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Activity Number: 574
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #311740
Title: Semiparametric Efficient Estimation of the Proportion in Favor of Treatment
Author(s): Alexander Luedtke*+ and Daniel Rubin and Mark J. van der Laan
Companies: University of California, Berkeley and FDA and University of California, Berkeley
Keywords: composite outcomes ; prioritized outcomes ; causal inference ; double robust estimation ; targeted minimum loss based estimation ; TMLE
Abstract:

Many experiments wishing to study the efficacy of a treatment have multiple outcomes of interest. Marginally testing each outcome for an effect and correcting for multiple testing can lead to substantial power loss and a lack of interpretability. Recent works have proposed instead reporting the proportion of the time that a treated patient has a better overall outcome than an untreated patient according to a chosen comparison function. We generalize these works by developing an analogous causal parameter that can be identified with a statistical parameter under assumptions. We then derive the canonical gradient of this statistical parameter. We use this best linear approximation of the parameter mapping to develop a semiparametric efficient substitution estimator under the targeted minimum loss based estimation (TMLE) framework. This allows the needed regressions to be estimated using machine learning algorithms, while still yielding valid inference under mild regularity conditions. The proposed estimator is double robust in the sense that it is consistent if either the treatment mechanism or the conditional means of the outcome given covariates are estimated consistently.


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