Adjust Zhong's two-stage Phase II design

Description

Adjust Zhong's two-stage design for over-running or under-running.

Usage

adj.two(n1, r1, s1, n2, alpha1, alpha2, beta, pc, pe, ...)

Arguments

Details

This function enumerates feasible second-stage boundaries after the first-stage sample size and boundaries have been fixed.

Value

An object of class "opt.design", returned as a list containing:

Author(s)

Wenchuan Guo <wguo1017@gmail.com>, Jianan Hui <jiananhuistat@gmail.com>

Examples

 n1 <- 22
 r1 <- 6
 s1 <- 22
 n2 <- 24
 pc <- 0.4
 pe <- pc + 0.15
 alpha1 <- 0.3
 alpha2 <- 0.1
 beta <- 0.2
 out <- adj.two(n1, r1, s1, n2, alpha1, alpha2, beta, pc, pe)

Run dose-finding simulations

Description

Run dose-finding simulations based on a customized decision table.

Usage

dec.sim(truep, decTable, start.level = 1, nsim = 1000)

Arguments

Details

Assume there are d dose levels to be studied. Let n_i and m_i denote the cumulative number of patients treated and the cumulative number of DLTs observed at the current dose level, respectively. Let n_{max} be the maximum number of patients allowed at each dose level. The procedure is as follows:

Step 1

Update the cumulative numbers of DLTs (m_i) and total treated patients (n_i) at the current dose, then use the decision table to choose an action. If the decision is "S", go to Step 2. If the decision is "D" or "DU", go to Step 3. If the decision is "E", go to Step 4.

Step 2

If n_i = n_{max}, declare dose i as the MTD. Otherwise, treat an additional cohort at the current dose and return to Step 1.

Step 3

If the current dose is the lowest dose, stop the trial and declare that the MTD is below the lowest dose. Otherwise, if the next lower dose has not yet reached n_{max}, treat an additional cohort there, move to that dose, and return to Step 1. If the next lower dose has already reached n_{max}, stop the trial and declare that dose as the MTD. If the decision is "DU", record the current dose as unusable and do not treat additional patients there.

Step 4

If the current dose is the highest dose, stop the trial and declare that the MTD is above the highest dose. Otherwise, if the next higher dose has been marked "DU", continue treating the current dose until it reaches n_{max}; if that limit is reached, declare the current dose as the MTD. If the next higher dose is available and has not yet reached n_{max}, treat an additional cohort there, move up to that dose, and return to Step 1. Otherwise, declare the current dose as the MTD.

Value

An object of class "dec.sim". Use summary.dec.sim to obtain and print a summary table of the results. The returned object is a list containing:

Author(s)

Wenchuan Guo <wguo1017@gmail.com>

Examples

truep <- c(0.3, 0.45, 0.5, 0.6)
res <- dec.table(0.6,0.4,0.2,0.3,c(3,3,3))
out <- dec.sim(truep, as.matrix(res$table), start.level = 2, nsim = 1000)
summary(out, pt = 0.3)

Generate a dose-finding decision table

Description

Generate a two- or three-stage dose-finding decision table.

Usage

dec.table(alpha.l, alpha.r, alpha.u, pt, n, sf.param = 4, pe.par = 0.25, ...)

Arguments

Details

An alpha-spending method is available for two- and three-stage designs. dec.table uses the Hwang-Shih-DeCani spending function.

Value

An object of class "dec.table", returned as a list containing:

Author(s)

Wenchuan Guo <wguo1017@gmail.com>

Examples

alpha.l <- 0.6
alpha.r <- 0.4
alpha.u <- 0.1
pt <- 0.3
# Print the decision table for a 3+3+3 design
n <- rep(3, 3)
dec.table(alpha.l, alpha.r, alpha.u, pt, n)$table
# 3+3 design
n <- rep(3, 2)
dec.table(alpha.l, alpha.r, alpha.u, pt, n)$table

Zhong's 2- or 3-stage Phase II design

Description

Calculate the optimal 2- or 3-stage design proposed by Bob Zhong.

Usage

opt.design(
  alpha1,
  alpha2,
  beta,
  pc,
  pe,
  stage = 2,
  stop.eff = FALSE,
  frac_n1 = NULL,
  frac_n2 = NULL,
  sf.param = NULL,
  show = FALSE,
  nmax = 100,
  n.choice = 1,
  ...
)

Arguments

Details

In the two-stage design, n1 patients are treated in the first stage. At the end of stage 1, the trial either continues to stage 2 or stops early for inefficacy, depending on the number of observed responses. If the trial continues, an additional n2 patients are treated. The three-stage design extends the two-stage design by adding one interim stage between stages 1 and 2. The left-side rejection region is defined by response <= r_i for i = 1, 2, 3, and the right-side rejection region is defined by response > s. An alpha-spending method is available for both two- and three-stage designs. opt.design uses the Hwang-Shih-DeCani spending function; you can change the definition of HSD to use a different spending function.

Value

An object of class "opt.design", returned as a list containing:

Author(s)

Wenchuan Guo <wguo1017@gmail.com>, Jianan Hui <jiananhuistat@gmail.com>

References

Zhong. (2012) Single-arm Phase IIA clinical trials with go/no-go decisions. Contemporary Clinical Trials, 33, 1272–1279.

