metaLong-package: metaLong: Longitudinal Meta-Analysis with Robust Inference...

In metaLong: Longitudinal Meta-Analysis with Robust Variance Estimation and Sensitivity Analysis

metaLong: Longitudinal Meta-Analysis with Robust Inference and Sensitivity Analysis

Description

metaLong provides a coherent workflow for synthesising evidence from studies that report outcomes at multiple follow-up time points. The package covers:

• Pooling: ml_meta() fits a random-effects or fixed-effects model at each time point using robust variance estimation (RVE) and optional Tipton small-sample corrections via clubSandwich.

• Sensitivity: ml_sens() computes the time-varying Impact Threshold for a Confounding Variable (ITCV), both raw and significance-adjusted.

• Benchmark calibration: ml_benchmark() regresses each observed study-level covariate and compares its partial correlation against the ITCV threshold.

• Nonlinear time trends: ml_spline() fits natural-cubic-spline meta-regression over follow-up time with pointwise confidence bands.

• Fragility: ml_fragility() computes leave-one-out and leave-k-out fragility indices across the trajectory.

• Visualisation: ml_plot() and S3 plot() methods produce publication-ready trajectory, sensitivity, and benchmark figures.

Typical workflow

meta  <- ml_meta(data, yi="g", vi="vg", study="id", time="wave")
sens  <- ml_sens(data, meta, yi="g", vi="vg", study="id", time="wave")
bench <- ml_benchmark(data, meta, sens, yi="g", vi="vg",
                       study="id", time="wave",
                       covariates=c("pub_year","n","quality"))
spl   <- ml_spline(meta, df=3)
ml_plot(meta, sens, bench, spl)

Author(s)

Maintainer: Subir Hait [e-mail] (ORCID)

References

Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. Sociological Methods & Research, 29(2), 147-194.

Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39-65.

Tipton, E. (2015). Small sample adjustments for robust variance estimation with meta-regression. Psychological Methods, 20(3), 375-393.

See Also

Useful links:

• https://github.com/causalfragility-lab/metaLong

• Report bugs at https://github.com/causalfragility-lab/metaLong/issues

metaLong documentation built on March 31, 2026, 1:07 a.m.