Title: Analysis of Data with Measurement Error Using a Validation
Imports: survival , dplyr , tidyr , ggplot2
Description: Analyze data with measurement error when there is a validation subsample randomly selected from the full sample.  The method assumes surrogate variables measured with error are available for the full sample, and reference variables measured with little or no error are available for this randomly chosen subsample of the full sample.  Measurement errors may be differential or non differential, in any or all predictors (simultaneously) as well as outcome.  The "augmented" estimates derived are based upon the multivariate correlation between regression model parameter estimates for the reference variables and for the surrogate variables in the validation subset.  Because the validation subsample is chosen at random whatever biases are imposed by measurement error, non-differential or differential, are reflected in this correlation and can be used to derive estimates for the reference variables using data from the whole sample.  This work derives from Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong Y, Huang J et al. (2020) <doi:10.1093/jamia/ocz180>.
Copyright: Mayo Foundation for Medical Education and Research
Author: Walter K Kremers [aut, cre] (<https://orcid.org/0000-0001-5714-3473>)
Maintainer: Walter K Kremers <kremers.walter@mayo.edu>

Description: Analyze data with measurement error when there is a validation subsample randomly selected from the full sample.  The method assumes surrogate variables measured with error are available for the full sample, and reference variables measured with little or no error are available for this randomly selected subsample of the full sample.  Measurement errors may be differential or non differential, in any or all predictors (simultaneously) as well as outcome.  The "augmented" estimates derived are based upon the multivariate correlation between regression model parameter estimates for the reference variables and for the surrogate variables in the validation subset.  Because the validation subsample is chosen at random whatever biases are imposed by measurement error, non-differential or differential, are reflected in this correlation and can be used to derive estimates for the reference variables using data from the whole sample.  The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection.  This work derives from Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020) <doi:10.1093/jamia/ocz180>.