samba.df

'samba.df' is the sampled data from the entire population

datasets

Health research using data from electronic health records (EHR) has gained
popularity, but misclassification of EHR-derived disease status and lack of
representativeness of the study sample can result in substantial bias in
effect estimates and can impact power and type I error for association
tests. Here, the assumed target of inference is the relationship between
binary disease status and predictors modeled using a logistic regression
model. 'SAMBA' implements several methods for obtaining bias-corrected
point estimates along with valid standard errors as proposed in Beesley and
Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.

Alexander Rix

SAMBA

Selection and Misclassification Bias Adjustment for Logistic
Regression Models

Lauren Beesley

samba.df function

A synthetic data.frame with 4999 observations on 5 variables:<dl class="dl-horizontal">
 <dt>X</dt><dd>Covariate for sensitivity model.</dd>
 <dt>Z</dt><dd>Covariate for disease model.</dd>
 <dt>W</dt><dd>Selection Covariate</dd>
 <dt>D</dt><dd>True disease status.</dd>
 <dt>Dstar</dt><dd>Observed disease status.</dd>
</dl>

Format

Synthetic example data for SAMBA adapted from the vignette — samba.df

A synthetic data.frame with 4999 observations on 5 variables:<dl class='dl-horizontal'>
 <dt>X</dt><dd>Covariate for sensitivity model.</dd>
 <dt>Z</dt><dd>Covariate for disease model.</dd>
 <dt>W</dt><dd>Selection Covariate</dd>
 <dt>D</dt><dd>True disease status.</dd>
 <dt>Dstar</dt><dd>Observed disease status.</dd>
</dl>

samba.df: Synthetic example data for SAMBA adapted from the vignette

Description

Usage

Arguments

Format