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Get started with smdi

Janick Weberpals

smdi.Rmd

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 0.029 (0.003, 0.071) 0.783 0.510 -0.06 (95% CI -0.16, 0.03) -0.06 (95% CI -0.16, 0.03)
egfr_cat 0.243 (0.010, 0.485) <.001 0.629 0.06 (95% CI -0.03, 0.15) -0.01 (95% CI -0.10, 0.09)
pdl1_num 0.062 (0.019, 0.338) <.001 0.516 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)