<- See Stata 18's new features
Highlights
Multilevel meta-analysis and meta-regression
Adjust for moderators
Multiple levels of hierarchy
Random intercepts and slopes
Random-effects covariance structures
Sensitivity analysis
REML and ML estimation methods
Multilevel Q statistic and test
Heterogeneity
Cochran's multilevel \(I^2\) statistic
Higgins–Thompson multilevel \(I^2\) statistics
Postestimation
Prediction of random effects
Comparative and diagnostic standard errors
Variance–covariance matrix of random effects
Residuals
Standardized residuals
See more meta-analysis features
You want to analyze results from multiple studies, in which the reported effect sizes are nested within higher-level groupings such as regions or schools. Stata 18 adds two new commands, meta meregress and meta multilevel, to the meta suite to perform multilevel meta-analysis and meta-regression. Include random intercepts and coefficients at different levels of hierarchy, and assume different random-effects covariance structures, including exchangeable and unstructured. Perform sensitivity analysis by placing various constraints on variance components. Assess heterogeneity. Predict random effects and their comparative and diagnostic standard errors. And more.
Multilevel meta-analysis is a powerful statistical tool to synthesize effect sizes with a hierarchical structure, such as in a meta-analysis exploring the impact of a new teaching technique on math testing scores in different school districts. Here the effect sizes are nested within schools that are themselves nested within districts. Multilevel meta-analysis allows us not only to determine the overall effect of the technique but also to assess the variability among the effect sizes at different levels of the hierarchy. This is important because studies within the same district are likely to be similar and thus potentially dependent, and ignoring this dependence may lead to inaccurate results. By properly accounting for the dependence among the effect sizes, we can produce more accurate inference and gain a better understanding of the impact of the teaching technique.
-> Example dataset: Modified school calendar data
-> Multilevel meta-analysis: Constant-only model
-> Multilevel heterogeneity
-> Multilevel meta-regression and random slopes: Incorporating moderators
-> Random-effects covariance structures
-> Predicting random effects
Many studies suggest that the long summer break at the end of a school year is linked to a learning gap between students because of students' differential access to learning opportunities in the summer.
Cooper, Valentine, and Melson (2003) conducted a multilevel meta-analysis on schools that modified their calendars without prolonging the school year. The dataset consists of 56 studies that were conducted in 11 school districts. Some schools adopted modified calendars that featured shorter breaks more frequently throughout the year (for example, 12 weeks of school followed by 4 weeks off) as opposed to the traditional calendar with a longer summer break and shorter winter and spring breaks. The studies compared the academic achievement of students on a traditional calendar with those on a modified calendar. The effect size (stdmdiff) is the standardized mean difference, with positive values indicating higher achievement, on average, in the group on the modified calendar. The standard error (se) of stdmdiff was also reported by each study. Let's first describe our dataset:
. webuse schoolcal
(Effect of modified school calendar on student achievement)
. describe
Contains data from https://www.stata-press.com/data/r18/schoolcal.dta
Observations: 56 Effect of modified school calendar on student
achievement
Variables: 8 19 Jan 2023 21:44
(_dta has notes)
| Variable Storage Display Value | ||||||
| name type format label Variable label | ||||||
| district int %12.0g District ID | ||||||
| school byte %9.0g School ID | ||||||
| study byte %12.0g Study ID | ||||||
| stdmdiff double %10.0g Standardized difference in means of achievement test scores | ||||||
| var double %10.0g Within-study variance of stdmdiff | ||||||
| year int %12.0g Year of the study | ||||||
| se double %10.0g Within-study standard-error of stdmdiff | ||||||
| year_c byte %9.0g Year of the study centered around 1990 | ||||||
Because the schools are nested within districts, we fit a three-level random-intercepts model. This requires that we specify two random-effects equations: one for level 3 (identified by variable district) and one for level 2 (identified by variable school).
