[aut, cre]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 |
| Reference manual: | SurrogateRegression.pdf |
| Vignettes: |
Surrogate Outcome Regression Analysis |
| 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: SurrogateRegression_0.5.0.tgz, r-oldrel: SurrogateRegression_0.5.0.tgz |
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