Title: Analysis of Data with Measurement Error Using a Validation
        Subsample
Package: meerva
Version: 0.2-2
Date: 2021-10-26
Depends: R (>= 3.4.0)
Imports: survival , dplyr , tidyr , ggplot2 , mvtnorm , matrixcalc
ByteCompile: Yes
Authors@R: c(person(c("Walter", "K"), "Kremers",
                    email="kremers.walter@mayo.edu",
                    role=c("aut", "cre"),
                    comment = c(ORCID = "0000-0001-5714-3473") ))
Description: Sometimes data for analysis are obtained using more
        convenient or less expensive means yielding "surrogate"
        variables for what could be obtained more accurately, albeit
        with less convenience; or less conveniently or at more expense
        yielding "reference" variables, thought of as being measured
        without error. Analysis of the surrogate variables measured
        with error generally yields biased estimates when the objective
        is to make inference about the reference variables. Often it is
        thought that ignoring the measurement error in surrogate
        variables only biases effects toward the null hypothesis, but
        this need not be the case. Measurement errors may bias
        parameter estimates either toward or away from the null
        hypothesis.  If one has a data set with surrogate variable data
        from the full sample, and also reference variable data from a
        randomly selected subsample, then one can assess the bias
        introduced by measurement error in parameter estimation, and
        use this information to derive improved estimates based upon
        all available data. Formulaically these estimates based upon
        the reference variables from the validation subsample combined
        with the surrogate variables from the whole sample can be
        interpreted as starting with the estimate from reference
        variables in the validation subsample, and "augmenting" this
        with additional information from the surrogate variables. This
        suggests the term "augmented" estimate.  The meerva package
        calculates these augmented estimates in the regression setting
        when there is a randomly selected subsample with both surrogate
        and reference variables. Measurement errors may be differential
        or non-differential, in any or all predictors (simultaneously)
        as well as outcome. The augmented estimates derive, in part,
        from the multivariate correlation between regression model
        parameter estimates from the reference variables and the
        surrogate variables, both from the validation subset. Because
        the validation subsample is chosen at random any biases imposed
        by measurement error, whether non-differential or differential,
        are reflected in this correlation and these correlations 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.  The augmented
        estimates, as well as how measurement error may arise in
        practice, is described in more detail by Kremers WK (2021)
        <arXiv:2106.14063> and is an extension of the works by 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>.
License: GPL-3
NeedsCompilation: no
Copyright: Mayo Foundation for Medical Education and Research
RoxygenNote: 7.1.2
Suggests: R.rsp
VignetteBuilder: R.rsp
Packaged: 2026-09-25 11:31:29 UTC; root
Author: Walter K Kremers [aut, cre]
        (<https://orcid.org/0000-0001-5714-3473>)
Maintainer: Walter K Kremers <kremers.walter@mayo.edu>
Repository: https://cran.r-universe.dev
Date/Publication: 2021-10-27 09:30:06 UTC
RemoteUrl: https://github.com/cran/meerva
RemoteRef: HEAD
RemoteSha: 96d0a13964ed65971a3265048efc9325364110c5
Built: R 4.6.0; ; 2026-09-25 11:33:35 UTC; unix
