to separate the two commands we wished to execute on each imputed datum; see [MI] mi xeq for details. Finally, to obtain the average, we divided r2 by the number of imputations.

We recommend applying Rubin’s combination rules to parameters in a metric for which the asymptotic normal approximation works well. Fisher’s z, or inverse hyperbolic tangent [atanh()], transformation is often recommended for the correlation coefficient R to improve its asymptotic normality. Thus we can combine the R2 values in the transformed metric and then use the inverse transformation, tanh(), to switch back to the original metric.

. local M = 30

. scalar r2 = 0

. qui mi xeq 1/M': regress price tax sqft age nfeatures ne custom corner; scalar
> r2 = r2 + atanh(sqrt(e(r2)))

. scalar r2 = tanh(r2/M')^2

. di as txt "R2 using Fisher's z over imputed data = " as res r2
R2 using Fisher's z over imputed data = .82306058

The two estimates of the R2 are very similar.

### Combining point estimates and their variances

If we want to combine point estimates and their variances, we can use mi estimate. However, we need to create an e-class program (see program) that saves the necessary results where mi estimate expects to see them. The general guidelines are described in program properties.

Below we demonstrate how to use mi estimate to combine results from r-class command roctab using the heart attack example from [MI] mi estimate. The roctab command posts the estimate of the area under the curve (AUC) in r(area) and its standard error in r(se):

. webuse mheart1s20
(Fictional heart attack data; BMI missing)

. qui mi passive: generate high_bmi = (bmi>30) if bmi<.

. mi xeq 0: roctab attack high_bmi

m=0 data:
-> roctab attack high_bmi

 ROC Asymptotic normal Obs area Std. err. [95% conf. interval] 132 0.5452 0.0322 0.48208 0.60833
. return list scalars: r(N) = 132 r(area) = .5452035889029503 r(se) = .0322055279124565 r(lb) = .4820819140914361 r(ub) = .6083252637144645 r(level) = 95

In the above series of commands, we created a new variable indicating high body mass index (BMI) values, high_bmi, which we use as a classification variable in the receiver operating characteristic (ROC) analysis of heart attacks. Because bmi is the imputed variable, high_bmi (being a function of bmi) is a passive variable. As such, we used mi passive: generate to create the passive variable high_bmi; see mi register and mi passive for details.

Following the guidelines in program properties, we need to create an e-class program that stores the AUC estimate and its standard error in e(b) and e(V). We also need to store other results, such as the name of the command in e(cmd), the number of observations in e(N), and the title of the command in e(title).

In the following code, the e-class program eroctab is defined. eroctab calls roctab and posts necessary results to e(); see ereturn for details. This program accepts two arguments: the name of the reference variable (stored in the local macro refvar) and the name of the classification variable (stored in the local macro classvar). In step 1, we perform ROC analysis of variables in refvar and classvar using roctab. In step 2, we save the estimates of the area and its variance in temporary matrices b and V, respectively. Then in step 3, we label columns of coefficient matrix b' and rows and columns of the VCE matrix V' consistently, as required by ereturn post. Finally, in step 4, we post coefficient and VCE matrices as well as other results to e().

cap program drop eroctab
program eroctab, eclass
version 12.0

/* Step 1: perform ROC analysis */
args refvar classvar
roctab refvar' classvar'

/* Step 2: save estimate and its variance in temporary matrices*/
tempname b V
mat b' = r(area)
mat V' = r(se)^2
local N = r(N)

/* Step 3: make column names and row names consistent*/
mat colnames b' = AUC
mat colnames V' = AUC
mat rownames V' = AUC

/*Step 4: post results to e()*/
ereturn post b' V', obs(N')
ereturn local cmd "eroctab"
ereturn local title "ROC area"
end

Inference about the AUC estimate in roctab is based on the large-sample normal approximation. Sometimes commands, for example regress, adjust for small samples and use Student’s t distribution for inference. In such cases, the corresponding (residual or denominator) degrees of freedom must be posted to e(df_r). This can be done by specifying the dof() option with ereturn post.

