smdi_data

Example dataset with partially observed covariates.

datasets

An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.

Janick Weberpals

smdi

Perform Structural Missing Data Investigations

smdi_data function

<div class="section" id="smdi-data">
<h3><code>smdi_data</code></h3>
A data frame with 2,500 rows and 14 columns:<dl>
<dt>exposure</dt>
<dd>Treatment assignment variable (binary). Indicates initiation of the exposure of interest (1) versus a comparator regimen (0)</dd><dt>age_num</dt>
<dd>Age at baseline in years</dd><dt>female_cat</dt>
<dd>Is gender female (0 = no, 1 = yes)</dd><dt>ecog_cat</dt>
<dd>ECOG performance score at baseline (0 versus 1). Shows 30% missingness following an MCAR mechanism.</dd><dt>smoking_cat</dt>
<dd>Smoking status at baseline (0 = non-smoker, 1 = smoker)</dd><dt>physical_cat</dt>
<dd>Physical activity at baseline (not active versus active)</dd><dt>egfr_cat</dt>
<dd>EGFR mutation status (0 = wild-type, 1 = alteration). Shows 20% missingness following an MAR mechanism.</dd><dt>alk_cat</dt>
<dd>ALK transolcation mutation status (0 = wild-type, 1 = alteration)</dd><dt>pdl1_num</dt>
<dd>PD-L1 cell staining biomarker in %. Shows 40% missingness following an MNAR(value) mechanism</dd><dt>histology_cat</dt>
<dd>Tumor histology (0 = nonsquamous, 1 = squamous)</dd><dt>ses_cat</dt>
<dd>Socio-economic status (multi-categorical: 1-low, 2-middle, 3-high)</dd><dt>copd_cat</dt>
<dd>COPD comorbidity at baseline</dd><dt>eventtime</dt>
<dd>time to censoring event</dd><dt>status</dt>
<dd>event indicator at time t; 0 = censored, 1 = deceased</dd>
</dl></div>

Format

smdi exemplary lung cancer dataset — smdi_data

<div class='section' id='smdi-data'>
<h3><code>smdi_data</code></h3>


A data frame with 2,500 rows and 14 columns:<dl>
<dt>exposure</dt>
<dd>Treatment assignment variable (binary). Indicates initiation of the exposure of interest (1) versus a comparator regimen (0)</dd>

<dt>age_num</dt>
<dd>Age at baseline in years</dd>

<dt>female_cat</dt>
<dd>Is gender female (0 = no, 1 = yes)</dd>

<dt>ecog_cat</dt>
<dd>ECOG performance score at baseline (0 versus 1). Shows 30% missingness following an MCAR mechanism.</dd>

<dt>smoking_cat</dt>
<dd>Smoking status at baseline (0 = non-smoker, 1 = smoker)</dd>

<dt>physical_cat</dt>
<dd>Physical activity at baseline (not active versus active)</dd>

<dt>egfr_cat</dt>
<dd>EGFR mutation status (0 = wild-type, 1 = alteration). Shows 20% missingness following an MAR mechanism.</dd>

<dt>alk_cat</dt>
<dd>ALK transolcation mutation status (0 = wild-type, 1 = alteration)</dd>

<dt>pdl1_num</dt>
<dd>PD-L1 cell staining biomarker in %. Shows 40% missingness following an MNAR(value) mechanism</dd>

<dt>histology_cat</dt>
<dd>Tumor histology (0 = nonsquamous, 1 = squamous)</dd>

<dt>ses_cat</dt>
<dd>Socio-economic status (multi-categorical: 1-low, 2-middle, 3-high)</dd>

<dt>copd_cat</dt>
<dd>COPD comorbidity at baseline</dd>

<dt>eventtime</dt>
<dd>time to censoring event</dd>

<dt>status</dt>
<dd>event indicator at time t; 0 = censored, 1 = deceased</dd>


</dl>

</div>

smdi_data: smdi exemplary lung cancer dataset

Description

Usage

Arguments

Format

`smdi_data`