Covariate Adjusted and Stratified Survival Analysis

Calculate log-rank test as well as hazard ratio estimates for survival data, optionally adjusted for covariates and a stratification factor.

Usage

robin_surv(
  formula,
  data,
  treatment,
  comparisons,
  contrast = c("hazardratio", "none"),
  test = "logrank",
  ...
)

Arguments

formula: (formula) A formula of analysis, of the form Surv(time, status) ~ covariates + strata(x, y, z). If no covariates should be adjusted for, use 1 instead on the right hand side. The intercept must not be removed. If no stratification factors should be used for the analysis, do not use strata() in the formula.
data: (data.frame) Input data frame.
treatment: (formula) A formula of treatment assignment or assignment by stratification, of the form treatment ~ scheme(vars). Note that currently the randomization scheme is not used in the analysis. However, any variables that were used in the randomization scheme must be included in the model formula, either as covariates, or as strata().
comparisons: (list) An optional list of comparisons between treatment levels to be performed, see details. By default, all pairwise comparisons are performed automatically.
contrast: (character(1)) The contrast statistic to be used, currently only "hazardratio" is supported. Can be disabled by specifying "none", in which case only the log-rank test is performed.
test: (character(1)) The test to be used, currently only "logrank" is supported.
...: Additional arguments passed to the survival analysis functions, in particular hr_se_plugin_adjusted (please see here for details).

Value

A surv_effect object containing the results of the survival analysis.

Details

The user can optionally specify a list of comparisons between treatment levels to be performed. The list must have two elements:

Treatment level indices of the treatment group.
Treatment level indices of the control group.

So for example if you would like to compare level 3 with level 1, and also level 3 with level 2 (but not level 2 with level 1) then you can specify: comparisons = list(c(3, 3), c(1, 2))

Examples

# Adjusted for covariates meal.cal and age and adjusted for stratification by strata:
robin_surv(
  formula = Surv(time, status) ~ meal.cal + age + strata(strata),
  data = surv_data,
  treatment = sex ~ pb(strata)
)
#> Model        :  Surv(time, status) ~ meal.cal + age + strata(strata) 
#> Randomization:  sex ~ pb(strata)  ( Permuted-Block )
#> 
#> Contrast     :  Log Hazard ratio
#> 
#>                  Estimate Std.Err Z Value Pr(>|z|)   
#> Male v.s. Female  0.55219 0.19133  2.8861   0.0039 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Test         :  Log-Rank
#> 
#>                  Test Stat. Pr(>|z|)   
#> Male v.s. Female     2.9496 0.003181 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Adjusted for stratification by strata and ecog but not for covariates:
robin_surv(
  formula = Surv(time, status) ~ 1 + strata(strata, ecog),
  data = surv_data,
  treatment = sex ~ sr(1)
)
#> Model        :  Surv(time, status) ~ 1 + strata(strata, ecog) 
#> Randomization:  sex ~ sr(1)  ( Simple )
#> 
#> Contrast     :  Log Hazard ratio
#> 
#>                  Estimate Std.Err Z Value Pr(>|z|)   
#> Male v.s. Female  0.55482 0.17063  3.2516 0.001147 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Test         :  Log-Rank
#> 
#>                  Test Stat. Pr(>|z|)   
#> Male v.s. Female     3.2856 0.001018 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Unadjusted for covariates and stratification:
robin_surv(
  formula = Surv(time, status) ~ 1,
  data = surv_data,
  treatment = sex ~ sr(1)
)
#> Model        :  Surv(time, status) ~ 1 
#> Randomization:  sex ~ sr(1)  ( Simple )
#> 
#> Contrast     :  Log Hazard ratio
#> 
#>                  Estimate Std.Err Z Value Pr(>|z|)   
#> Male v.s. Female  0.53343 0.16727   3.189 0.001428 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Test         :  Log-Rank
#> 
#>                  Test Stat. Pr(>|z|)   
#> Male v.s. Female     3.2135 0.001311 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1