. meta meregress stdmdiff || district: || school: , essevariable(se) Performing EM optimization ... Performing gradient-based optimization: Iteration 0: Log restricted-likelihood = -104.8525 (not concave) Iteration 1: Log restricted-likelihood = -46.670529 (not concave) Iteration 2: Log restricted-likelihood = -22.871266 (not concave) Iteration 3: Log restricted-likelihood = -12.977299 Iteration 4: Log restricted-likelihood = -7.9642885 Iteration 5: Log restricted-likelihood = -7.9587271 Iteration 6: Log restricted-likelihood = -7.9587239 Iteration 7: Log restricted-likelihood = -7.9587239 Computing standard errors ... Multilevel REML meta-analysis Number of obs = 56
| No. of Observations per group | ||||
| Group variable | groups Minimum Average Maximum | |||
| district | 11 3 5.1 11 | |||
| school | 56 1 1.0 1 | |||
| stdmdiff | Coefficient Std. err. z P>|z| [95% conf. interval] | |||||
| _cons | .1847132 .0845559 2.18 0.029 .0189866 .3504397 | |||||
| Random-effects parameters | Estimate | |
| district: Identity | ||
| sd(_cons) | .2550724 | |
| school: Identity | ||
| sd(_cons) | .1809324 | |
The first table displays information on groups at different levels of hierarchy with one row for each grouping (level of hierarchy).
The second table displays the fixed-effects coefficients. Here there is only an intercept corresponding to the overall effect size \(\hat{\theta}\). The value of \(\theta\) is 0.185 with a 95% CI of [0.019, 0.35]. This means that, on average, students following the modified school calendar achieved higher scores than those who did not.
The third table displays the random-effects parameters, traditionally known as variance components in the context of multilevel or mixed-effects models. The variance-component estimates are now organized and labeled according to each level. By default, meta meregress reports standard deviations of the random intercepts (and correlations if they existed in the model) at each level. But you can instead specify the variance option to report variances (and covariances if they existed in the model). We have \(\hat{\tau_3} = 0.255\) and \(\hat{\tau_2} = 0.181\). These values are the building blocks for assessing heterogeneity across different hierarchical levels and are typically interpreted in that context. In general, the higher the value of \(\tau_l\), the more heterogeneity is expected among the groups within level \(l\).
Alternatively, this can be specified using the meta multilevel command as follows:
. meta multilevel stdmdiff, relevels(district school) essevariable(se) (output omitted)
The meta multilevel command was designed to fit random-intercepts meta-regression models, which are commonly used in practice. It is a convenience wrapper for meta meregress.
We will use the postestimation command estat heterogeneity to quantify the multilevel heterogeneity among the effect sizes.
. estat heterogeneity Method: Cochran Joint: I2 (%) = 90.50 Method: Higgins–Thompson district: I2 (%) = 63.32 school: I2 (%) = 31.86 Total: I2 (%) = 95.19
Cochran’s \(I^2\) quantifies the amount of heterogeneity jointly for all levels of hierarchy. \(I^2 = 90.50\%\) means that 90.50% of the variability among the effect sizes is due to true heterogeneity in our data as opposed to the sampling variability.
The multilevel Higgins–Thompson \(I^2\) statistics assess the contribution of each level of hierarchy to the total heterogeneity, in addition to their joint contribution. For example, between-schools heterogeneity or heterogeneity within districts (level 2 heterogeneity) is the lowest, accounting for about 32% of the total variation in our data, whereas between-districts heterogeneity (level 3 heterogeneity) accounts for about 63% of the total variation.
We will use variable year_c to conduct a three-level meta-regression and include random slopes (corresponding to variable year_c) at the district level.
. meta meregress stdmdiff year_c || district: year_c || school: , essevariable(se) Performing EM optimization ... Performing gradient-based optimization: Iteration 0: Log restricted-likelihood = -101.95646 (not concave) Iteration 1: Log restricted-likelihood = -94.528133 (not concave) Iteration 2: Log restricted-likelihood = -29.169697 (not concave) Iteration 3: Log restricted-likelihood = -10.67081 (not concave) Iteration 4: Log restricted-likelihood = -7.5089434 (not concave) Iteration 5: Log restricted-likelihood = -7.2219899 Iteration 6: Log restricted-likelihood = -7.2085474 (not concave) Iteration 7: Log restricted-likelihood = -7.2082538 (not concave) Iteration 8: Log restricted-likelihood = -7.2079523 (not concave) Iteration 9: Log restricted-likelihood = -7.2073687 (not concave) Iteration 10: Log restricted-likelihood = -7.2067537 (not concave) Iteration 11: Log restricted-likelihood = -7.1989783 Iteration 12: Log restricted-likelihood = -7.1891619 Iteration 13: Log restricted-likelihood = -7.1815206 Iteration 14: Log restricted-likelihood = -7.1813888 Iteration 15: Log restricted-likelihood = -7.1813887 Computing standard errors ... Multilevel REML meta-regression Number of obs = 56
| No. of Observations per group | ||||
| Group variable | groups Minimum Average Maximum | |||
| district | 11 3 5.1 11 | |||
| school | 56 1 1.0 1 | |||
| stdmdiff | Coefficient Std. err. z P>|z| [95% conf. interval] | |||||
| year_c | .0059598 .0106378 0.56 0.575 -.0148899 .0268094 | |||||
| _cons | .1805809 .0904865 2.00 0.046 .0032306 .3579311 | |||||
| Random-effects parameters | Estimate | |
| district: Independent | ||
| sd(year_c) | .0177247 | |
| sd(_cons) | .219239 | |
| school: Identity | ||
| sd(_cons) | .1807703 | |
The estimate of the regression coefficient of variable year_c is 0.006 with a 95% CI of [–0.015, 0.027] . We do not see any evidence for the association between stdmdiff and year_c (p = 0.575).
