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| Title | Accounting for clustering with mi impute | |
| Authors | Wesley Eddings and Yulia Marchenko, StataCorp |
Note 1: This frequently asked question (FAQ) assumes familiarity with multiple imputation. Please see the documentation entries [MI] intro substantive and [MI] intro if you are unfamiliar with the method. Also, if your data have already been imputed, see the documentation entry [MI] mi import on how to import your data to mi and see [MI] mi estimate on how to analyze your multiply imputed data.
Note 2: Because the mi impute command is based on random draws, results may differ on previous versions as a consequence of the 64-bit Mersenne Twister pseudorandom numbers, which was added to Stata in version 14.
As of Stata 11.1, the mi estimate command can be used to analyze multiply imputed clustered (panel or longitudinal) data by fitting several clustered-data models, such as xtreg, xtlogit, and mixed; see mi estimation for the full list. However, we must also account for clustering when creating multiply imputed data; this FAQ will show how.
We can create multiply imputed data with mi impute, Stata’s official command for imputing missing values. There is no definitive recommendation in the literature on the best way to impute clustered data, but three strategies have been suggested:
We will explain how to carry out each strategy with mi impute. We will assume for now that we have data in long form and that only one variable has missing values; extensions to more than one imputed variable will be described later.
If there are not too many clusters, we can account for clustering by including cluster indicators in our imputation model. The factor-variable syntax of Stata makes it easy to include the indicators with mi impute: we do not even have to generate any new variables.
Our first example dataset, data1.dta, has 40 observations within each of 10 clusters; the variable id indexes observations within clusters. Ten percent of the observations have missing values for the observation-level predictor x; no values of the response y are missing. We want to study the association between y and the partially observed predictor x while accounting for the association within clusters.
. use http://www.stata.com/support/faqs/data1
. describe
Contains data from http://www.stata.com/support/faqs/data1.dta
Observations: 400
Variables: 4 29 Jul 2010 14:56
| Variable Storage Display Value |
| name type format label Variable label |
| cluster float %9.0g id float %9.0g y double %10.0g x double %10.0g |
| -> cluster = 1 | |||||
| Variable | Obs Mean Std. dev. Min Max | |
| y | 40 97.49968 33.62784 23.14309 160.8217 | |
| x | 38 30.00173 7.943642 12.95944 42.72091 |
| -> cluster = 2 | |||||
| Variable | Obs Mean Std. dev. Min Max | |
| y | 40 100.2756 31.70555 20.78498 151.5145 | |
| x | 39 30.77486 8.020621 5.549631 44.48839 |
| -> cluster = 3 | |||||
| Variable | Obs Mean Std. dev. Min Max | |
| y | 40 147.5954 37.44895 71.18038 217.404 | |
| x | 38 31.85539 8.794805 16.45632 49.47706 |
We impute the missing values of x with mi impute regress, a Gaussian regression imputation method. We account for clustering by including in our imputation model the factor variable i.cluster. The response y should also be included as a predictor:
. mi set wide . mi register imputed x . mi impute regress x y i.cluster, add(5) noisily rseed(123) Running regress on observed data:
| Source | SS df MS | Number of obs = 360 | |
| F(10, 349) = 32.74 | |||
| Model | 11088.9434 10 1108.89434 | Prob > F = 0.0000 | |
| Residual | 11821.207 349 33.8716533 | R-squared = 0.4840 | |
| Adj R-squared = 0.4692 | |||
| Total | 22910.1504 359 63.8165749 | Root MSE = 5.8199 |
| x | Coefficient Std. err. t P>|t| [95% conf. interval] | |
| y | .1572187 .0088668 17.73 0.000 .1397797 .1746578 | |
| cluster | ||
| 2 | .5249299 1.326672 0.40 0.693 -2.084348 3.134208 | |
| 3 | -6.21639 1.410625 -4.41 0.000 -8.990786 -3.441994 | |
| 4 | -1.153281 1.364677 -0.85 0.399 -3.837306 1.530743 | |
| 5 | .6848743 1.387169 0.49 0.622 -2.043388 3.413136 | |
| 6 | -4.79826 1.409348 -3.40 0.001 -7.570143 -2.026376 | |
| 7 | -1.828347 1.34203 -1.36 0.174 -4.46783 .8111363 | |
| 8 | -1.427531 1.349231 -1.06 0.291 -4.081178 1.226117 | |
