Reader reaction: Instrumental variable additive hazards models with exposure-dependent censoring

Reader reaction: Instrumental variable additive hazards models with exposure-dependent censoring.

Li, Fine, and Brookhart (2015) presented an extension of the two-stage least squares (2SLS) method for additive hazards models which requires an assumption that the censoring distribution is unrelated to the endogenous exposure variable. We present another extension of 2SLS that can address this limitation.

Analysis of Additive Instrumental Variable Models

We apply Bayesian methods to a model involving a binary endogenous regressor and an instrumental variable in which the functional forms of some of the covariates in both the treatment assignment and outcome distributions are unknown. Continuous and binary response variables are considered. Under the assumption that the functional form is additive in the covariates, we develop efficient Markov c...

Additive hazards regression with censoring indicators missing at random.

In this article, the authors consider a semiparametric additive hazards regression model for right-censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters. Nonparametric smoothing techniques are employed to estimate the probability of non-missi...

What do instrumental variable models deliver with discrete dependent variables?

Generalised instrumental variable models

The ability to allow for flexible forms of unobserved heterogeneity is an essential ingredient in modern microeconometrics. In this paper we extend the application of instrumental variable (IV) methods to a wide class of problems in which multiple values of unobservable variables can be associated with particular combinations of observed endogenous and exogenous variables. In our Generalized In...