SurrogateRegression: Surrogate Outcome Regression Analysis

Performs estimation and inference on a partially missing target outcome while borrowing information from a correlated surrogate outcome to increase estimation precision and improve power. The target and surrogate outcomes are jointly modeled within a bivariate outcome regression framework. Unobserved values of either outcome are regarded as missing data. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization algorithm. A flexible association test is provided for evaluating hypotheses about the target regression parameters. See McCaw ZR, Gaynor SM, Sun R, Lin X; “Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis” <doi:10.1101/2020.11.29.403063>.

Version:	0.5.0
Depends:	R (≥ 3.4.0)
Imports:	methods, mvnfast, plyr, Rcpp, stats
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown
Published:	2020-12-03
Author:	Zachary McCaw [aut, cre]
Maintainer:	Zachary McCaw <zmccaw at alumni.harvard.edu>
License:	GPL-3
NeedsCompilation:	yes
CRAN checks:	SurrogateRegression results

Documentation:

Reference manual:	SurrogateRegression.pdf
Vignettes:	Surrogate Outcome Regression Analysis

Downloads:

Package source:	SurrogateRegression_0.5.0.tar.gz
Windows binaries:	r-devel: SurrogateRegression_0.5.0.zip, r-release: SurrogateRegression_0.5.0.zip, r-oldrel: SurrogateRegression_0.5.0.zip
macOS binaries:	r-release (arm64): SurrogateRegression_0.5.0.tgz, r-oldrel (arm64): SurrogateRegression_0.5.0.tgz, r-release (x86_64): SurrogateRegression_0.5.0.tgz, r-oldrel (x86_64): SurrogateRegression_0.5.0.tgz

Linking:

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