SurrogateRegression {SurrogateRegression}

SurrogateRegression: Surrogate Outcome Regression Analysis

Description

This package performs estimation and inference on a partially missing target outcome while borrowing information from a correlated surrogate outcome. Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. However, in order for surrogate inference to improve power, the target and surrogate outcomes must be correlated, and the target outcome must be partially missing. The primary estimation function is FitBNR. In the case of bilateral missingness, i.e. missingness in both the target and surrogate outcomes, estimation is performed via an expectation conditional maximization either (ECME) algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. Inference on regression parameters for the target outcome is performed using TestBNR.

Author(s)

Zachary R. McCaw