ezcox: Easily Process a Batch of Cox Models

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

Installation

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

install.packages("ezcox")

And the development version from GitHub with:

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

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

remotes::install_github("ShixiangWang/forestmodel")

Example

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

library(survival)
library(ezcox)
#> Welcome to 'ezcox' package!
#> =======================================================================
#> You are using ezcox version 1.0.4
#> 
#> Project home : https://github.com/ShixiangWang/ezcox
#> Documentation: https://shixiangwang.github.io/ezcox
#> Cite as      : arXiv:2110.14232
#> =======================================================================
#> 
data(lung)
#> Warning in data(lung): data set 'lung' not found
head(lung)
#>   inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
#> 1    3  306      2  74   1       1       90       100     1175      NA
#> 2    3  455      2  68   1       0       90        90     1225      15
#> 3    3 1010      1  56   1       0       90        90       NA      15
#> 4    5  210      2  57   1       1       90        60     1150      11
#> 5    1  883      2  60   1       0      100        90       NA       0
#> 6   12 1022      1  74   1       1       50        80      513       0

# 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_cont…¹ contr…² ref_l…³ n_con…⁴ n_ref    beta    HR lower…⁵ upper…⁶
#>   <chr>    <lgl>     <chr>   <chr>     <int> <int>   <dbl> <dbl>   <dbl>   <dbl>
#> 1 age      FALSE     age     age         228   228  0.0187 1.02    1       1.04 
#> 2 sex      FALSE     sex     sex         228   228 -0.531  0.588   0.424   0.816
#> 3 ph.ecog  FALSE     ph.ecog ph.ecog     227   227  0.476  1.61    1.29    2.01 
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹is_control, ²contrast_level, ³ref_level, ⁴n_contrast,
#> #   ⁵lower_95, ⁶upper_95

# 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_cont…¹ contr…² ref_l…³ n_con…⁴ n_ref    beta    HR lower…⁵ upper…⁶
#>   <chr>    <lgl>     <chr>   <chr>     <int> <int>   <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE     sex     sex         228   228 -0.513  0.599   0.431   0.831
#> 2 sex      TRUE      age     age         228   228  0.017  1.02    0.999   1.04 
#> 3 ph.ecog  FALSE     ph.ecog ph.ecog     227   227  0.443  1.56    1.24    1.96 
#> 4 ph.ecog  TRUE      age     age         228   228  0.0113 1.01    0.993   1.03 
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹is_control, ²contrast_level, ³ref_level, ⁴n_contrast,
#> #   ⁵lower_95, ⁶upper_95

Run parallelly

For parallel computation, users can use ezcox_parallel(). This function has same arguments as ezcox(). For variables < 200, this function is not recommended.

ezcox_parallel(lung, covariates = c("sex", "ph.ecog"), controls = "age")
#> Loading required namespace: furrr
#> # A tibble: 4 × 12
#>   Variable is_cont…¹ contr…² ref_l…³ n_con…⁴ n_ref    beta    HR lower…⁵ upper…⁶
#>   <chr>    <lgl>     <chr>   <chr>     <int> <int>   <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE     sex     sex         228   228 -0.513  0.599   0.431   0.831
#> 2 sex      TRUE      age     age         228   228  0.017  1.02    0.999   1.04 
#> 3 ph.ecog  FALSE     ph.ecog ph.ecog     227   227  0.443  1.56    1.24    1.96 
#> 4 ph.ecog  TRUE      age     age         228   228  0.0113 1.01    0.993   1.03 
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹is_control, ²contrast_level, ³ref_level, ⁴n_contrast,
#> #   ⁵lower_95, ⁶upper_95

Filter

Sometimes, we may need to filter result from multi-variable models.

lung$ph.ecog = factor(lung$ph.ecog)
zz = ezcox(lung, covariates = "sex", controls = "ph.ecog")
#> => Processing variable sex
#> ==> Building Surv object...
#> ==> Building Cox model...
#> ==> Done.

