Performs Cox proportional hazards regression analysis on multiple variables. Optionally determines optimal cutoffs to dichotomize continuous predictors before modeling. Returns hazard ratios, confidence intervals, and p-values for each variable.
Arguments
- pdata
Data frame containing survival time, event status, and predictor variables.
- variable
Character vector specifying the names of predictor variables to analyze.
- time
Character string specifying the column name containing follow-up time. Default is `"time"`.
- status
Character string specifying the column name containing event status (1 = event occurred, 0 = censored). Default is `"status"`.
- best_cutoff
Logical indicating whether to compute optimal cutoffs for continuous variables and analyze dichotomized versions. Default is `FALSE`.
Value
Data frame containing hazard ratios (HR), 95 and p-values for each variable, sorted by p-value.
Examples
sig_stad <- load_data("sig_stad")
batch_surv(
pdata = sig_stad,
variable = colnames(sig_stad)[69:ncol(sig_stad)],
time = "OS_time",
status = "OS_status"
)
#> # A tibble: 255 × 5
#> P HR CI_low_0.95 CI_up_0.95 ID
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 0.000420 1.21 1.09 1.35 Taurine_and_Hypotaurine_Metabolism
#> 2 0.00263 1.10 1.03 1.17 TGFb_Family_Member_Li_et_al
#> 3 0.00273 1.03 1.01 1.06 Cytokines_Li_et_al
#> 4 0.00273 1.13 1.04 1.23 Retinoic_Acid_Metabolism
#> 5 0.00278 1.04 1.01 1.06 CAF.S1
#> 6 0.00305 1.09 1.03 1.16 Arachidonic_Acid_Metabolism
#> 7 0.00353 1.09 1.03 1.15 GPAGs
#> 8 0.00354 1.21 1.06 1.37 PPARgama_target_genes
#> 9 0.00358 1.13 1.04 1.23 EMT2
#> 10 0.00393 1.09 1.03 1.16 MDSC_Peng_et_al
#> # ℹ 245 more rows