gen_base_coefs

Generate coefficients corresponding to an imperfect risk score, interpretable
as 'baseline' behaviour in the absence of a risk score

Simulation

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (to appear in Annals of Applied Statistics) for details of methods.

James Liley

OptHoldoutSize

Estimation of Optimal Size for a Holdout Set for Updating a
Predictive Score

Sami Haidar-Wehbe

Sam Emerson

Louis Aslett

gen_base_coefs function

<dl><dt>coefs</dt>
<dd>Original coefficients</dd>
<dt>noise</dt>
<dd>Set to TRUE to add Gaussian noise to coefficients</dd>
<dt>num_vars</dt>
<dd>Number of variables at hand for baseline calculation</dd>
<dt>max_base_powers</dt>
<dd>If &gt;1, return a matrix of coefficients, with successively more noise</dd></dl>

Arguments

Coefficients for imperfect risk score — gen_base_coefs

<dl>

<dt>coefs</dt>
<dd>Original coefficients</dd>


<dt>noise</dt>
<dd>Set to TRUE to add Gaussian noise to coefficients</dd>


<dt>num_vars</dt>
<dd>Number of variables at hand for baseline calculation</dd>


<dt>max_base_powers</dt>
<dd>If &gt;1, return a matrix of coefficients, with successively more noise</dd>

</dl>

gen_base_coefs: Coefficients for imperfect risk score

Description

Usage

Value

Arguments

Examples