The Basics of Running a Meta-Analysis

Here we walk through the basic steps of running a meta-analysis with PyMARE.

Start with the necessary imports

from pprint import pprint

from pymare import core, datasets, estimators

Load the data

We will use the Michael et al.1 dataset, which comes from the metadat library 2.

We only want to do a mean analysis, so we won’t have any covariates except for an intercept.

data, meta = datasets.michael2013()
dset = core.Dataset(data=data, y="yi", v="vi", X=None, add_intercept=True)
dset.to_df()
y v intercept
0 0.23 0.038539 1.0
1 -0.04 0.007195 1.0
2 0.23 0.016788 1.0
3 0.11 0.019805 1.0
4 -0.04 0.003913 1.0
5 0.05 0.002331 1.0
6 0.31 0.022049 1.0
7 0.10 0.024691 1.0
8 0.33 0.025544 1.0
9 -0.05 0.009700 1.0
10 0.18 0.009267 1.0
11 -0.02 0.009692 1.0


Now we fit a model

You must first initialize the estimator, after which you can use fit() to fit the model to numpy arrays, or fit_dataset() to fit it to a Dataset.

Tip

We generally recommend using fit_dataset() over fit().

There are a number of methods, such as get_heterogeneity_stats() and permutation_test(), which only work when the Estimator is fitted to a Dataset.

However, fit() requires less memory than fit_dataset(), so it can be useful for large-scale meta-analyses, such as neuroimaging image-based meta-analyses.

The summary() function will return a MetaRegressionResults object, which contains the results of the analysis.

name estimate se z-score p-value ci_0.025 ci_0.975
0 intercept 0.052993 0.026738 1.981957 0.047484 0.000588 0.105399


We can also extract some useful information from the results object

The get_heterogeneity_stats() method will calculate heterogeneity statistics.

Out:

{'H': array([1.19590745]),
 'I^2': array([30.07944649]),
 'Q': array([15.73214091]),
 'p(Q)': array([0.15136996])}

The get_re_stats() method will estimate the confidence interval for \tau^2.

Out:

{'ci_l': array([0.]), 'ci_u': array([0.04013725]), 'tau^2': 0.0}

The permutation_test() method will run a permutation test to estimate more accurate p-values.

name estimate se z-score p-value p-value (perm.) ci_0.025 ci_0.975
0 intercept 0.052993 0.026738 1.981957 0.047484 0.058 0.000588 0.105399


References

1

Robert B Michael, Eryn J Newman, Matti Vuorre, Geoff Cumming, and Maryanne Garry. On the (non) persuasive power of a brain image. Psychonomic bulletin & review, 20(4):720–725, 2013. URL: https://doi.org/10.3758/s13423-013-0391-6, doi:10.3758/s13423-013-0391-6.

2

Thomas White, Daniel Noble, Alistair Senior, W. Kyle Hamilton, and Wolfgang Viechtbauer. metadat: Meta-Analysis Datasets. 2022. R package version 1.2-0. URL: https://CRAN.R-project.org/package=metadat.

Total running time of the script: ( 0 minutes 0.037 seconds)

Gallery generated by Sphinx-Gallery