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Hazard ratio estimation in interactions

Source: R/coxph.R
estimate_coef.Rd

This function estimates the hazard ratios between arms when an interaction variable is given with specific values.

Usage

estimate_coef(
  variable,
  given,
  lvl_var,
  lvl_given,
  coef,
  mmat,
  vcov,
  conf_level = 0.95
)

Arguments

variable, given

(character(2))
names of the two variables in the interaction. We seek the estimation of the levels of variable given the levels of given.

lvl_var, lvl_given

(character)
corresponding levels given by levels().

coef

(numeric)
vector of estimated coefficients.

mmat

(named numeric) a vector filled with 0s used as a template to obtain the design matrix.

vcov

(matrix)
variance-covariance matrix of underlying model.

conf_level

(proportion)
confidence level of estimate intervals.

Value

A list of matrices (one per level of variable) with rows corresponding to the combinations of variable and given, with columns:

  • coef_hat: Estimation of the coefficient.

  • coef_se: Standard error of the estimation.

  • hr: Hazard ratio.

  • lcl, ucl: Lower/upper confidence limit of the hazard ratio.

Details

Given the cox regression investigating the effect of Arm (A, B, C; reference A) and Sex (F, M; reference Female). The model is abbreviated: y ~ Arm + Sex + Arm x Sex. The cox regression estimates the coefficients along with a variance-covariance matrix for:

  • b1 (arm b), b2 (arm c)

  • b3 (sex m)

  • b4 (arm b: sex m), b5 (arm c: sex m)

Given that I want an estimation of the Hazard Ratio for arm C/sex M, the estimation will be given in reference to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5), therefore the interaction coefficient is given by b2 + b5 while the standard error is obtained as $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ for a confidence level of 0.95.

See also

s_cox_multivariate().

Examples

library(dplyr)
library(survival)

ADSL <- tern_ex_adsl %>%
  filter(SEX %in% c("F", "M"))

adtte <- tern_ex_adtte %>% filter(PARAMCD == "PFS")
adtte$ARMCD <- droplevels(adtte$ARMCD)
adtte$SEX <- droplevels(adtte$SEX)

mod <- coxph(
  formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,
  data = adtte
)

mmat <- stats::model.matrix(mod)[1, ]
mmat[!mmat == 0] <- 0