| 9 | 1.565089 1.353659 1.16 0.248 -1.097267 4.227444 | |
| 10 | -2.067867 1.384883 -1.49 0.136 -4.791633 .6558993 | |
| _cons | 14.49285 1.287011 11.26 0.000 11.96157 17.02412 | |
| Observations per m | ||||
| Variable | Complete Incomplete Imputed | Total | ||
| x | 360 40 40 | 400 | ||
We used the noisily option of mi impute to display the intermediate regression output, which shows that nine dummy variables were properly included for the ten clusters. We now fit our analysis model by using, for example, mixed with the mi estimate: prefix:
. mi estimate: mixed y x || cluster:
Multiple-imputation estimates Imputations = 5
Mixed-effects ML regression Number of obs = 400
Group variable: cluster Number of groups = 10
Obs per group:
min = 40
avg = 40.0
max = 40
Average RVI = 0.0723
Largest FMI = 0.1366
DF adjustment: Large sample DF: min = 238.81
avg = 22,755.03
max = 89,559.86
Model F test: Equal FMI F( 1, 292.0) = 285.80
Prob > F = 0.0000
| y | Coefficient Std. err. t P>|t| [95% conf. interval] | |
| x | 2.92631 .1730981 16.91 0.000 2.585632 3.266988 | |
| _cons | 22.40641 7.02119 3.19 0.001 8.627185 36.18563 | |
| Random-effects parameters | Estimate Std. err. [95% conf. interval] | |
| cluster: Identity | ||
| sd(_cons) | 14.02273 3.406021 8.711221 22.57284 | |
| sd(Residual) | 25.46452 .9772013 23.61045 27.46419 | |
The coefficient of x is estimated to be about 3 with a standard error of about 0.2, and the cluster-level intercepts have a mean of about 22 with a standard deviation of about 14. Had we not included the cluster variable in our imputation model, we would have obtained a smaller estimate of the variance component for clusters.
Graham (2009) suggests that cluster indicators can work well for as many as 35 indicator variables. Strategy 1 is best suited for data with few clusters and many observations within each cluster.
By including clusters as indicator variables in our imputation model (strategy 1), we allow the regression function of the imputed variable to vary by cluster. More generally, we can allow the distributions of the imputed values to differ among clusters by imputing each cluster separately (Graham 2009). Since Stata 12, we can use mi impute with the by() option.
Our second example dataset, data2.dta, like the first, includes a response variable with no missing values and a predictor x with 10% missing values. We have 50 observations within each of 20 clusters. We will impute each cluster separately and then fit an analysis model with mixed.
. use http://www.stata.com/support/faqs/data2.dta . mi set wide . mi register imputed x . mi impute regress x y, add(5) by(cluster, noreport) rseed(123) Univariate imputation Imputations = 5 Linear regression added = 5 Imputed: m=1 through m=5 updated = 0
| Observations per m | |||||
| by() | |||||
| Variable | Complete Incomplete Imputed | Total | |||
| cluster = 1 | |||||
| x | 44 6 6 | 50 | |||
| cluster = 2 | |||||
| x | 47 3 3 | 50 | |||
| ... | |||||
| cluster = 19 | |||||
| x | 45 5 5 | 50 | |||
| cluster = 20 | |||||
| x | 46 4 4 | 50 | |||
| Overall | |||||
| x | 900 100 100 | 1000 | |||
We now fit mi estimate: mixed to our multiply imputed data:
. mi estimate: mixed y x || cluster:
Multiple-imputation estimates Imputations = 5
Mixed-effects ML regression Number of obs = 1,000
Group variable: cluster Number of groups = 20
Obs per group:
min = 50
avg = 50.0
max = 50
Average RVI = 0.0546
Largest FMI = 0.1551
DF adjustment: Large sample DF: min = 187.43
avg = 156,793.24
max = 422,770.46
Model F test: Equal FMI F( 1, 2471.3) = 1577.92
Prob > F = 0.0000
| y | Coefficient Std. err. t P>|t| [95% conf. interval] | |
| x | 8.134255 .2047741 39.72 0.000 7.732709 8.535802 | |
| _cons | 19.50823 6.343581 3.08 0.002 7.07497 31.9415 | |
| Random-effects parameters | Estimate Std. err. [95% conf. interval] | |
| cluster: Identity | ||
| sd(_cons) | 26.52947 4.312226 19.29167 36.48273 | |
| sd(Residual) | 30.48525 .7451697 29.05013 31.99127 | |
The coefficient for x is about 8 with a standard error of about 0.2, and the intraclass correlation is about (272)/(272 + 302) = 0.45. The intraclass correlation ranges from zero to one, and larger values mean that the clustering variable is more informative.
Imputing each cluster separately requires a sufficient number of observations in each cluster.
