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Fit for logistic regression — fit_logistic • tern

Fit a (conditional) logistic regression model.

fit_logistic(
  data,
  variables = list(response = "Response", arm = "ARMCD", covariates = NULL, interaction =
    NULL, strata = NULL),
  response_definition = "response"
)

data

(data.frame)
the data frame on which the model was fit.

variables

(named list of string)
list of additional analysis variables.

response_definition

(string)
the definition of what an event is in terms of response. This will be used when fitting the (conditional) logistic regression model on the left hand side of the formula.

A fitted logistic regression model.

The variables list needs to include the following elements:

• arm: Treatment arm variable name.

• response: The response arm variable name. Usually this is a 0/1 variable.

• covariates: This is either NULL (no covariates) or a character vector of covariate variable names.

• interaction: This is either NULL (no interaction) or a string of a single covariate variable name already included in covariates. Then the interaction with the treatment arm is included in the model.

library(dplyr)

adrs_f <- tern_ex_adrs %>%
  filter(PARAMCD == "BESRSPI") %>%
  filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
  mutate(
    Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
    RACE = factor(RACE),
    SEX = factor(SEX)
  )
formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
mod1 <- fit_logistic(
  data = adrs_f,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("AGE", "RACE")
  )
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
mod2 <- fit_logistic(
  data = adrs_f,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("AGE", "RACE"),
    interaction = "AGE"
  )
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

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