tern (version 0.9.8)

h_logistic_regression: Helper functions for multivariate logistic regression

Description

Helper functions used in calculations for logistic regression.

Usage

h_get_interaction_vars(fit_glm)
h_interaction_coef_name(
  interaction_vars,
  first_var_with_level,
  second_var_with_level
)
h_or_cat_interaction(
  odds_ratio_var,
  interaction_var,
  fit_glm,
  conf_level = 0.95
)
h_or_cont_interaction(
  odds_ratio_var,
  interaction_var,
  fit_glm,
  at = NULL,
  conf_level = 0.95
)
h_or_interaction(
  odds_ratio_var,
  interaction_var,
  fit_glm,
  at = NULL,
  conf_level = 0.95
)
h_simple_term_labels(terms, table)
h_interaction_term_labels(terms1, terms2, table, any = FALSE)
h_glm_simple_term_extract(x, fit_glm)
h_glm_interaction_extract(x, fit_glm)
h_glm_inter_term_extract(odds_ratio_var, interaction_var, fit_glm, ...)
h_logistic_simple_terms(x, fit_glm, conf_level = 0.95)
h_logistic_inter_terms(x, fit_glm, conf_level = 0.95, at = NULL)

Value

Vector of names of interaction variables.

Name of coefficient.

Odds ratio.

Odds ratio.

Odds ratio.

Term labels containing numbers of patients.

Term labels containing numbers of patients.

Tabulated main effect results from a logistic regression model.

Tabulated interaction term results from a logistic regression model.

A data.frame of tabulated interaction term results from a logistic regression model.

Tabulated statistics for the given variable(s) from the logistic regression model.

Tabulated statistics for the given variable(s) from the logistic regression model.

Arguments

fit_glm

(glm)
logistic regression model fitted by stats::glm() with "binomial" family. Limited functionality is also available for conditional logistic regression models fitted by survival::clogit(), currently this is used only by extract_rsp_biomarkers().

interaction_vars

(character(2))
interaction variable names.

first_var_with_level

(character(2))
the first variable name with the interaction level.

second_var_with_level

(character(2))
the second variable name with the interaction level.

odds_ratio_var

(string)
the odds ratio variable.

interaction_var

(string)
the interaction variable.

conf_level

(proportion)
confidence level of the interval.

at

(numeric or NULL)
optional values for the interaction variable. Otherwise the median is used.

terms

(character)
simple terms.

table

(table)
table containing numbers for terms.

terms1

(character)
terms for first dimension (rows).

terms2

(character)
terms for second dimension (rows).

any

(flag)
whether any of term1 and term2 can be fulfilled to count the number of patients. In that case they can only be scalar (strings).

x

(character)
a variable or interaction term in fit_glm (depending on the helper function used).

...

additional arguments for the lower level functions.

Functions

• h_get_interaction_vars(): Helper function to extract interaction variable names from a fitted model assuming only one interaction term.

• h_interaction_coef_name(): Helper function to get the right coefficient name from the interaction variable names and the given levels. The main value here is that the order of first and second variable is checked in the interaction_vars input.

• h_or_cat_interaction(): Helper function to calculate the odds ratio estimates for the case when both the odds ratio and the interaction variable are categorical.

• h_or_cont_interaction(): Helper function to calculate the odds ratio estimates for the case when either the odds ratio or the interaction variable is continuous.

• h_or_interaction(): Helper function to calculate the odds ratio estimates in case of an interaction. This is a wrapper for h_or_cont_interaction() and h_or_cat_interaction().

• h_simple_term_labels(): Helper function to construct term labels from simple terms and the table of numbers of patients.

• h_interaction_term_labels(): Helper function to construct term labels from interaction terms and the table of numbers of patients.

• h_glm_simple_term_extract(): Helper function to tabulate the main effect results of a (conditional) logistic regression model.

• h_glm_interaction_extract(): Helper function to tabulate the interaction term results of a logistic regression model.

• h_glm_inter_term_extract(): Helper function to tabulate the interaction results of a logistic regression model. This basically is a wrapper for h_or_interaction() and h_glm_simple_term_extract() which puts the results in the right data frame format.

• h_logistic_simple_terms(): Helper function to tabulate the results including odds ratios and confidence intervals of simple terms.

• h_logistic_inter_terms(): Helper function to tabulate the results including odds ratios and confidence intervals of interaction terms.

Examples

library(dplyr)
library(broom)

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")
  )
)
mod2 <- fit_logistic(
  data = adrs_f,
  variables = list(
    response = "Response",
    arm = "ARMCD",
    covariates = c("AGE", "RACE"),
    interaction = "AGE"
  )
)

h_glm_simple_term_extract("AGE", mod1)
h_glm_simple_term_extract("ARMCD", mod1)

h_glm_interaction_extract("ARMCD:AGE", mod2)

h_glm_inter_term_extract("AGE", "ARMCD", mod2)

h_logistic_simple_terms("AGE", mod1)

h_logistic_inter_terms(c("RACE", "AGE", "ARMCD", "AGE:ARMCD"), mod2)