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.
smdi_outcome(
data = NULL,
covar = NULL,
model = c("glm", "linear", "cox"),
glm_family = NULL,
form_lhs = NULL,
exponentiated = FALSE,
n_cores = 1
)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
dataframe or tibble object with partially observed/missing variables
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
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 object to specify a certain family of generalized linear models (e.g., binomial, gaussian, Gamma, poisson, etc.). For all options see ?stats::family
string specifying the left-hand side of the outcome formula (see details)
logical, should results be exponentiated (default is FALSE)
integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems)
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)"
...
library(smdi)
smdi_outcome(
data = smdi_data,
model = "cox",
form_lhs = "Surv(eventtime, status)"
)
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