enrichment_simulation: Prognostic Enrichment with Simulated Data

Description Usage Arguments Value See Also Examples

View source: R/enrichment_simulation.R

Description

Evaluating biomarkers for prognostic enrichment of clinical trials using simulated data

Usage

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enrichment_simulation(baseline.event.rate, reduction.under.treatment,
  estimated.auc, roc.type = NULL, cost.screening = NULL,
  cost.keeping = NULL, simulation.sample.size = 5e+05,
  alternative = c("one.sided", "two.sided"), power = 0.9, alpha = 0.025,
  selected.biomarker.quantiles = seq(from = 0, to = 95, by = 5))

Arguments

baseline.event.rate

A number between 0 and 1 indicating the prevalence of the event in the study population.

reduction.under.treatment

A number between 0 and 1 indicating the percent reduction in event rate under treatment that the trial should be able to detect with the specified power.

estimated.auc

A numeric vector, with each entry between 0.5 and 1, that specifies the AUC for each biomarker to use in simulations.

roc.type

A character vector with the same length as the estimated.auc argument. Each entry must be one of "symmetric", "right.shifted", or "left.shifted", which describes the general shape of the ROC curve to use for simulated data. Defaults to "symmetric" for each biomarker.

cost.screening

A positive number indicating the cost of screening a patient to determine trial eligibility, This argument is optional; if both cost.screening and cost.keeping are specified, then then the total cost of the trial based on each screening threshold is estimated and returned.

cost.keeping

A positive number indicating the cost of retaining a patient in the trial after enrolling. This argument is optional; if both cost.screening and cost.keeping are specified, then then the total cost of the trial based on each screening threshold is estimated and returned.

simulation.sample.size

A positive number giving the sample size to use for simulated data. Defaults to 500,000 (to help see trends).

alternative

Character specifying whether the alternative hypothesis is one-sided (“one.sided”) with a higher outcome probability in the treatment group or two-sided (“two.sided”). Defaults to “one.sided”.

power

Number between 0 and 1 giving the power the trial should have to reject the null hypothesis that there is no treatment effect. Defaults to 0.9.

alpha

Number between 0 and 1 giving the type I error rate for testing the null hypothesis that there is no treatment effect. Defaults to 0.025.

selected.biomarker.quantiles

Numeric vector specifying the quantiles of the biomarker measured in controls that will be used to screen trial participants. Defaults to 0, 5, ..., 95. All entries must be between at least 0 and less than 001.

Value

A list with components

See Also

enrichment_analysis, plot_enrichment_summaries

Examples

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## three biomarkers with symmetric ROC curves
simulation.three.markers <- enrichment_simulation(baseline.event.rate=0.2,
reduction.under.treatment=0.3,
estimated.auc=c(0.72, 0.82, 0.85),
roc.type=c("symmetric", "symmetric", "symmetric"),
cost.screening=1,
cost.keeping=10,
simulation.sample.size=1e+5)
head(simulation.three.markers$estimates)

BioPET documentation built on May 29, 2017, 11:26 p.m.

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