enrichment_analysis {BioPET}R Documentation

Prognostic Enrichment with Real Data

Description

Evaluating biomarkers for prognostic enrichment of clinical trials using real data

Usage

enrichment_analysis(formula, data, family = binomial(link = logit),
  reduction.under.treatment, cost.screening = NULL, cost.keeping = NULL,
  do.bootstrap = TRUE, n.bootstrap = 1000, smooth.roc = FALSE,
  power = 0.9, alpha = 0.025, alternative = c("one.sided", "two.sided"),
  selected.biomarker.quantiles = seq(from = 0, to = 0.95, by = 0.05))

Arguments

formula

Object of class "formula", in the form "outcome ~ predictors", where the outcome is a binary indicator with a value of 1 in cases and a value of 0 in controls.

data

Data frame containing the outcome and predictors specified in the “formula” argument. Observations with a missing value of the outcome or of any predictor are dropped.

family

Character object or call to the family() function specifying the link function that is passed to 'glm' to estimate a risk score when more than one predictor is specified. Defaults to binomial(link = "logit"), which yields logistic regression.

reduction.under.treatment

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

cost.screening

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

cost.keeping

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

do.bootstrap

Logical indicating whether bootstrap 95% confidence intervals should be computed. Defaults to TRUE.

n.bootstrap

Number of bootstrap samples to draw, if “do.bootstrap” is set to TRUE. Defaults to 1000.

smooth.roc

Logical indicating the “smooth” argument passed to the roc() function from the ‘pROC’ package when a single biomarker is given. Defaults to FALSE.

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.

alternative

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

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, 0.05, ..., 0.95. All entries must be between at least 0 and less than 1.

Value

A list with components

See Also

enrichment_simulation, plot_enrichment_summaries

Examples

data(dcaData)

## using a single biomarker in the dataset
analysis.single.marker <- enrichment_analysis(Cancer ~ Marker1,
data=dcaData,
reduction.under.treatment=0.3,
cost.screening=100,
cost.keeping=1000)
head(analysis.single.marker$estimates)
head(analysis.single.marker$bootstrap.CIs)

## combining two biomarkers in the dataset
analysis.two.markers <- enrichment_analysis(Cancer ~ Marker1 + Marker2,
data=dcaData,
reduction.under.treatment=0.3,
cost.screening=100,
cost.keeping=1000)
head(analysis.two.markers$estimates)
head(analysis.two.markers$bootstrap.CIs)

[Package BioPET version 0.2.2 Index]