Determine the optimal cutpoint for one or multiple continuous variables at once, using the maximally selected rank statistics from the 'maxstat' R package. This is an outcome-oriented methods providing a value of a cutpoint that correspond to the most significant relation with outcome (here, survival).

  • surv_cutpoint(): Determine the optimal cutpoint for each variable using 'maxstat'.

  • surv_categorize(): Divide each variable values based on the cutpoint returned by surv_cutpoint().

surv_cutpoint(
  data,
  time = "time",
  event = "event",
  variables,
  minprop = 0.1,
  progressbar = TRUE
)

surv_categorize(x, variables = NULL, labels = c("low", "high"))

# S3 method for surv_cutpoint
summary(object, ...)

# S3 method for surv_cutpoint
print(x, ...)

# S3 method for surv_cutpoint
plot(x, variables = NULL, ggtheme = theme_classic(), bins = 30, ...)

# S3 method for plot_surv_cutpoint
print(x, ..., newpage = TRUE)

Arguments

data

a data frame containing survival information (time, event) and continuous variables (e.g.: gene expression data).

time, event

column names containing time and event data, respectively. Event values sould be 0 or 1.

variables

a character vector containing the names of variables of interest, for wich we want to estimate the optimal cutpoint.

minprop

the minimal proportion of observations per group.

progressbar

logical value. If TRUE, show progress bar. Progressbar is shown only, when the number of variables > 5.

x, object

an object of class surv_cutpoint

labels

labels for the levels of the resulting category.

...

other arguments. For plots, see ?ggpubr::ggpar

ggtheme

function, ggplot2 theme name. Default value is theme_classic. Allowed values include ggplot2 official themes. see ?ggplot2::ggtheme.

bins

Number of bins for histogram. Defaults to 30.

newpage

open a new page. See grid.arrange.

Value

  • surv_cutpoint(): returns an object of class 'surv_cutpoint', which is a list with the following components:

    • maxstat results for each variable (see ?maxstat::maxstat)

    • cutpoint: a data frame containing the optimal cutpoint of each variable. Rows are variable names and columns are c("cutpoint", "statistic").

    • data: a data frame containing the survival data and the original data for the specified variables.

    • minprop: the minimal proportion of observations per group.

    • not_numeric: contains data for non-numeric variables, in the context where the user provided categorical variable names in the argument variables.

    Methods defined for surv_cutpoint object are summary, print and plot.

  • surv_categorize(): returns an object of class 'surv_categorize', which is a data frame containing the survival data and the categorized variables.

Examples

# 0. Load some data data(myeloma) head(myeloma)
#> molecular_group chr1q21_status treatment event time CCND1 CRIM1 #> GSM50986 Cyclin D-1 3 copies TT2 0 69.24 9908.4 420.9 #> GSM50988 Cyclin D-2 2 copies TT2 0 66.43 16698.8 52.0 #> GSM50989 MMSET 2 copies TT2 0 66.50 294.5 617.9 #> GSM50990 MMSET 3 copies TT2 1 42.67 241.9 11.9 #> GSM50991 MAF <NA> TT2 0 65.00 472.6 38.8 #> GSM50992 Hyperdiploid 2 copies TT2 0 65.20 664.1 16.9 #> DEPDC1 IRF4 TP53 WHSC1 #> GSM50986 523.5 16156.5 10.0 261.9 #> GSM50988 21.1 16946.2 1056.9 363.8 #> GSM50989 192.9 8903.9 1762.8 10042.9 #> GSM50990 184.7 11894.7 946.8 4931.0 #> GSM50991 212.0 7563.1 361.4 165.0 #> GSM50992 341.6 16023.4 2096.3 569.2
# 1. Determine the optimal cutpoint of variables res.cut <- surv_cutpoint(myeloma, time = "time", event = "event", variables = c("DEPDC1", "WHSC1", "CRIM1")) summary(res.cut)
#> cutpoint statistic #> DEPDC1 279.8 4.275452 #> WHSC1 3205.6 3.361330 #> CRIM1 82.3 1.968317
# 2. Plot cutpoint for DEPDC1 # palette = "npg" (nature publishing group), see ?ggpubr::ggpar plot(res.cut, "DEPDC1", palette = "npg")
#> $DEPDC1
#>
# 3. Categorize variables res.cat <- surv_categorize(res.cut) head(res.cat)
#> time event DEPDC1 WHSC1 CRIM1 #> GSM50986 69.24 0 high low high #> GSM50988 66.43 0 low low low #> GSM50989 66.50 0 low high high #> GSM50990 42.67 1 low high low #> GSM50991 65.00 0 low low low #> GSM50992 65.20 0 high low low
# 4. Fit survival curves and visualize library("survival") fit <- survfit(Surv(time, event) ~DEPDC1, data = res.cat) ggsurvplot(fit, data = res.cat, risk.table = TRUE, conf.int = TRUE)
#> Warning: Vectorized input to `element_text()` is not officially supported. #> Results may be unexpected or may change in future versions of ggplot2.