R/smdi_data.R

In smdi: Perform Structural Missing Data Investigations

#' smdi exemplary lung cancer dataset
#'
#' Example dataset with partially observed covariates.
#'
#' @format ## `smdi_data`
#' A data frame with 2,500 rows and 14 columns:
#' \describe{
#'   \item{exposure}{Treatment assignment variable (binary). Indicates initiation of the exposure of interest (1) versus a comparator regimen (0)}
#'   \item{age_num}{Age at baseline in years}
#'   \item{female_cat}{Is gender female (0 = no, 1 = yes)}
#'   \item{ecog_cat}{ECOG performance score at baseline (0 versus 1). Shows 30% missingness following an MCAR mechanism.}
#'   \item{smoking_cat}{Smoking status at baseline (0 = non-smoker, 1 = smoker)}
#'   \item{physical_cat}{Physical activity at baseline (not active versus active)}
#'   \item{egfr_cat}{EGFR mutation status (0 = wild-type, 1 = alteration). Shows 20% missingness following an MAR mechanism.}
#'   \item{alk_cat}{ALK transolcation mutation status (0 = wild-type, 1 = alteration)}
#'   \item{pdl1_num}{PD-L1 cell staining biomarker in %. Shows 40% missingness following an MNAR(value) mechanism}
#'   \item{histology_cat}{Tumor histology (0 = nonsquamous, 1 = squamous)}
#'   \item{ses_cat}{Socio-economic status (multi-categorical: 1-low, 2-middle, 3-high)}
#'   \item{copd_cat}{COPD comorbidity at baseline}
#'   \item{eventtime}{time to censoring event}
#'   \item{status}{event indicator at time t; 0 = censored, 1 = deceased}
#' }
#' @source https://janickweberpals.gitlab-pages.partners.org/smdi/articles/data_generation.html
"smdi_data"