Package 'ezcox' reference manual

Title:	Easily Process a Batch of Cox Models
Description:	A tool to operate a batch of univariate or multivariate Cox models and return tidy result.
Authors:	Shixiang Wang [aut, cre]
Maintainer:	Shixiang Wang <[email protected]>
License:	GPL-3
Version:	1.0.4
Built:	2024-11-19 04:56:28 UTC
Source:	https://github.com/ShixiangWang/ezcox

Clean ezcox Model File Directory

Description

Clean ezcox Model File Directory

Usage

clean_model_dir(model_dir = file.path(tempdir(), "ezcox"))
clean_model_dir(model_dir = file.path(tempdir(), "ezcox"))

Arguments

model_dir

a path for storing model results.

Value

nothing

Examples


clean_model_dir()

clean_model_dir()

Run Cox Analysis in Batch Mode

Description

Run Cox Analysis in Batch Mode

Usage

ezcox(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  global_method = c("likelihood", "wald", "logrank"),
  keep_models = FALSE,
  return_models = FALSE,
  model_dir = file.path(tempdir(), "ezcox"),
  verbose = TRUE,
  ...
)
ezcox(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  global_method = c("likelihood", "wald", "logrank"),
  keep_models = FALSE,
  return_models = FALSE,
  model_dir = file.path(tempdir(), "ezcox"),
  verbose = TRUE,
  ...
)

Arguments

`data`	a `data.frame` containing variables, time and os status.
`covariates`	column names specifying variables.
`controls`	column names specifying controls. The names with pattern "*:\|()" will be treated as interaction/combination term, please make sure all column names in `data` are valid R variable names.
`time`	column name specifying time, default is 'time'.
`status`	column name specifying event status, default is 'status'.
`global_method`	method used to obtain global p value for cox model, should be one of "likelihood", "wald", "logrank". The likelihood-ratio test, Wald test, and score logrank statistics. These three methods are asymptotically equivalent. For large enough N, they will give similar results. For small N, they may differ somewhat. The Likelihood ratio test has better behavior for small sample sizes, so it is generally preferred.
`keep_models`	If `TRUE`, keep models as local files.
`return_models`	default `FALSE`. If `TRUE`, return a `list` contains cox models.
`model_dir`	a path for storing model results.
`verbose`	if `TRUE`, print extra info.
`...`	other parameters passing to `survival::coxph()`.

Value

a ezcox object

Author(s)

Shixiang Wang [email protected]

Examples

library(survival)

# Build unvariable models
t1 <- ezcox(lung, covariates = c("age", "sex", "ph.ecog"))
t1

# Build multi-variable models
# Control variable 'age'
t2 <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age")
t2

# Return models
t3 <- ezcox(lung,
  covariates = c("age", "sex", "ph.ecog"),
  return_models = TRUE
)
t3
t4 <- ezcox(lung,
  covariates = c("sex", "ph.ecog"), controls = "age",
  return_models = TRUE
)
t4
library(survival)

# Build unvariable models
t1 <- ezcox(lung, covariates = c("age", "sex", "ph.ecog"))
t1

# Build multi-variable models
# Control variable 'age'
t2 <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age")
t2

# Return models
t3 <- ezcox(lung,
  covariates = c("age", "sex", "ph.ecog"),
  return_models = TRUE
)
t3
t4 <- ezcox(lung,
  covariates = c("sex", "ph.ecog"), controls = "age",
  return_models = TRUE
)
t4

Group Cox Analysis and Visualization

Description

Group Cox Analysis and Visualization

Usage

ezcox_group(
  data,
  grp_var,
  covariate,
  controls = NULL,
  time = "time",
  status = "status",
  sort = FALSE,
  decreasing = TRUE,
  add_all = FALSE,
  add_caption = TRUE,
  verbose = TRUE,
  headings = list(variable = "Group", n = "N", measure = "Hazard ratio", ci = NULL, p =
    "p"),
  ...
)
ezcox_group(
  data,
  grp_var,
  covariate,
  controls = NULL,
  time = "time",
  status = "status",
  sort = FALSE,
  decreasing = TRUE,
  add_all = FALSE,
  add_caption = TRUE,
  verbose = TRUE,
  headings = list(variable = "Group", n = "N", measure = "Hazard ratio", ci = NULL, p =
    "p"),
  ...
)

Arguments

`data`	a `data.frame` containing variables, time and os status.
`grp_var`	a group column.
`covariate`	a covariable for cox analysis.
`controls`	column names specifying controls. The names with pattern "*:\|()" will be treated as interaction/combination term, please make sure all column names in `data` are valid R variable names.
`time`	column name specifying time, default is 'time'.
`status`	column name specifying event status, default is 'status'.
`sort`	if `TRUE`, sort the models by the HR values.
`decreasing`	logical, should the sort order be increasing or decreasing?
`add_all`	if `TRUE`, add a group for all data rows.
`add_caption`	if `TRUE`, add caption to the plot.
`verbose`	if `TRUE`, print extra info.
`headings`	a `list` for setting the heading text.
`...`	other arguments passing to `forestmodel::forest_model()`.

