ScoreBNEM

Performs a Score test of the null hypothesis that a subset of the regression
parameters for the target outcome are zero.

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>.

Zachary McCaw

SurrogateRegression

Surrogate Outcome Regression Analysis

ScoreBNEM function

<dl><dt>t</dt>
<dd>Target outcome vector.</dd>
<dt>s</dt>
<dd>Surrogate outcome vector.</dd>
<dt>X</dt>
<dd>Target model matrix.</dd>
<dt>Z</dt>
<dd>Surrogate model matrix.</dd>
<dt>is_zero</dt>
<dd>Logical vector, with as many entires as columns in the target model
matrix, indicating which columns have coefficient zero under the null.</dd>
<dt>init</dt>
<dd>Optional list of initial parameters for fitting the null model.</dd>
<dt>maxit</dt>
<dd>Maximum number of parameter updates.</dd>
<dt>eps</dt>
<dd>Minimum acceptable improvement in log likelihood.</dd>
<dt>report</dt>
<dd>Report model fitting progress? Default is FALSE.</dd></dl>

Arguments

Score Test via Expectation Maximization. — ScoreBNEM

<dl>

<dt>t</dt>
<dd>Target outcome vector.</dd>


<dt>s</dt>
<dd>Surrogate outcome vector.</dd>


<dt>X</dt>
<dd>Target model matrix.</dd>


<dt>Z</dt>
<dd>Surrogate model matrix.</dd>


<dt>is_zero</dt>
<dd>Logical vector, with as many entires as columns in the target model
matrix, indicating which columns have coefficient zero under the null.</dd>


<dt>init</dt>
<dd>Optional list of initial parameters for fitting the null model.</dd>


<dt>maxit</dt>
<dd>Maximum number of parameter updates.</dd>


<dt>eps</dt>
<dd>Minimum acceptable improvement in log likelihood.</dd>


<dt>report</dt>
<dd>Report model fitting progress? Default is FALSE.</dd>

</dl>

ScoreBNEM: Score Test via Expectation Maximization.

Description

Usage

Value

Arguments