Mapping R workflows to PyMetaAnalysis

This guide helps users translate conventional R meta and metafor workflows into PyMetaAnalysis calls. It is a terminology and configuration map, not a claim that similarly named functions always use identical formulas or defaults.

Always compare the resolved configuration and numerical output when porting an analysis. See validation for the R versions, calls, and fixtures used by this project.

Entry points

Analysis PyMetaAnalysis R metafor R meta
Generic effects and variances meta_analysis() rma.uni(yi, vi, ...) metagen(TE, seTE, ...)
Binary 2x2 tables, inverse variance meta_binary(..., method="IV") escalc() then rma.uni() metabin(..., method="Inverse")
Binary 2x2 tables, Mantel-Haenszel meta_binary(..., method="MH") rma.mh() metabin(..., method="MH")
Continuous group summaries meta_continuous() escalc() then rma.uni() metacont()
Subgroups subgroup= on a high-level call separate fits or a moderator model subgroup=
Leave-one-out result.leave_one_out() leave1out() for supported fits metainf()
Cumulative analysis result.cumulative() cumul() metacum()

PyMetaAnalysis intentionally has no metabin, metacont, or rma aliases. One documented Python entry point per input shape keeps result types and provenance behavior consistent.

Input names

Meaning PyMetaAnalysis R metafor R meta
Study effect effect yi TE
Sampling variance variance vi square of seTE
Study label study or DataFrame index slab studlab
Treatment events event_treat ai event.e
Treatment total n_treat n1i n.e
Control events event_control ci event.c
Control total n_control n2i n.c
Treatment mean/SD mean_treat, sd_treat m1i, sd1i mean.e, sd.e
Control mean/SD mean_control, sd_control m2i, sd2i mean.c, sd.c

PyMetaAnalysis accepts DataFrame column names or aligned one-dimensional array-like values. When study= is omitted for a DataFrame, its index supplies the display labels.

Measures and scales

PyMetaAnalysis measure Meaning metafor measure meta sm Model scale
"OR" Odds ratio "OR" "OR" log ratio
"RR" Risk ratio "RR" "RR" log ratio
"RD" Risk difference "RD" "RD" identity
"MD" Mean difference "MD" "MD" identity
"SMD" Exact-corrected Hedges' g "SMD" with the documented correction "SMD" with exact Hedges correction identity

For OR and RR, estimate and ci remain on the log model scale. display_estimate and display_ci provide exponentiated ratios. This is similar to choosing transformed or untransformed printing in R, but both scales remain explicit attributes in Python.

Models, pooling, and heterogeneity

PyMetaAnalysis metafor analogue meta analogue Notes
model="common" rma.uni(..., method="EE") common=TRUE, random=FALSE Inverse-variance common-effect fit
model="random" random-effects rma.uni() random=TRUE Requires a tau-squared policy
method="IV" inverse-variance weighting method="Inverse" Binary API only; generic and continuous fits are IV
method="MH" rma.mh() method="MH" Common-effect OR/RR only
tau2_method="REML" method="REML" method.tau="REML" PyMetaAnalysis random-effects default
tau2_method="PM" method="PM" method.tau="PM" Paule-Mandel
tau2_method="DL" method="DL" method.tau="DL" DerSimonian-Laird

The same label does not guarantee identical optimizer tolerances, boundary handling, or heterogeneity definitions. In particular, PyMetaAnalysis records i2_method; random-effects inverse-variance results use the documented tau-squared/typical-variance definition, while common-effect and MH results use the Q-based definition.

Confidence and prediction intervals

PyMetaAnalysis ci_method metafor R meta Behavior
"normal" default test="z" method.random.ci="classic" Normal mean interval
"hartung_knapp" test="knha" method.random.ci="HK" Unmodified HK variance and t quantile
"hartung_knapp_adhoc" test="adhoc" HK plus an explicitly selected ad hoc correction HK variance cannot fall below the classic variance

Eligible random-effects fits include the documented HTS prediction interval. R packages offer additional prediction-interval choices, so matching the mean interval does not by itself guarantee a matching prediction interval.

Sparse binary studies

The closest names are:

PyMetaAnalysis R meta Meaning
continuity_correction incr Increment used for corrected study effects
correction_scope="only_zero_studies" method.incr="only0" Correct only studies containing a zero cell
correction_scope="if_any_zero" method.incr="if0all" Correct every study when any study contains a zero cell
correction_scope="all_studies" method.incr="all" Correct every study
correction_scope="none" no increment Disable study-effect correction
mh_continuity_correction=None MH.exact=TRUE in intent Avoid correction for exact MH pooling where defined

These are conceptual mappings, not interchangeable switches. R meta also supports dataset-wide correction scopes and methods that PyMetaAnalysis does not implement. For OR/RR, double-zero and double-all studies are excluded from all model and heterogeneity calculations by default while remaining visible in the result table. RD uses its separate rd_zero_variance policy.

Read zero-event studies before translating sparse analyses.

Worked generic translation

Python:

import meta_analyze as ma

result = ma.meta_analysis(
    data=studies,
    effect="yi",
    variance="vi",
    model="random",
    tau2_method="REML",
    ci_method="hartung_knapp_adhoc",
)

The corresponding metafor configuration is conceptually:

rma.uni(
  yi = yi,
  vi = vi,
  method = "REML",
  test = "adhoc",
  data = studies
)

The corresponding R meta configuration starts from standard errors rather than variances. PyMetaAnalysis can accept that uncertainty column directly as standard_error=; no manual squaring step is required:

result = ma.meta_analysis(
    studies,
    effect="yi",
    standard_error="sei",
    model="random",
    tau2_method="REML",
    ci_method="hartung_knapp_adhoc",
)

metagen(
  TE = yi,
  seTE = sqrt(vi),
  common = FALSE,
  random = TRUE,
  method.tau = "REML",
  method.random.ci = "HK",
  data = studies
)

Check the R package's explicit ad hoc HK option before treating the last call as numerically equivalent.

Primary R references