JSM 2005 - Toronto

Abstract #302476

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 214
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302476
Title: Estimation of Treatment Effects in Randomized Trials with Noncompliance and a Binary Outcome
Author(s): Nicholas Jewell*+
Companies: University of California, Berkeley
Address: Division of Biostatistics, Berkeley, CA, 94611,
Keywords: Binary outcome ; Causal relative risk ; Instrumental variable ; Intent to treat ; Noncompliance
Abstract:

In this paper, we consider a class of estimators of a received treatment effect on a binary outcome among treated subjects within covariate and treatment arm strata in randomized trials with noncompliance. Recent work of Vansteelandt and Goethebeur and Robins and Rotnitzky present consistent and asymptotically linear estimators of a causal odds ratio, which rely---beyond correct specification of a model for the causal odds ratio---on a correctly specified model for a (potentially high-dimensional) nuisance parameter. Here, we propose consistent, asymptotically linear (and locally efficient) estimators of a causal relative risk and a new parameter (coined a switch causal relative risk), which rely on only the correct specification of a model for the parameter of interest. Our estimators are always consistent and asymptotically linear at the null hypothesis of no-treatment effect. They thereby provide valid testing procedures since, by construction, our model for the causal relative risk always includes the value 1. Some brief simulations results and application to a simple published data set will be described in the talk. This is joint work with Mark van der Laan and Alan Hubbard.

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Revised March 2005