The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

drmeta

Design-Robust Meta-Analysis: A Variance-Function Framework for Causal Credibility

drmeta implements the DR-Meta model of Hait (2026) — a variance-function random-effects framework that embeds causal design robustness directly into the heterogeneity structure of meta-analysis.

The core idea

Standard random-effects meta-analysis absorbs design-quality differences into a single constant \(\tau^2\). This allows large-sample but weakly-identified studies to dominate pooled estimates. DR-Meta instead models between-study variance as a monotone-decreasing function of a design robustness index \(\text{DR}_i \in [0,1]\):

\[y_i \sim N\!\bigl(\mu,\; \underbrace{v_i}_{\text{sampling}} + \underbrace{\tau^2(\text{DR}_i;\,\psi)}_{\text{design-dependent}}\bigr)\]

Study weights \(w_i = 1/\sigma_i^2\) therefore automatically down-weight weaker designs — not via ad-hoc quality multipliers, but through a coherent likelihood-based mechanism.

Special cases

Condition	DR-Meta reduces to
\(\text{DR}_i = c\) (constant)	Classical random-effects (Proposition 1)
\(\tau_0^2 = 0\)	Fixed-effects meta-analysis (Proposition 2)

Installation

# Install the development version from GitHub:
devtools::install_github("causalfragility-lab/drmeta")

Quick start

library(drmeta)

# 1. Build the DR index
dr <- dr_from_design(c("RCT", "DiD", "OLS", "IV", "matching"))

# or from continuous sub-scores:
dr <- dr_score(
  balance = c(0.9, 0.6, 0.3, 0.8, 0.5),
  overlap  = c(0.85, 0.7, 0.4, 0.75, 0.6),
  design   = dr_from_design(c("RCT", "DiD", "OLS", "IV", "matching"))
)

# 2. Fit DR-Meta
fit <- drmeta(yi = my_effects, vi = my_variances, dr = dr)
summary(fit)

# 3. Visualise
dr_forest(fit)       # forest plot ordered by DR
dr_plot(fit)         # weights vs DR (Lemma 3)
dr_plot_vfun(fit)    # estimated variance function

# 4. Diagnostics
dr_heterogeneity(fit)  # Q, I², Proposition 6 decomposition
dr_loo(fit)            # leave-one-out influence

# 5. Publication bias
dr_pub_bias(fit)  # PET / PEESE / Egger with DR-Meta weights
dr_funnel(fit)    # funnel plot

Key functions

Function	Description
`drmeta()`	Fit DR-Meta via profiled REML (core estimation)
`dr_score()`	Build DR index from continuous sub-scores
`dr_from_design()`	Build DR index from design type labels
`dr_weights()`	Compute and inspect DR-Meta weights
`dr_variance()`	Evaluate the fitted variance function
`dr_heterogeneity()`	Cochran’s Q, I², H², Proposition 6 decomposition
`dr_loo()`	Leave-one-out influence diagnostics
`dr_forest()`	Forest plot ordered by DR
`dr_plot()`	Weight vs DR diagnostic plot
`dr_plot_vfun()`	Variance function curve
`dr_pub_bias()`	PET / PEESE / Egger publication bias
`dr_funnel()`	Funnel plot with DR-coloured points
`normalize_01()`	Rescale any vector to [0,1]

Connection to the literature

DR-Meta is a special case of the meta-analytic location-scale model (Viechtbauer & Lopez-Lopez, 2022) in which the scale component is explicitly motivated by causal design theory. It is complementary to bias-model frameworks (Turner et al., 2009; Rhodes et al., 2015; Mathur & VanderWeele, 2020), which adjust the mean for confounding while DR-Meta adjusts the variance.

References

Hait, S. (2026). Design-Robust Meta-Analysis: A Variance-Function Framework for Causal Credibility. Unpublished manuscript.
Viechtbauer, W., & Lopez-Lopez, J. A. (2022). Research Synthesis Methods, 13, 697–715. https://doi.org/10.1002/jrsm.1562
Turner, R. M., et al. (2009). JRSS-A, 172, 21–47.
Rhodes, K. M., et al. (2015). JRSS-A, 178, 641–666.
Mathur, M. B., & VanderWeele, T. J. (2020). JASA, 115, 1190–1204.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.