Likelihood-Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable

• Activity Number: 325
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Roland A. Matsouaka*+ and Eric Tchetgen
• Companies: Harvard School of Public Health and Harvard School of Public Health
• Keywords: Non-compliance ; Causal odds ratio ; Congenial parametrization ; Treatment on the treated ; Goodness-of-fit ; likelihood parametrization

Abstract

Current estimating equation methods for logistic structural nested mean models (SNMMs) either rely heavily on possible "uncongenial" modeling assumptions or involve a cumbersome integral equation which must be solved, for each independent unit and at each step of solving the estimating equation. These drawbacks have impeded widespread use of these methods.

In this talk, we present an alternative parametrization of the likelihood function for the logistic SNMM that circumvents computational complexity of existing methods while ensuring a congenial parametrization of SNMM. We also provide a goodness-of-fit (GOF) test statistic for evaluating parametric assumptions made by the likelihood model. Our method can be easily implemented using standard statistical softwares, and is illustrated via a simulation study and two data applications.