Package 'OptimalSurrogate'

Simulated data with continuous surrogate marker

Description

Simulated data with continuous surrogate marker

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Simulated data with discrete surrogate marker

Description

Simulated data with discrete surrogate marker

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PTE estimation with a continuous surrogate marker

Description

PTE estimation with continuous surrogate marker

Usage

pte_cont(sob, yob, aob, var = TRUE, conf.int = TRUE, rep = 500)

Arguments

sob	CONTINUOUS surrogate marker
yob	outcome of interest (continuous or binary)
aob	treatment assignment (1: treatment; 0: control)
var	whether variance should be calculated (TRUE/FALSE)
conf.int	whether 95% confidence intervals should be calculated (TRUE/FALSE)
rep	number of resampling replications (default is 500)

Value

Estimates	Estimates of the treatment effect on the priamry outcome, delta, the treatment effect on the transformation of the surrogate, delta.gs, two versions of the proportion of treatment effect explained by the surrogate, pte1 and pte2; if var = TRUE, standard error estimates are also provided (se); if conf.int = TRUE, 95% confidence intervals are also provided
Transformed.S	the transformed surrogate, g(s), for each value of the surrogate, s; if var = TRUE, standard error estimates are also provided (se); if conf.int = TRUE, 95% confidence intervals are also provided

Examples

data(marker_cont)
out <- pte_cont(
  sob = marker_cont$sob, 
  yob = marker_cont$yob, 
  aob = marker_cont$aob, var = FALSE, conf.int = FALSE)
out

x <- as.numeric(names(out$Transformed.S))
plot(x, out$Transformed.S, ylim = range(out$Transformed.S), type = "l", 
     las = 1, xlab = "Surrogate Marker", ylab = "Optimal Transformation")


out <- pte_cont(
  sob = marker_cont$sob, 
  yob = marker_cont$yob, 
  aob = marker_cont$aob, 
  var = TRUE, conf.int = TRUE, rep = 1000)
out$Estimates

x <- as.numeric(rownames(out$Transformed.S))
plot(x, out$Transformed.S[, "est"], ylim = range(out$Transformed.S[, -2]), type = "l", 
     las = 1, xlab = "Surrogate Marker", ylab = "Optimal Transformation")
  lines(x, out$Transformed.S[, "lower"], lty = 2)
  lines(x, out$Transformed.S[, "upper"], lty = 2)

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PTE estimation with a discrete surrogate marker

Description

PTE estimation with discrete surrogate marker

Usage

pte_disc(sob, yob, aob, var = TRUE, conf.int = TRUE, rep = 500)

Arguments

sob	DISCRETE surrogate marker
yob	outcome of interest (continuous or binary)
aob	treatment assignment (1: treatment; 0: control)
var	whether variance should be calculated (TRUE/FALSE)
conf.int	whether 95% confidence intervals should be calculated (TRUE/FALSE)
rep	number of resampling replications (default is 500)

Value

Estimates	Estimates of the treatment effect on the priamry outcome, delta, the treatment effect on the transformation of the surrogate, delta.gs, two versions of the proportion of treatment effect explained by the surrogate, pte1 and pte2; if var = TRUE, standard error estimates are also provided (se); if conf.int = TRUE, 95% confidence intervals are also provided
Transformed.S	the transformed surrogate, g(s), for each value of the surrogate, s; if var = TRUE, standard error estimates are also provided (se); if conf.int = TRUE, 95% confidence intervals are also provided

Examples

data(marker_disc)
out <- pte_disc(
  sob = marker_disc$sob, 
  yob = marker_disc$yob, 
  aob = marker_disc$aob, var = FALSE, conf.int = FALSE)
out

out <- pte_disc(
  sob = marker_disc$sob, 
  yob = marker_disc$yob, 
  aob = marker_disc$aob, 
  var = TRUE, conf.int = TRUE, rep = 1000)
out

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