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Step: Cox Proportional Hazards Model

step_cox.Rd

Perform survival analysis by fitting a Cox proportional hazards model using glystats::gly_cox() for each variable. This step identifies variables associated with survival outcomes.

Usage

step_cox(
  on = "exp",
  time_col = "time",
  event_col = "event",
  p_adj_method = "BH",
  ...
)

Arguments

on

Name of the experiment to run Cox regression on. Can be "exp", "sig_exp", "trait_exp", "sig_trait_exp", "motif_exp", "sig_motif_exp".

time_col

Column name in sample information containing survival time. Default is "time".

event_col

Column name in sample information containing event indicator (1 for event, 0 for censoring). Default is "event".

p_adj_method

Method for adjusting p-values. See p.adjust.methods. Default is "BH". If NULL, no adjustment is performed.

...

Additional arguments passed to glystats::gly_cox().

Value

A glysmith_step object.

Details

Data required:

  • exp (if on = "exp"): The experiment to run Cox regression on

  • trait_exp (if on = "trait_exp"): The trait experiment to run Cox regression on

  • motif_exp (if on = "motif_exp"): The motif experiment to run Cox regression on

The experiment must contain survival data with time_col and event_col columns in the sample information.

Tables generated (with suffixes):

  • cox: A table containing Cox regression results with columns:

    • variable: Variable name

    • coefficient: Regression coefficient (log hazard ratio)

    • std.error: Standard error of the coefficient

    • statistic: Wald test statistic

    • p_val: Raw p-value from Wald test

    • hr: Hazard ratio (exp(coefficient))

    • p_adj: Adjusted p-value (if p_adj_method is not NULL)

AI Prompt

This section is for AI in inquire_blueprint() only.

  • Include this step when users want to identify variables associated with survival outcomes.

  • This step requires survival data (time and event columns) in the sample information.

  • Always ask for the column names for survival data, unless explicitly provided.

See also

glystats::gly_cox(), survival::coxph()

Examples

step_cox()
#> <step "step_cox()"> Cox proportional hazards model
step_cox(time_col = "survival_time", event_col = "death")
#> <step "step_cox(time_col = \"survival_time\", event_col = \"death\")"> Cox
#> proportional hazards model
step_cox(on = "sig_exp", p_adj_method = "bonferroni")
#> <step "step_cox(on = \"sig_exp\", p_adj_method = \"bonferroni\")"> Cox
#> proportional hazards model of significant variables