Value

a list.

Examples

library(survival)
ezcox_group(lung, grp_var = "sex", covariate = "ph.ecog")
ezcox_group(lung, grp_var = "sex", covariate = "ph.ecog", controls = "age")
p <- ezcox_group(lung,
  grp_var = "sex", covariate = "ph.ecog",
  controls = "age", add_all = TRUE
)
library(survival)
ezcox_group(lung, grp_var = "sex", covariate = "ph.ecog")
ezcox_group(lung, grp_var = "sex", covariate = "ph.ecog", controls = "age")
p <- ezcox_group(lung,
  grp_var = "sex", covariate = "ph.ecog",
  controls = "age", add_all = TRUE
)

Parallelly Run Cox Analysis in Batch Mode

Description

Parallelly Run Cox Analysis in Batch Mode

Usage

ezcox_parallel(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  batch_size = 100,
  global_method = c("likelihood", "wald", "logrank"),
  keep_models = FALSE,
  return_models = FALSE,
  model_dir = file.path(tempdir(), "ezcox"),
  parallel = TRUE,
  verbose = FALSE
)
ezcox_parallel(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  batch_size = 100,
  global_method = c("likelihood", "wald", "logrank"),
  keep_models = FALSE,
  return_models = FALSE,
  model_dir = file.path(tempdir(), "ezcox"),
  parallel = TRUE,
  verbose = FALSE
)

Arguments

`data`	a `data.frame` containing variables, time and os status.
`covariates`	column names specifying variables.
`controls`	column names specifying controls.
`time`	column name specifying time, default is 'time'.
`status`	column name specifying event status, default is 'status'.
`batch_size`	processing size in a batch.
`global_method`	method used to obtain global p value for cox model, should be one of "likelihood", "wald", "logrank". The likelihood-ratio test, Wald test, and score logrank statistics. These three methods are asymptotically equivalent. For large enough N, they will give similar results. For small N, they may differ somewhat. The Likelihood ratio test has better behavior for small sample sizes, so it is generally preferred.
`keep_models`	If `TRUE`, keep models as local files.
`return_models`	default `FALSE`. If `TRUE`, return a `list` contains cox models.
`model_dir`	a path for storing model results.
`parallel`	if `TRUE`, do parallel computation by furrr package.
`verbose`	if `TRUE`, print extra info. If `parallel` is `TRUE`, set `verbose` to `FALSE` may speed up.

Value

a ezcox object

Author(s)

Shixiang Wang [email protected]

Examples

library(survival)
t <- ezcox_parallel(lung, covariates = c("sex", "ph.ecog"), controls = "age")
t
library(survival)
t <- ezcox_parallel(lung, covariates = c("sex", "ph.ecog"), controls = "age")
t

Filter ezcox

Description

Filter ezcox

Usage

filter_ezcox(x, levels = "auto", type = c("both", "contrast", "ref"))
filter_ezcox(x, levels = "auto", type = c("both", "contrast", "ref"))

Arguments

`x`	a `ezcox` object from `ezcox()`.
`levels`	levels to filter, default is 'auto', it will filter all control variables.
`type`	default is 'both' for filtering both contrast level and reference level. It can also be 'contrast' for filtering only contrast level and 'ref' for filtering only reference level.

Value

a ezcox object

Author(s)

Shixiang Wang [email protected]

Examples

library(survival)
lung$ph.ecog <- factor(lung$ph.ecog)
zz <- ezcox(lung, covariates = c("sex", "age"), controls = "ph.ecog")
zz
filter_ezcox(zz)
filter_ezcox(zz, c("0", "2"))
filter_ezcox(zz, c("0", "2"), type = "contrast")
t <- filter_ezcox(zz, c("0", "2"), type = "ref")
t
library(survival)
lung$ph.ecog <- factor(lung$ph.ecog)
zz <- ezcox(lung, covariates = c("sex", "age"), controls = "ph.ecog")
zz
filter_ezcox(zz)
filter_ezcox(zz, c("0", "2"))
filter_ezcox(zz, c("0", "2"), type = "contrast")
t <- filter_ezcox(zz, c("0", "2"), type = "ref")
t

