Fit a Cox proportional-hazards model with phs as the sole predictor and
return predicted survival curves for individuals at specified PHS
percentiles. Unlike phs_km_curve(), these are smooth model-based
predictions rather than empirical group estimates.
Usage
phs_cox_curve(
data,
phs = "phs",
time = "age",
event = "status",
percentiles = c(0.01, 0.05, 0.2, 0.5, 0.8, 0.95, 0.99),
ref_data = NULL,
output = "plot",
conf_int = TRUE,
conf_int_alpha = 0.15,
palette = "hazrd",
...
)Arguments
- data
data.frame with columns specified by
phs,time,event- phs
string or numeric vector; column name or vector of PHS values
- time
string or numeric vector; column name or vector of event times
- event
string or numeric vector; column name or vector of event indicators (0/1)
- percentiles
numeric vector of percentiles strictly in (0, 1) at which to compute Cox-predicted survival curves; default
c(0.01, 0.05, 0.20, 0.50, 0.80, 0.95, 0.99)- ref_data
optional data.frame used to compute the PHS value at each requested percentile (training reference); if
NULL, percentiles are computed fromdata- output
'plot'(default) or'data'- conf_int
logical; include confidence intervals in output/plot
- conf_int_alpha
numeric; alpha for confidence ribbons when plotting
- palette
string; colour palette for plots (default:
'hazrd')- ...
additional args (reserved)