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Get started with smdi

Janick Weberpals

library(smdi)
library(gt)

smdi_diagnose() - the flagship function

The smdi main function is smdi_diagnose() which calls all three group diagnostics, all of which are also accessible individually.

smdi_diagnose() builds on theoretical concepts developed and validated in a comprehensive simulation study based on the workstream:

Approaches to Handling Partially Observed Confounder Data From Electronic Health Records (EHR) In Non-randomized Studies of Medication Outcomes.

A most minimal example could look like this (if you want to accept all of the default parameters).

smdi_diagnose(
  data = smdi_data,
  covar = NULL, # NULL includes all covariates with at least one NA
  model = "cox",
  form_lhs = "Surv(eventtime, status)"
  ) %>% 
  smdi_style_gt()
Covariate ASMD (min/max)1 p Hotelling1 AUC2 beta univariate (95% CI)3 beta (95% CI)3
ecog_cat 0.030 (0.003, 0.071) 0.783 0.522 -0.06 (95% CI -0.16, 0.03) -0.06 (95% CI -0.16, 0.03)
egfr_cat 0.216 (0.010, 0.485) <.001 0.613 0.06 (95% CI -0.03, 0.15) -0.01 (95% CI -0.10, 0.09)
pdl1_num 0.056 (0.016, 0.338) <.001 0.517 0.12 (95% CI 0.01, 0.23) 0.11 (95% CI -0.00, 0.22)
p little: <.001, Abbreviations: ASMD = Median absolute standardized mean difference across all covariates, AUC = Area under the curve, beta = beta coefficient, CI = Confidence interval, max = Maximum, min = Minimum
1 Group 1 diagnostic: Differences in patient characteristics between patients with and without covariate
2 Group 2 diagnostic: Ability to predict missingness
3 Group 3 diagnostic: Assessment if missingness is associated with the outcome (univariate, adjusted)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.