The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: Using Historical Controls for Designing Phase II Clinical Trials
Version: 0.1.0
Description: Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.
Depends: R (≥ 3.5.0)
License: GPL-2 | GPL-3
LazyData: TRUE
RoxygenNote: 6.1.1
Imports: clinfun, GenBinomApps, stats
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2018-12-12 08:06:44 UTC; edelmand
Author: Dominic Edelmann [aut, cre]
Maintainer: Dominic Edelmann <dominic.edelmann@dkfz-heidelberg.de>
Repository: CRAN
Date/Publication: 2018-12-21 15:20:06 UTC

hctrial: A package for designing phase 2 clinical trials adjusting for heterogeneous populations.

Description

The hctrial package provides functions for designing phase 2 clinical trials that adjust for the heterogeneity in the population.

Details

Two different ways are considered for designing a trial: based on known subgroups or based on historical data.

For initializing a stratified trial, use strat_start.

At interim, strat_interim should be used to adjust the trial.

At the end of the study, strat_end is used to adjust the trial again.

hist_start, hist_interim and hist_end work analogously, but are based on historical controls.


Adjust a design based on historical controls at the end of the study using the covariate data of the patients accrued in stage 2.

Description

Adjust a design based on historical controls at the end of the study using the covariate data of the patients accrued in stage 2.

Usage

hist_end(interim, stagetwo_data)

Arguments

interim

An design based on historical controls and adjusted at interim as returned by hist_interim().

stagetwo_data

A dataframe containing the relevant covariate data of the patients accrued in stage 2.

Value

A list returning the arguments of the function and the final design of the trial.

Examples

X <- abs(rnorm(1000, 0, 1))
Y <- rbinom(1000, 1, 1-exp(-X))
mydata <- data.frame("X" = X, "Y" = Y)
start <- hist_start(mydata, Y~X, c1 = 2)
n1 <- start$des_start[2]
X1 <- abs(rnorm(n1, 0, 1))
dataone <- data.frame("X" = X1)
interim <- hist_interim(start, dataone)
n2 <- interim$des_interim[4]
X2 <- abs(rnorm(n2, 0, 1))
datatwo <- data.frame("X" = X2)
hist_end(interim, datatwo)

Adjust a design based on historical controls at interim using the covariate data of the patients accrued in stage 1.

Description

Adjust a design based on historical controls at interim using the covariate data of the patients accrued in stage 1.

Usage

hist_interim(start, stageone_data)

Arguments

start

An initialized design based on historical controls as returned by hist_start().

stageone_data

A dataframe containing the relevant covariate data of the patients accrued in stage 1.

Value

A list returning the arguments of the function and the preliminary design of a trial based on historical controls adjusted at interim.

Examples

X <- abs(rnorm(1000, 0, 1))
Y <- rbinom(1000, 1, 1-exp(-X))
mydata <- data.frame("X" = X, "Y" = Y)
start <- hist_start(mydata, Y~X, c1 = 2)
n1 <- start$des_start[2]
X1 <- abs(rnorm(n1, 0, 1))
dataone <- data.frame("X" = X1)
hist_interim(start, dataone)

Initializes a design based on historical controls before the start of the study.

Description

Initializes a design based on historical controls before the start of the study.

Usage

hist_start(hist_data, formula, phi = "odds_ratio", c1, modelfit = NULL,
  mean0 = NULL, mean1 = NULL, alpha = 0.05, beta = 0.2)

Arguments

hist_data

A data frame containing covariates and binary responses for historical controls.

formula

A formula which is used for fitting a logistic regression model on the historical data.

phi

The relation between the response rate under the null and the response rate under the interesting alternative. "odds_ratio" assumes that the odds ratio (OR) between these response rates is constant with OR = c1+1. "difference" assumes that the response rate under the alternative is c1 higher than under the null. Can also be specified by the user by providing a function with arguments c and x.

c1

parameter for obtaining the response rate under the alternative, see description of phi.

modelfit

Can be used instead of formula and hist_data to provide an arbitrary fitted model that is compatible with predict(modelfit, type="response"). formula and hist_data are ignored if modelfit is specified.

mean0

Optional: Can be used to overwrite the estimated average response rate under the null of the fitted model.

mean1

Optional: Can be used to overwrite the estimated average response rate under the alternative of the fitted model.

alpha

Specified type I error of the trial.

beta

Specified type II error of the trial.

Value

A list returning the arguments of the function and the preliminary design for starting the stratified trial.

Examples

X <- abs(rnorm(1000, 0, 1))
Y <- rbinom(1000, 1, 1-exp(-X))
mydata <- data.frame("X" = X, "Y" = Y)
hist_start(mydata, Y~X, c1 = 2)

Adjust a subspace stratified design at the end of the study.

Description

Adjust a subspace stratified design at the end of the study.

Usage

strat_end(interim, sub_stagetwo)

Arguments

interim

A preliminary stratified design adjusted at interim as returned by strat_interim().

sub_stagetwo

The subtypes observed for the patients accrued in stage 2.

Value

A list returning the arguments of the function and the final design of the stratified trial.

Examples

p0_sub <- c(0.1, 0.3, 0.5)
p1_sub <- c(0.3, 0.5, 0.7)
distr_sub <- c(1/3, 1/3, 1/3)
start <- strat_start(p0_sub, p1_sub, distr_sub)
n1 <- start$des_start[2]
subone <- sample(c(1,2,3), n1, TRUE)
interim <- strat_interim(start, subone)
n2 <- interim$des_interim[4]
subtwo <- sample(c(1,2,3), n2, TRUE)
strat_end(interim, subtwo)

Adjust a subspace stratified design at interim.

Description

Adjust a subspace stratified design at interim.

Usage

strat_interim(start, sub_stageone)

Arguments

start

An initialized stratified design as returned by strat_start().

sub_stageone

The subtypes observed for the patients accrued in stage 1.

Value

A list returning the arguments of the function and the preliminary design of a stratified trial adjusted at interim.

Examples

p0_sub <- c(0.1, 0.3, 0.5)
p1_sub <- c(0.3, 0.5, 0.7)
distr_sub <- c(1/3, 1/3, 1/3)
start <- strat_start(p0_sub, p1_sub, distr_sub)
n1 <- start$des_start[2]
subone <- sample(c(1,2,3), n1, TRUE)
strat_interim(start, subone)

Initializes a subspace stratified design before the start of the study.

Description

Initializes a subspace stratified design before the start of the study.

Usage

strat_start(p0_sub, p1_sub, distr_sub, alpha = 0.05, beta = 0.2)

Arguments

p0_sub

A vector, where the $i$-th entry corresponds to the response rate under the null for the $i$-th subtype.

p1_sub

A vector, where the $i$-th entry corresponds to the response rate under the alternative for the $i$-th subtype.

distr_sub

A vector, where the $i$-th entry corresponds to the prevalence of the $i$-th subtype in the population.

alpha

Specified type I error of the trial.

beta

Specified type II error of the trial.

Value

A list returning the arguments of the function and the preliminary design for starting the stratified trial.

Examples

p0_sub <- c(0.1, 0.3, 0.5)
p1_sub <- c(0.3, 0.5, 0.7)
distr_sub <- c(1/3, 1/3, 1/3)
strat_start(p0_sub, p1_sub, distr_sub)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.