Now we can use eroctab with the mi estimate: prefix to obtain multiple-imputation estimates of the ROC area:

. mi estimate, cmdok: eroctab attack high_bmi

 Multiple-imputation estimates Imputations = 20 ROC area Number of obs = 154 Average RVI = 0.0753 Largest FMI = 0.0705 DF adjustment: Large sample DF: min = 3,873.11 avg = 3,873.11 max = 3,873.11
 Coefficient Std. err. t P>|t| [95% conf. interval] AUC .5450938 .0312929 17.42 0.000 .4837416 .6064459

In the command line above, we used the cmdok option with mi estimate because eroctab is not one of the estimation commands officially supported by mi estimate. Alternatively, we could have included the mi property in the definition of the program eroctab to use it with mi estimate directly:

cap program drop eroctab
program eroctab, eclass properties(mi)
...
end

. mi estimate: eroctab attack high_bmi

### An example using suest

Another situation when you need to define your own program is when your results are not obtained via a single command, but by using a sequence of commands such as, for example, with suest.

Below we demonstrate an example in which we want to compare the effects of smoking on heart attacks from two logistic models: one adjusting for both age and BMI and the other adjusting only for BMI.

First, we create a new program, mysuest, which fits two models of interest and then uses suest to combine the two estimation results. The models of interest are passed as two arguments and are stored in local macros model1 and model2. To avoid specifying the cmdok option with mi estimate, we add properties(mi) to the definition of the mysuest program.

cap program drop mysuest
program mysuest, eclass properties(mi)
version 12.0
args model1 model2

qui model1'
estimates store est1
qui `model2'
estimates store est2
suest est1 est2
estimates drop est1 est2

ereturn local title "Seemingly unrelated estimation"
end

Because suest is an estimation command and already stores the appropriate results in e(), we did not need to manually post (or repost) anything to e() except e(title), which is not stored by suest.

We can now use mysuest with mi estimate:

. webuse mheart1s20, clear
(Fictional heart attack data; BMI missing)

. mi estimate: mysuest "logit attack smokes age bmi" "logit attack smokes bmi"

Multiple-imputation estimates                   Imputations       =         20
Seemingly unrelated estimation                  Number of obs     =        154
Average RVI       =     0.0541
Largest FMI       =     0.1568
DF adjustment:   Large sample                   DF:     min       =     794.17
avg       = 234,946.42
Within VCE type:       Robust                           max       = 667,104.68

 Coefficient Std. err. t P>|t| [95% conf. interval] est1_attack smokes 1.187115 .3574611 3.32 0.001 .486502 1.887728 age .0352896 .0162749 2.17 0.030 .0033913 .067188 bmi .102868 .0478936 2.15 0.032 .0089024 .1968336 _cons -5.312597 1.69795 -3.13 0.002 -8.641634 -1.983561 est2_attack smokes 1.172281 .3496687 3.35 0.001 .4869414 1.85762 bmi .0921183 .0470325 1.96 0.051 -.0002044 .1844411 _cons -3.038422 1.229553 -2.47 0.014 -5.451692 -.6251518

Recall that within multiple-imputation framework, to test the equality of coefficients, we must first estimate their difference and then use mi testtransform to test the hypothesis; see [MI] mi test for details.

So, to test the coefficients on smokes from the two logistic models, we first estimate their difference with mi estimate. To display only transformed results, we specify the nocoef option:

. mi estimate (diff: [est1_attack]smokes - [est2_attack]smokes), nocoef:
> mysuest "logit attack smokes age bmi" "logit attack smokes bmi"

Multiple-imputation estimates                   Imputations       =         20
Seemingly unrelated estimation                  Number of obs     =        154
Average RVI       =     0.0047
Largest FMI       =     0.0047
DF adjustment:   Large sample                   DF:     min       = 866,779.11
avg       = 866,779.11
Within VCE type:       Robust                           max       = 866,779.11

diff: [est1_attack]smokes - [est2_attack]smokes

 Coefficient Std. err. t P>|t| [95% conf. interval] diff .0148342 .079597 0.19 0.852 -.1411732 .1708417

Although the t test, which is automatically reported by mi estimate, is sufficient for testing the hypothesis of no difference between the two coefficient estimates of smokes, we can also use the following mi testtransform command to test this hypothesis:

. mi testtransform diff
note: assuming equal fractions of missing information.

diff: [est1_attack]smokes - [est2_attack]smokes

( 1)  diff = 0

F(  1,866779.1) =    0.03
Prob > F =    0.8522

We do not have sufficient evidence to reject the null hypothesis of the equality of the smoking effects from the considered logistic models.

We could easily extend our mysuest program to allow more than two models to be combined by suest.