Surrogate Outcome Regression Analysis
Bivariate Regression Model
Show for Bivariate Regression Model
Check Initiation
Check Test Specification
Extract Coefficients from Bivariate Regression Model
Covariance Information Matrix
Tabulate Covariance Parameters
Covariate Update
Fit Bivariate Normal Regression Model via Expectation Maximization.
Fit Bivariate Normal Regression Model via Least Squares
Fit Bivariate Normal Regression Model
Ordinary Least Squares
Format Output
Update Iteration
Matrix Determinant
Matrix Inverse
Matrix Inner Product
Matrix Outer Product
Quadratic Form
Matrix Matrix Product
Observed Data Log Likelihood
Parameter Initialization
Partition Data by Outcome Missingness Pattern.
Print for Bivariate Regression Model
Simulate Bivariate Normal Data with Missingness
Regression Information
Tabulate Regression Coefficients
Regression Update
Extract Residuals from Bivariate Regression Model
Schur complement
Score Test via Expectation Maximization.
SurrogateRegression: Surrogate Outcome Regression Analysis
Test Bivariate Normal Regression Model.
Matrix Trace
EM Update
Extract Covariance Matrix from Bivariate Normal Regression Model
Wald Test via Expectation Maximization.
Wald Test via Least Squares.
Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). 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. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" <doi:10.1111/biom.13629>.