Proportional hazards assumption test for Cox regression

Tests the proportional hazards (PH) assumption using Schoenfeld residuals via cox.zph. Re-fits the same models as assoc_coxph (same interface) and returns a tidy result table with term-level and global test statistics.

Usage

assoc_coxph_zph(
  data,
  outcome_col,
  time_col,
  exposure_col,
  covariates = NULL,
  base = TRUE,
  strata = NULL
)

assoc_zph(
  data,
  outcome_col,
  time_col,
  exposure_col,
  covariates = NULL,
  base = TRUE,
  strata = NULL
)

Arguments

data: (data.frame or data.table) Analysis dataset.
outcome_col: (character) Binary event indicator (0/1 or TRUE/FALSE).
time_col: (character) Follow-up time column name.
exposure_col: (character) One or more exposure variable names.
covariates: (character or NULL) Covariates for the Fully adjusted model. Default: NULL.
base: (logical) Include Unadjusted and Age and sex adjusted models. Default: TRUE.
strata: (character or NULL) Optional stratification variable.

Value

A data.table with one row per exposure \(\times\) term \(\times\) model combination, and columns:

exposure: Exposure variable name.
term: Coefficient name.
model: Ordered factor: Unadjusted < Age and sex adjusted < Fully adjusted.
chisq: Schoenfeld residual chi-squared statistic.
df: Degrees of freedom.
p_value: P-value for the PH test (term-level).
ph_satisfied: Logical; TRUE if p_value > 0.05.
global_chisq: Global chi-squared for the whole model.
global_df: Global degrees of freedom.
global_p: Global p-value for the whole model.

Details

A non-significant p-value (p > 0.05) indicates the PH assumption is satisfied for that term. The global test (global_p) reflects the overall PH assumption for the whole model.

Examples

dt <- ops_toy(scenario = "association", n = 500)
#> ✔ ops_toy: 500 participants | 33 columns | scenario = "association" | seed = 42
dt <- dt[dm_timing != 1L]

zph <- assoc_coxph_zph(
  data         = dt,
  outcome_col  = "dm_status",
  time_col     = "dm_followup_years",
  exposure_col = "p20116_i0",
  covariates   = c("bmi_cat", "tdi_cat"),
  base         = FALSE
)
#> ℹ outcome_col dm_status: logical detected, converting TRUE/FALSE -> 1/0
#> 
#> ── assoc_coxph_zph ─────────────────────────────────────────────────────────────
#> ℹ 1 exposure x 1 model = 1 PH assumption test
#> 
#> ── p20116_i0 ──
#> 
#> ✔   Fully adjusted | p20116_i0: chisq = 2.876, p = 0.237 [OK] satisfied
#> ℹ   Global: chisq = 4.582, p = 0.801
#> ✔ Done: all 1 term satisfy the PH assumption.
zph[ph_satisfied == FALSE]
#> Empty data.table (0 rows and 10 cols): exposure,term,model,chisq,df,p_value...