Examples

 alpha1 <- 0.15
 alpha2 <- 0.10
 beta <- 0.15
 pc <- 0.25
 pe <- pc + 0.20
 # calculate optimal two-stage design without using alpha-spending
 opt.design(alpha1, alpha2, beta, pc, pe, stage=2)
 
 # calculate optimal two-stage design with Pocock-like spending function 
 opt.design(alpha1, alpha2, beta, pc, pe, stage = 2, sf.param = 1)
 
 # calculate optimal three-stage design with an O'Brien-Fleming-like spending function
 opt.design(alpha1, alpha2, beta, pc, pe, stage = 3, sf.param = -4)

Plot simulation results from a "dec.sim" object

Description

Available plot types are: true toxicity at each dose level (type = "s"); a bar plot of the probability of selecting each dose as the MTD (type = "prob"); a bar plot of the average number of patients treated at each dose level (type = "np"); and a bar plot of the average number of DLTs at each dose level (type = "dlt"). Setting type = "all" produces all four plots.

Usage

## S3 method for class 'dec.sim'
plot(
  x,
  pt,
  s = 1,
  type = c("all", "s", "prob", "np", "dlt"),
  label = TRUE,
  col = "cornflowerblue",
  text.col = "darkblue",
  cex = 1,
  ...
)

Arguments

Examples

# generate decision table
dt <- dec.table(0.6,0.4,0.2,0.3,c(3,3,3))
# Simulate trials from test data
test.file <- system.file("extdata", "testS.csv", package = "tsdf")
out <- sl.sim(dt$table, test.file)
plot(out, pt=rep(0.3,2), s=1, type="all")
plot(out, pt=rep(0.3,2), s=2, type="prob")
plot(out, pt=rep(0.3,2), s=1, type="np")
plot(out, pt=rep(0.3,2), s=2, type="dlt")

Plot a decision table from a "dec.table" object

Description

plot method for class "dec.table".

Usage

## S3 method for class 'dec.table'
plot(x, ...)

Arguments

Details

plot.dec.table plots the decision boundaries.

Examples

truep <- c(0.3, 0.45, 0.5, 0.6)
out <- dec.table(0.6,0.4,0.2,0.3,c(3,3,3))
plot(out)

Print a decision table from a "dec.table" object

Description

print method for class "dec.table".

Usage

## S3 method for class 'dec.table'
print(x, ...)

Arguments

Details

print.dec.table prints the decision table together with a legend.

Examples

print(dec.table(0.6,0.4,0.2,0.3,c(3,3,3)))

Print Zhong's design from an "opt.design" object

Description

print method for class "opt.design".

Usage

## S3 method for class 'opt.design'
print(x, ...)

Arguments

Examples

alpha1 <- 0.20
alpha2 <- 0.1
beta <- 0.20
pc <- 0.5
pt <- pc + 0.2
out <- opt.design(alpha1, alpha2, beta, pc, pt, stage = 2, sf.param = 1)
print(out)

Dose-finding simulations for a list of scenarios

Description

Run dose-finding simulations based on a customized decision table for one or more scenarios read from a file.

Usage

sl.sim(decTable, file, header = TRUE, sep = ",", ...)

Arguments

Details

In each row of the input file, the parameters must be ordered as start.level, nsim, and truep. The dose-finding algorithm is described in dec.sim.

Value

An object of class "dec.sim" (for one scenario) or "sl.sim" (for multiple scenarios). Use summary to obtain and print a summary of the results. The returned object is a list containing:

Author(s)

Wenchuan Guo <wguo1017@gmail.com>

Examples

dt <- dec.table(0.6,0.4,0.2,0.3,c(3,3,3))
test.file <- system.file("extdata", "testS.csv", package = "tsdf")
# use a customized decision table
table.file <- system.file("extdata", "decTable.csv", package = "tsdf")
dec <- read.csv(table.file, row.names = 1, check.names = FALSE)
out1 <- sl.sim(as.matrix(dt$table), test.file)
out2 <- sl.sim(dec, test.file)

Summarize simulation results from a "dec.sim" object

Description

summary method for class "dec.sim".

Usage

## S3 method for class 'dec.sim'
summary(object, pt, ...)

Arguments

Details

summary formats key statistics from dose-finding simulations. Given the target toxicity, it reports the probability of selecting each dose level as the MTD, the probability of selecting doses above the true MTD, the probability of selecting the true MTD, and the probability that subjects are treated at or below the true MTD. The true MTD is defined as the highest dose level with toxicity probability less than or equal to the target toxicity. If the target is below the smallest toxicity probability, the lowest dose level is treated as the MTD. For example, if the target is 0.3 and the true toxicity probabilities for five doses are 0.1, 0.25, 0.35, 0.40, and 0.50, then the MTD is dose 2.

Examples

test.file <- system.file("extdata", "testS.csv", package = "tsdf")
dt <- dec.table(0.6,0.4,0.2,0.3,c(3,3,3))
out <- sl.sim(dt$table, test.file)
pt <- c(0.3, 0.4)
summary(out, pt)