statsmodels.stats.meta_analysis.combine_effects¶

statsmodels.stats.meta_analysis.
combine_effects
(effect, variance, method_re='iterated', row_names=None, use_t=False, alpha=0.05, **kwds)[source]¶ combining effect sizes for effect sizes using metaanalysis
This currently does not use np.asarray, all computations are possible in pandas.
 Parameters
 effect
array
mean of effect size measure for all samples
 variance
array
variance of mean or effect size measure for all samples
 method_re{“iterated”, “chi2”}
method that is use to compute the between random effects variance “iterated” or “pm” uses Paule and Mandel method to iteratively estimate the random effects variance. Options for the iteration can be provided in the
kwds
“chi2” or “dl” uses DerSimonian and Laird onestep estimator. row_names
list
of
strings
(optional
) names for samples or studies, will be included in results summary and table.
 alpha
float
in
(0, 1) significance level, default is 0.05, for the confidence intervals
 effect
 Returns
 results
CombineResults
Contains estimation results and intermediate statistics, and includes a method to return a summary table. Statistics from intermediate calculations might be removed at a later time.
 results
Notes
Status: Basic functionality is verified, mainly compared to R metafor package. However, API might still change.
This computes both fixed effects and random effects estimates. The random effects results depend on the method to estimate the RE variance.
Scale estimate In fixed effects models and in random effects models without fully iterated random effects variance, the model will in general not account for all residual variance. Traditional metaanalysis uses a fixed scale equal to 1, that might not produce test statistics and confidence intervals with the correct size. Estimating the scale to account for residual variance often improves the small sample properties of inference and confidence intervals. This adjustment to the standard errors is often referred to as HKSJ method based attributed to Hartung and Knapp and Sidik and Jonkman. However, this is equivalent to estimating the scale in WLS. The results instance includes both, fixed scale and estimated scale versions of standard errors and confidence intervals.
References
 Borenstein, Michael. 2009. Introduction to MetaAnalysis.
Chichester: Wiley.
 Chen, DingGeng, and Karl E. Peace. 2013. Applied MetaAnalysis with R.
Chapman & Hall/CRC Biostatistics Series. Boca Raton: CRC Press/Taylor & Francis Group.