A handmade clubSandwich for multi-site trials

I’m just back from the Society for Research on Educational Effectiveness meetings, where I presented work on small-sample corrections for cluster-robust variance estimators in two-stage least squares models, which I’ve implemented in the clubSandwich R package.

clubSandwich at the Austin R User Group Meetup

Last night I attended a joint meetup between the Austin R User Group and R Ladies Austin, which was great fun. The evening featured several lightning talks on a range of topics, from breaking into data science to network visualization to starting your own blog.

Pooling clubSandwich results across multiple imputations

A colleague recently asked me about how to apply cluster-robust hypothesis tests and confidence intervals, as calculated with the clubSandwich package, when dealing with multiply imputed datasets. Standard methods (i.

Imputing covariance matrices for meta-analysis of correlated effects

In many systematic reviews, it is common for eligible studies to contribute effect size estimates from not just one, but multiple relevant outcome measures, for a common sample of participants.


Cluster-robust variance estimation

Clustered standard errors and hypothesis tests in fixed effects models

I’ve recently been working with my colleague Beth Tipton on methods for cluster-robust variance estimation in the context of some common econometric models, focusing in particular on fixed effects models for panel data—or what statisticians would call “longitudinal data” or “repeated measures.

The clubSandwich package for meta-analysis with RVE

I’ve recently been working on small-sample correction methods for hypothesis tests in linear regression models with cluster-robust variance estimation. My colleague (and grad-schoolmate) Beth Tipton has developed small-sample adjustments for t-tests (of single regression coefficients) in the context of meta-regression models with robust variance estimation, and together we have developed methods for multiple-contrast hypothesis tests.

Meta-sandwich with extra mustard

In an earlier post about sandwich standard errors for multi-variate meta-analysis, I mentioned that Beth Tipton has recently proposed small-sample corrections for the covariance estimators and t-tests, based on the bias-reduced linearization approach of McCaffrey, Bell, and Botts (2001).

Another meta-sandwich

In a previous post, I provided some code to do robust variance estimation with metafor and sandwich. Here’s another example, replicating some more of the calculations from Tanner-Smith & Tipton (2013).

A meta-sandwich

A common problem arising in many areas of meta-analysis is how to synthesize a set of effect sizes when the set includes multiple effect size estimates from the same study.