zz
#> # A tibble: 4 × 12
#>   Variable is_control contr…¹ ref_l…² n_con…³ n_ref   beta    HR lower…⁴ upper…⁵
#>   <chr>    <lgl>      <chr>   <chr>     <dbl> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE      sex     sex         228   228 -0.545  0.58   0.417   0.806
#> 2 sex      TRUE       1       0           113    63  0.418  1.52   1.03    2.25 
#> 3 sex      TRUE       2       0            50    63  0.947  2.58   1.66    4.01 
#> 4 sex      TRUE       3       0             1    63  2.05   7.76   1.04   58    
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹contrast_level, ²ref_level, ³n_contrast, ⁴lower_95,
#> #   ⁵upper_95

# At default, it will drop all control variables
filter_ezcox(zz)
#> # A tibble: 1 × 12
#>   Variable is_control contr…¹ ref_l…² n_con…³ n_ref   beta    HR lower…⁴ upper…⁵
#>   <chr>    <lgl>      <chr>   <chr>     <dbl> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE      sex     sex         228   228 -0.545  0.58   0.417   0.806
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹contrast_level, ²ref_level, ³n_contrast, ⁴lower_95,
#> #   ⁵upper_95

# You can specify levels to filter out
filter_ezcox(zz, c("0", "2"))
#> Filtering control levels in 'both' mode:
#>  0, 2
#> # A tibble: 1 × 12
#>   Variable is_control contr…¹ ref_l…² n_con…³ n_ref   beta    HR lower…⁴ upper…⁵
#>   <chr>    <lgl>      <chr>   <chr>     <dbl> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE      sex     sex         228   228 -0.545  0.58   0.417   0.806
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹contrast_level, ²ref_level, ³n_contrast, ⁴lower_95,
#> #   ⁵upper_95
filter_ezcox(zz, c("0", "2"), type = "contrast")
#> Filtering control levels in 'contrast' mode:
#>  0, 2
#> # A tibble: 3 × 12
#>   Variable is_control contr…¹ ref_l…² n_con…³ n_ref   beta    HR lower…⁴ upper…⁵
#>   <chr>    <lgl>      <chr>   <chr>     <dbl> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE      sex     sex         228   228 -0.545  0.58   0.417   0.806
#> 2 sex      TRUE       1       0           113    63  0.418  1.52   1.03    2.25 
#> 3 sex      TRUE       3       0             1    63  2.05   7.76   1.04   58    
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹contrast_level, ²ref_level, ³n_contrast, ⁴lower_95,
#> #   ⁵upper_95
filter_ezcox(zz, c("0", "2"), type = "ref")
#> Filtering control levels in 'ref' mode:
#>  0, 2
#> # A tibble: 1 × 12
#>   Variable is_control contr…¹ ref_l…² n_con…³ n_ref   beta    HR lower…⁴ upper…⁵
#>   <chr>    <lgl>      <chr>   <chr>     <dbl> <dbl>  <dbl> <dbl>   <dbl>   <dbl>
#> 1 sex      FALSE      sex     sex         228   228 -0.545  0.58   0.417   0.806
#> # … with 2 more variables: p.value <dbl>, global.pval <dbl>, and abbreviated
#> #   variable names ¹contrast_level, ²ref_level, ³n_contrast, ⁴lower_95,
#> #   ⁵upper_95

# More see ?filter_ezcox

Get models

Get raw models may help users understand the detail and do further visualization.

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

show_models(mds)
#> Warning in recalculate_width_panels(panel_positions, mapped_text =
#> mapped_text, : Unable to resize forest panel to be smaller than its heading;
#> consider a smaller text size

# Set model names
show_models(mds, model_names = paste0("Model ", 1:2))
#> Warning in recalculate_width_panels(panel_positions, mapped_text =
#> mapped_text, : Unable to resize forest panel to be smaller than its heading;
#> consider a smaller text size

# Merge all models and drop control variables
show_models(mds, merge_models = TRUE, drop_controls = TRUE)
#> covariates=NULL but drop_controls=TRUE, detecting controls...
#> Yes. Setting variables to keep...
#> Done.
#> Warning in recalculate_width_panels(panel_positions, mapped_text =
#> mapped_text, : Unable to resize forest panel to be smaller than its heading;
#> consider a smaller text size

More see ?show_models.

Citation

Shixiang Wang (2021). ezcox: Easily Process a Batch of Cox Models. R package version 1.0.0. https://github.com/ShixiangWang/ezcox

Shixiang Wang SYSUCC