Find the best cutpoint for a covariate — best

Repeatedly finds cutpoints for an explanatory variable in a univariable Cox PH model. Also plots survival curves for each cutpoint.

Usage

best_cut(
  f,
  d,
  n = c("b", "t", "qd", "qn"),
  AIC.range = 3,
  nround = 3,
  plot = TRUE,
  filename = NULL,
  nrow = NULL,
  ncol = NULL,
  title = "",
  ...
)

Arguments

f: formula object
d: data frame
n: number of groups to transform variable into. Options are "b" (two), "t" (three), "qd" (four), and "qn" (five)
AIC.range: If range of AIC is within AIC.range units, the likelihood is too flat. We choose the best cutpoint using the alternative method.
nround: number of digits to round AIC and p-value on plots
plot: logical; If TRUE, shows the survival curves for each cutpoint in a facetted figure
filename: file name for saving a png image of figure
nrow: number of rows in facetted plot
ncol: number of columns in facetted plot
title: title for plot
...: additional arguments for plot

Value

A list with the following elements

cuts: vector of cutpoints considered
fits: A list of coxph objects run for each cutpoint
results: A table showing the likelihood ratio test p-value, log likelihood, and AIC for each cutpoint
opt.cut: optimal cutpoint value
flat.lik: If TRUE, the likelihood was too flat and the alternative method was used

Additionally, if plot = TRUE, the function also returns KM survival curves for each possible cutpoint.

Details

Takes the cutpoint resulting in the lowest AIC. If the range of AIC values is within AIC.range units, take the cutpoint that results in the two groups having the most similar numbers of events and cases. The function can cut a variable into anywhere from 2 to 5 groups.

Author

Derek Chiu