A third way to account for within-cluster correlation is to impute jointly over clusters using a multivariate normal model. Observations within clusters may be viewed as a sample from a multivariate normal distribution with an unrestricted covariance structure. The multivariate normal strategy works well when there are only a few observations in each cluster (Allison 2002). There is a limitation to this strategy: it is best suited to balanced repeated-measures data.
We will illustrate the multivariate normal strategy with a new balanced dataset. It has 50 clusters but only 5 observations within each cluster. (Such data might occur, for example, in a repeated-measures study of subjects’ test scores.) We would once again like to impute missing values of x and then fit a linear mixed-effects model with mixed.
Before we can fit the multivariate normal imputation model, we will need to reshape our data to wide form so that each cluster occupies a single row. The variable id indexes observations within clusters.
. use http://www.stata.com/support/faqs/data3 . reshape wide x y, i(cluster) j(id) (j = 1 2 3 4 5)
| Data Long -> Wide |
| Number of observations 250 -> 50 Number of variables 4 -> 11 j variable (5 values) id -> (dropped) xij variables: x -> x1 x2 ... x5 y -> y1 y2 ... y5 |
We can now impute with mi impute mvn, and the multivariate normal regression model will allow interdependencies within clusters.
. mi set wide
. mi register imputed x1 x2 x3 x4 x5
. mi impute mvn x1 x2 x3 x4 x5 = y1 y2 y3 y4 y5, add(5) rseed(123)
Performing EM optimization:
observed log likelihood = -296.02862 at iteration 16
Performing MCMC data augmentation ...
Multivariate imputation Imputations = 5
Multivariate normal regression added = 5
Imputed: m=1 through m=5 updated = 0
Prior: uniform Iterations = 500
burn-in = 100
between = 100
| Observations per m | |||||
| Variable | Complete Incomplete Imputed | Total | |||
| x1 | 42 8 8 | 50 | |||
| x2 | 43 7 7 | 50 | |||
| x3 | 46 4 4 | 50 | |||
| x4 | 47 3 3 | 50 | |||
| x5 | 47 3 3 | 50 | |||
To use mi estimate: mixed, we need to reshape our data back to long form. With mi data, we need to use the mi reshape command to do this:
. mi reshape long x y, i(cluster) j(id) reshaping m=0 data ... (j = 1 2 3 4 5)
| Data Wide -> Long |
| Number of observations 50 -> 250 Number of variables 11 -> 4 j variable (5 values) -> id xij variables: x1 x2 ... x5 -> x y1 y2 ... y5 -> y |
We are now ready to use mi estimate: mixed:
. mi estimate: mixed y x || cluster:
Multiple-imputation estimates Imputations = 5
Mixed-effects ML regression Number of obs = 250
Group variable: cluster Number of groups = 50
Obs per group:
min = 5
avg = 5.0
max = 5
Average RVI = 0.0942
Largest FMI = 0.2699
DF adjustment: Large sample DF: min = 65.13
avg = 551,115.87
max = 2201656.39
Model F test: Equal FMI F( 1, 65.1) = 45.75
Prob > F = 0.0000
| y | Coefficient Std. err. t P>|t| [95% conf. interval] | |
| x | .8791031 .1299657 6.76 0.000 .6195535 1.138653 | |
| _cons | 2.570404 2.13238 1.21 0.229 -1.625142 6.76595 | |
| Random-effects parameters | Estimate Std. err. [95% conf. interval] | |
| cluster: Identity | ||
| sd(_cons) | 11.37668 1.183462 9.278313 13.94961 | |
| sd(Residual) | 5.042148 .2573897 4.561861 5.573 | |
All three strategies can be modified to impute more than one variable. The indicator-variable and separate-imputation strategies, strategies 1 and 2, require a multivariate imputation method such as mi impute monotone, mi impute chained, or mi impute mvn in place of a univariate method such as mi impute regress. The multivariate normal strategy, strategy 3, can be extended by adding extra variables to the left-hand side of the equation in mi impute mvn. If we wanted to impute x and another variable z, the commands might look like this:
. reshape wide x y z, i(cluster) j(id) . mi set wide . mi register imputed x1 x2 x3 x4 x5 z1 z2 z3 z4 z5 . mi impute mvn x1 x2 x3 x4 x5 z1 z2 z3 z4 z5 = y1 y2 y3 y4 y5, add(5) . mi reshape long x y z, i(cluster) j(id)
All our examples had the same two-level structure—observations within clusters. More-complex multilevel structures are an active research area; one recent paper describing imputation for multilevel models is Goldstein et al. (2009).