Create a forest plot for simple data

Description

Create a forest plot for simple data

Usage

forester(
  data,
  display_cols = c("Variable", "HR", "lower_95", "upper_95"),
  estimate_precision = 2,
  null_line_at = 1,
  font_family = "mono",
  x_scale_linear = TRUE,
  xlim = NULL,
  xbreaks = NULL,
  point_sizes = 3,
  point_shape = 16,
  label_hjust = 0,
  label_vjust = -1,
  label_color = "blue",
  label_size = 3
)
forester(
  data,
  display_cols = c("Variable", "HR", "lower_95", "upper_95"),
  estimate_precision = 2,
  null_line_at = 1,
  font_family = "mono",
  x_scale_linear = TRUE,
  xlim = NULL,
  xbreaks = NULL,
  point_sizes = 3,
  point_shape = 16,
  label_hjust = 0,
  label_vjust = -1,
  label_color = "blue",
  label_size = 3
)

Arguments

`data`	Data frame (required). The information to be displayed as the forest plot.
`display_cols`	4 columns stand for axis text and the forest data, default using `c("term", "HR", "conf.low", "conf.high")`.
`estimate_precision`	Integer. The number of decimal places on the estimate (default 2).
`null_line_at`	Numeric. Default 0. Change to 1 if using relative measures such as OR, RR.
`font_family`	String. The font to use for the ggplot. Default "mono".
`x_scale_linear`	Logical. Default TRUE, change to FALSE for log scale
`xlim`	Vector. Manually specify limits for the x axis as a vector length 2, i.e. c(low, high)
`xbreaks`	Vector. X axis breaks to label. Specify limits in xlim if using this option.
`point_sizes`	Vector. Length should be equal to 1 or nrow(left_side_data). The sizes of the points in the center plot, where 3.25 is the default.
`point_shape`	Vector. Length should be equal to 1 or nrow(left_side_data). The shapes of the points in the center plot, where 16 (a filled circle) is the default.
`label_hjust`, `label_vjust`, `label_color`, `label_size`	hjust, vjust color and size for the label text.

Value

a ggplot object.

Examples

library(survival)

t1 <- ezcox(lung, covariates = c(
  "age", "sex",
  "ph.karno", "pat.karno"
))
p <- forester(t1, xlim = c(0, 1.5))
p
p2 <- forester(t1, xlim = c(0.5, 1.5))
p2
library(survival)

t1 <- ezcox(lung, covariates = c(
  "age", "sex",
  "ph.karno", "pat.karno"
))
p <- forester(t1, xlim = c(0, 1.5))
p
p2 <- forester(t1, xlim = c(0.5, 1.5))
p2

Get Model List from ezcox Object

Description

Models are renamed by the formulas.

Usage

get_models(x, variables = NULL)
get_models(x, variables = NULL)

Arguments

`x`	a `ezcox` object from `ezcox()`.
`variables`	a character vector representing variables to select.

Value

a named list with class ezcox_models

Examples

library(survival)
zz <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models = TRUE)
mds <- get_models(zz)
str(mds, max.level = 1)
library(survival)
zz <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models = TRUE)
mds <- get_models(zz)
str(mds, max.level = 1)

Show Forest Plot

Description

This is a wrapper of function ezcox, get_models and show_models. It focus on generating forest plot easily and flexibly.

Usage

show_forest(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  merge_models = FALSE,
  model_names = NULL,
  vars_to_show = NULL,
  drop_controls = FALSE,
  add_caption = TRUE,
  point_size = 3,
  point_shape = 15,
  color = "red",
  banded = TRUE,
  headings = list(variable = "Variable", n = "N", measure = "Hazard ratio", ci = NULL, p
    = "p"),
  model_dir = file.path(tempdir(), "ezcox"),
  verbose = TRUE,
  ...
)
show_forest(
  data,
  covariates,
  controls = NULL,
  time = "time",
  status = "status",
  merge_models = FALSE,
  model_names = NULL,
  vars_to_show = NULL,
  drop_controls = FALSE,
  add_caption = TRUE,
  point_size = 3,
  point_shape = 15,
  color = "red",
  banded = TRUE,
  headings = list(variable = "Variable", n = "N", measure = "Hazard ratio", ci = NULL, p
    = "p"),
  model_dir = file.path(tempdir(), "ezcox"),
  verbose = TRUE,
  ...
)