Although year_c did not explain the heterogeneity, we continue to include it as a moderator for illustration purposes.
By default, the random slopes and random intercepts (at the district level) are assumed independent. Alternatively, we can specify an exchangeable covariance structure using option covariance(exchangeable). We suppress the header and the iteration log and display results with three decimal points using the noheader, nolog, and cformat(%9.3f) options.
. meta meregress stdmdiff year_c || district: year_c, covariance(exchangeable) || school:, essevariable(se) noheader nolog cformat(%9.3f)
| stdmdiff | Coefficient Std. err. z P>|z| [95% conf. interval] | |||||
| year_c | 0.010 0.012 0.80 0.426 -0.014 0.033 | |||||
| _cons | 0.153 0.074 2.06 0.039 0.008 0.298 | |||||
| Random-effects parameters | Estimate | |
| district: Exchangeable | ||
| sd(year_c _cons) | 0.032 | |
| corr(year_c,_cons) | 1.000 | |
| school: Identity | ||
| sd(_cons) | 0.181 | |
Alternatively, we can specify a custom covariance structure by fixing the correlation between the intercepts and slopes at 0.5 and allowing for their standard deviations to be estimated from the data:
. matrix A = (.,.5 \ .5,.) . meta meregress stdmdiff year_c || district: year_c, covariance(custom A) || school:, essevariable(se) noheader nolog cformat(%9.3f)
| stdmdiff | Coefficient Std. err. z P>|z| [95% conf. interval] | |||||
| year_c | 0.007 0.011 0.67 0.500 -0.014 0.028 | |||||
| _cons | 0.170 0.082 2.08 0.038 0.010 0.330 | |||||
| Random-effects parameters | Estimate | |
| district: Custom | ||
| sd(year_c) | 0.026 | |
| sd(_cons) | 0.116 | |
| corr(year_c,_cons) | 0.500* | |
| school: Identity | ||
| sd(_cons) | 0.180 | |
Below, we predict the random effects using predict, reffects and obtain their diagnostic standard errors by specifying the reses(, diagnostic) option.
. quietly meta meregress stdmdiff || district: || school: , essevariable(se) . predict double u3 u2, reffects reses(se_u3 se_u2, diagnostic) . by district, sort: generate tolist = (_n==1) . list district u3 se_u3 if tolist
| district u3 se_u3 | |||
| 1. | 11 -.18998595 .07071817 | ||
| 5. | 12 -.08467077 .13168501 | ||
| 9. | 18 .1407273 .11790486 | ||
| 12. | 27 .24064814 .13641505 | ||
| 16. | 56 -.1072942 .13633364 | ||
| 20. | 58 -.23650899 .15003184 | ||
| 31. | 71 .5342778 .12606073 | ||
| 34. | 86 -.2004695 .1499012 | ||
| 42. | 91 .05711692 .14284823 | ||
| 48. | 108 -.14168396 .13094894 | ||
| 53. | 644 -.01215679 .10054689 | ||
The random intercepts u3 are district-specific deviations from the overall mean effect size. For example, for district 18, the predicted standardized mean difference is 0.14 higher than the overall effect size.
Cooper, H., Valentine, J. C., and Melson, A. 2003. The effects of modified school calendars on student achievement and on school and community attitudes. Review of Educational Research 73: 1–52.
Learn more about other new features in meta-analysis.
Read more in the Stata Meta-Analysis Reference Manual; see [META] meta meregress, [META] meta multilevel, and [META] meta me postestimation.
View all the new features in Stata 18.
