Tag: standard error

Useful package: clubSandwich

{clubSandwich} “provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, two-stage least squares regression models, and generalized linear models. Several adjustments are incorporated to improve small-sample performance. The package includes functions for estimating the variance-covariance matrix and for testing single- and multiple-contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddlepoint corrections. Tests of multiple-contrast hypotheses use an approximation to Hotelling’s T-squared distribution.”

Includes the approximation of the leave-one-cluster-out jackknife variance estimator by Bell and McCaffrey (2002; CR3) for LMMs in lme4::lmer. Doesn’t currently (27 Oct 2024) support GLMMs.

Variance estimation when matching with replacement

“Matching with replacement induces two types of correlations that must be accounted for when estimating the variance of estimated treatment effects. The first is a within-matched set correlation in outcomes. Matched subjects within the same matched set have similar values of the propensity score. Subjects who have the same value of the propensity score have measured baseline covariates that come from the same multivariate distribution. In the presence of confounding, baseline covariates are related to the outcome. Thus, matched subjects are more likely to have similar outcomes compared to two randomly selected subjects. The second source of correlation is induced by repeated use of control subjects. Failure to account for this correlation and acting as though the matched control subjects were independent observations will likely result in estimated standard errors that are artificially small and estimated confidence intervals that are artificially narrow. Added complexity is introduced by having subjects cross-classified with matched sets such that the same control subject can belong to more than one matched set.”

Austin, P. C., & Cafri, G. (2020, p. 1625). [Variance estimation when using propensity‐score matching with replacement with survival or time‐to‐event outcomes. Statistics in Medicine, 39(11), 1623–1640.]