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Computes association between missingness and outcome

smdi_outcome.Rd

This function fits outcome models with a covariate missingness indicator(s) of the covariates specified with covar. The estimates are computed by univariate and adjusted models on all other prognostic covariates in the dataset. Based on the underlying missingness mechanism, the estimate for the covariate missingness indicator may indicate a meaningful difference in the outcome between patients with vs w/o the observed confounder conditional on other covariates that could explain that difference.

Usage

smdi_outcome(
  data = NULL,
  covar = NULL,
  model = c("glm", "linear", "cox"),
  glm_family = NULL,
  form_lhs = NULL,
  exponentiated = FALSE,
  n_cores = 1
)

Arguments

data

dataframe or tibble object with partially observed/missing variables

covar

character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation and all remaining covariates will be used as predictors

model

character describing which outcome model to fit to assess the association between covar missingness indicator and outcome. Currently supported are models of type glm, linear and cox

glm_family

glm family object to specify a certain family of generalized linear models (e.g., binomial, gaussian, Gamma, poisson, etc.). For all options see ?stats::family

form_lhs

string specifying the left-hand side of the outcome formula (see details)

exponentiated

logical, should results be exponentiated (default is FALSE)

n_cores

integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems)

Value

returns a tibble with univariate and adjusted estimates for each partially observed covar:

  • estimate_univariate: univariate association between missingness indicator of covar and outcome

  • estimate_adjusted: association between missingness indicator of covar and outcome conditional on other fully observed covariates and missing indicator variables of other partially observed covariates

Details

The function automatically fits a univariate and adjusted outcome model. The currently supported models are glm (glm), linear (lm) and cox (survival). For adjusted models, the function uses all available covariates found in the dataset specified with the data parameter. If covariates should not be include in the outcome model, these covariates should be dropped beforehand (as with all other functions in the smdi package).

The left-hand side of the formula (form_lhs) needs to specify the outcome in one of the following ways:

  • glm (binary): character of column name with binary outcome, e.g. "MACE"

  • lm (continuous): character of column name with binary outcome, e.g. "WEIGHT_LOSS"

  • cox (time-to-event): LHS specifying time-to-event outcome, e.g. "Surv(TIME, STATUS)"

References

...

See also

glm coxph

Examples

library(smdi)

smdi_outcome(
 data = smdi_data,
 model = "cox",
 form_lhs = "Surv(eventtime, status)"
)
#> # A tibble: 3 × 3
#>   covariate estimate_univariate        estimate_adjusted         
#>   <chr>     <glue>                     <glue>                    
#> 1 ecog_cat  -0.06 (95% CI -0.16, 0.03) -0.06 (95% CI -0.16, 0.03)
#> 2 egfr_cat  0.06 (95% CI -0.03, 0.15)  -0.01 (95% CI -0.10, 0.09)
#> 3 pdl1_num  0.12 (95% CI 0.01, 0.23)   0.11 (95% CI -0.00, 0.22)