The goal of ezcox is to operate a batch of univariate or multivariate Cox models and return tidy result.
You can install the released version of ezcox from CRAN with:
And the development version from GitHub with:
This is a basic example which shows you how to get result from a batch of cox models.
library(ezcox)
data("lung", package = "survival")
# Build unvariable models
ezcox(lung, covariates = c("age", "sex", "ph.ecog"))
#> => Processing variable age
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> # A tibble: 3 x 11
#> Variable contrast_level ref_level n_contrast n_ref beta HR lower_95
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 age age age 228 228 0.0187 1.02 1
#> 2 sex sex sex 228 228 -0.531 0.588 0.424
#> 3 ph.ecog ph.ecog ph.ecog 227 227 0.476 1.61 1.29
#> # … with 3 more variables: upper_95 <dbl>, p.value <dbl>,
#> # global.pval <dbl>
# Build multi-variable models
# Control variable 'age'
ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age")
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> # A tibble: 4 x 11
#> Variable contrast_level ref_level n_contrast n_ref beta HR lower_95
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 sex sex sex 228 228 -0.513 0.599 0.431
#> 2 sex age age 228 228 0.017 1.02 0.999
#> 3 ph.ecog ph.ecog ph.ecog 227 227 0.443 1.56 1.24
#> 4 ph.ecog age age 228 228 0.0113 1.01 0.993
#> # … with 3 more variables: upper_95 <dbl>, p.value <dbl>,
#> # global.pval <dbl>