ezcox: Easily Process a Batch of Cox Models

CRAN status Hits R-CMD-check Codecov test coverage Lifecycle: stable

The goal of ezcox is to operate a batch of univariate or multivariate Cox models and return tidy result.

:arrow_double_down: Installation

You can install the released version of ezcox from CRAN with:

install.packages("ezcox")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("ShixiangWang/ezcox")

It is possible to install ezcox from Conda conda-forge channel:

conda install r-ezcox --channel conda-forge

Visualization feature of ezcox needs the recent version of forestmodel, please run the following commands:

remotes::install_github("ShixiangWang/forestmodel")

:beginner: Example

This is a basic example which shows you how to get result from a batch of cox models.

library(ezcox)
#> Welcome to 'ezcox' package!
#> =======================================================================
#> You are using ezcox version 0.8.1
#> 
#> Github page  : https://github.com/ShixiangWang/ezcox
#> Documentation: https://shixiangwang.github.io/ezcox/articles/ezcox.html
#> 
#> Run citation("ezcox") to see how to cite 'ezcox'.
#> =======================================================================
#> 
library(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 × 12
#>   Variable is_control contrast_level ref_level n_contrast n_ref    beta    HR
#>   <chr>    <lgl>      <chr>          <chr>          <int> <int>   <dbl> <dbl>
#> 1 age      FALSE      age            age              228   228  0.0187 1.02 
#> 2 sex      FALSE      sex            sex              228   228 -0.531  0.588
#> 3 ph.ecog  FALSE      ph.ecog        ph.ecog          227   227  0.476  1.61 
#> # … with 4 more variables: lower_95 <dbl>, 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 × 12
#>   Variable is_control contrast_level ref_level n_contrast n_ref    beta    HR
#>   <chr>    <lgl>      <chr>          <chr>          <int> <int>   <dbl> <dbl>
#> 1 sex      FALSE      sex            sex              228   228 -0.513  0.599
#> 2 sex      TRUE       age            age              228   228  0.017  1.02 
#> 3 ph.ecog  FALSE      ph.ecog        ph.ecog          227   227  0.443  1.56 
#> 4 ph.ecog  TRUE       age            age              228   228  0.0113 1.01 
#> # … with 4 more variables: lower_95 <dbl>, upper_95 <dbl>, p.value <dbl>,
#> #   global.pval <dbl>
lung$ph.ecog = factor(lung$ph.ecog)
zz = ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models=TRUE)
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
#> => Processing variable ph.ecog
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.
mds = get_models(zz)
str(mds, max.level = 1)
#> List of 2
#>  $ Surv ~ sex + age    :List of 19
#>   ..- attr(*, "class")= chr "coxph"
#>   ..- attr(*, "Variable")= chr "sex"
#>  $ Surv ~ ph.ecog + age:List of 22
#>   ..- attr(*, "class")= chr "coxph"
#>   ..- attr(*, "Variable")= chr "ph.ecog"
#>  - attr(*, "class")= chr [1:2] "ezcox_models" "list"
#>  - attr(*, "has_control")= logi TRUE

show_models(mds)

:star2: Vignettes

:page_with_curl: Citation

ezcox has not been published, if you are using it in academic research, please cite: