English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Consider estimating the G-formula for the counterfactual mean outcome under agiven treatment regime in a longitudinal study. Bang and Robins provided anestimator for this quantity that relies on a sequential regression formulationof this parameter. This approach is doubly robust in that it is consistent ifeither the outcome regressions or the treatment mechanisms are consistentlyestimated. We define a stronger notion of double robustness, termed sequentialdouble robustness, for estimators of the longitudinal G-formula. The definitionemerges naturally from a more general definition of sequential doublerobustness for the outcome regression estimators. An outcome regressionestimator is sequentially doubly robust (SDR) if, at each subsequent timepoint, either the outcome regression or the treatment mechanism is consistentlyestimated. This form of robustness is exactly what one would anticipate isattainable by studying the remainder term of a first-order expansion of theG-formula parameter. We show that a particular implementation of an existingprocedure is SDR. We also introduce a novel SDR estimator, whose developmentinvolves a novel translation of ideas used in targeted minimum loss-basedestimation to the infinite-dimensional setting.

Sequential Double Robustness in Right-Censored Longitudinal Models