ArXiv Preprint
Benkeser et al. demonstrate how adjustment for baseline covariates in
randomized trials can meaningfully improve precision for a variety of outcome
types, including binary, ordinal, and time-to-event. Their findings build on a
long history, starting in 1932 with R.A. Fisher and including the more recent
endorsements by the U.S. Food and Drug Administration and the European
Medicines Agency. Here, we address an important practical consideration: how to
select the adjustment approach -- which variables and in which form -- to
maximize precision, while maintaining nominal confidence interval coverage.
Balzer et al. previously proposed, evaluated, and applied Adaptive
Prespecification to flexibly select, from a prespecified set, the variables
that maximize empirical efficiency in small randomized trials (N<40). To avoid
overfitting with few randomized units, adjustment was previously limited to a
single covariate in a working generalized linear model (GLM) for the expected
outcome and a single covariate in a working GLM for the propensity score. Here,
we tailor Adaptive Prespecification to trials with many randomized units.
Specifically, using V-fold cross-validation and the squared influence curve as
the loss function, we select from an expanded set of candidate algorithms,
including both parametric and semi-parametric methods, the optimal combination
of estimators of the expected outcome and known propensity score. Using
simulations, under a variety of data generating processes, we demonstrate the
dramatic gains in precision offered by our novel approach.
Laura B. Balzer, Erica Cai, Lucas Godoy Garraza, Pracheta Amaranath
2022-10-31
