BioPETsurv (version 0.1.0)

surv_plot_enrichment: Plotting Clinical Trial Metrics for Prognostic Enrichment

Description

This function plots summaries of prognostic enrichment of clinical trials with survival outcomes, based on clinical trial metrics estimated by surv_enrichment.

Usage

surv_plot_enrichment(x, km.quantiles = c(0,0.25,0.5,0.75),
                     km.range = NULL, alt.color = NULL)

Arguments

x

Object returned by surv_enrichment.

km.quantiles

Enrichment levels on which Kaplan-Meier survival estimates (Plot 1) are plotted. Defaults to four quartiles.

km.range

(Optional) a scalar specifying the range of time for which Kaplan-Meier survival estimates (Plot 1) are plotted. Defaults to the last time point of observation.

alt.color

(Optional) allows the user to specify the color of curves for clinical trial metrics (Plots 2-6). The length should match the number of trial lengths considered. Defaults to ggplot2 color palette.

Value

A grid containing either the first 4 or 6 plots described below.

km.plot

The Kaplan-Meier survival curves for specified enrichment levels. The vertical reference line(s) correspond to end.of.trial or a,f. This will be presented even if method = "NNE" was specified.

prob.plot

The estimated event probability (and 95% confidence intervals) at each enrichment level.

ss.plot

The estimated sample size (and confidence intervals) at each enrichment level.

screen.plot

The estimated number of patients that need to be screened (and confidence intervals) to enroll the trial.

cost.plot

The estimated total cost of the trial (and confidence intervals).

reduction.cost.plot

The percentage of reduction in total cost comparing an enriched versus unenriched trial.

summary

A grid of the first 4 or all 6 plots combined together.

Examples

# NOT RUN {
## Following the example of 'surv_enrichment':
data(SurvMarkers)
library(survival)

SurvMarkers$surv <- Surv(SurvMarkers$time, SurvMarkers$event)
rslt <- surv_enrichment(formula = surv~x1+x2, data = SurvMarkers, hr = 0.8, a=12, f=36,
                         cost.screening = 300, cost.keeping = NULL, cost.unit.keeping = 300,
                         method = "KM", power = 0.9, alpha = 0.05, one.sided = FALSE,
                         selected.biomarker.quantiles = seq(from = 0, to = 0.9, by = 0.1),
                         do.bootstrap = FALSE, print.summary.tables = FALSE)

plots <- surv_plot_enrichment(rslt, km.quantiles = c(0,0.25,0.5,0.75))
# }