Arguments

`data`	a `data.frame` containing variables, time and os status.
`covariates`	a character vector optionally listing the variables to include in the plot (defaults to all variables).
`controls`	column names specifying controls. The names with pattern "*:\|()" will be treated as interaction/combination term, please make sure all column names in `data` are valid R variable names.
`time`	column name specifying time, default is 'time'.
`status`	column name specifying event status, default is 'status'.
`merge_models`	if 'TRUE', merge all models and keep the plot tight.
`model_names`	model names to show when `merge_models=TRUE`.
`vars_to_show`	default is `NULL`, show all variables (including controls). You can use this to choose variables to show, but remember, the models have not been changed.
`drop_controls`	works when `covariates=NULL` and `models` is a `ezcox_models`, if `TRUE`, it removes control variables automatically.
`add_caption`	if `TRUE`, add caption to the plot.
`point_size`	size of point.
`point_shape`	shape value of point.
`color`	color for point and segment.
`banded`	if `TRUE` (default), create banded background color.
`headings`	a `list` for setting the heading text.
`model_dir`	a path for storing model results.
`verbose`	if `TRUE`, print extra info.
`...`	other arguments passing to `forestmodel::forest_model()`.

Value

a ggplot object

Examples

library(survival)
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age")
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE)
show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  drop_controls = TRUE
)
p <- show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  vars_to_show = "sex"
)
p
library(survival)
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age")
show_forest(lung, covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE)
show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  drop_controls = TRUE
)
p <- show_forest(lung,
  covariates = c("sex", "ph.ecog"), controls = "age", merge_models = TRUE,
  vars_to_show = "sex"
)
p

Show Cox Models

Description

Show Cox Models

Usage

show_models(
  models,
  model_names = NULL,
  covariates = NULL,
  merge_models = FALSE,
  drop_controls = FALSE,
  headings = list(variable = "Variable", n = "N", measure = "Hazard ratio", ci = NULL, p
    = "p"),
  ...
)
show_models(
  models,
  model_names = NULL,
  covariates = NULL,
  merge_models = FALSE,
  drop_controls = FALSE,
  headings = list(variable = "Variable", n = "N", measure = "Hazard ratio", ci = NULL, p
    = "p"),
  ...
)

Arguments

`models`	a `ezcox_models` from `get_models()` or a (named) list of Cox models.
`model_names`	model names to show when `merge_models=TRUE`.
`covariates`	a character vector optionally listing the variables to include in the plot (defaults to all variables).
`merge_models`	if 'TRUE', merge all models and keep the plot tight.
`drop_controls`	works when `covariates=NULL` and `models` is a `ezcox_models`, if `TRUE`, it removes control variables automatically.
`headings`	a `list` for setting the heading text.
`...`	other arguments passing to `forestmodel::forest_model()`.

Value

a ggplot object

Examples

library(survival)
zz <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models = TRUE)
mds <- get_models(zz)
show_models(mds)
show_models(mds, model_names = paste0("Model ", 1:2))
show_models(mds, covariates = c("sex", "ph.ecog"))
show_models(mds, drop_controls = TRUE)
show_models(mds, merge_models = TRUE)
p <- show_models(mds, merge_models = TRUE, drop_controls = TRUE)
p
library(survival)
zz <- ezcox(lung, covariates = c("sex", "ph.ecog"), controls = "age", return_models = TRUE)
mds <- get_models(zz)
show_models(mds)
show_models(mds, model_names = paste0("Model ", 1:2))
show_models(mds, covariates = c("sex", "ph.ecog"))
show_models(mds, drop_controls = TRUE)
show_models(mds, merge_models = TRUE)
p <- show_models(mds, merge_models = TRUE, drop_controls = TRUE)
p

Package 'ezcox'

Help Index

Clean ezcox Model File Directory

Description

Usage

Arguments

Value

Examples

Run Cox Analysis in Batch Mode

Description

Usage

Arguments

Value

Author(s)

Examples

Group Cox Analysis and Visualization

Description

Usage

Arguments

Value

Examples

Parallelly Run Cox Analysis in Batch Mode

Description

Usage

Arguments

Value

Author(s)

Examples

Filter ezcox

Description

Usage

Arguments

Value

Author(s)

Examples

Create a forest plot for simple data

Description

Usage

Arguments

Value

Examples

Get Model List from ezcox Object

Description

Usage

Arguments

Value

Examples

Show Forest Plot

Description

Usage

Arguments

Value

Examples

Show Cox Models

Description

Usage

Arguments

Value

Examples