Type: | Package |
Title: | Calculates Significance Criteria and Power for a Single Arm Trial |
Version: | 0.1.3 |
Author: | David A. Schoenfeld |
Maintainer: | David A. Schoenfeld <dschoenfeld@mgh.harvard.edu> |
Description: | Given a database of previous treatment/placebo estimates, their standard errors and sample sizes, the program calculates a significance criteria and power estimate that takes into account the among trial variation. |
Depends: | R (≥ 3.5.0), rstan |
Suggests: | testthat |
License: | GPL-2 | GPL-3 [expanded from: GNU General Public License] |
Encoding: | UTF-8 |
LazyData: | TRUE |
LazyDataCompression: | xz |
RoxygenNote: | 7.1.2 |
NeedsCompilation: | no |
Packaged: | 2022-04-18 23:52:32 UTC; das15 |
Repository: | CRAN |
Date/Publication: | 2022-04-20 08:32:32 UTC |
Objects Created by hct for the ALS of Clinical Trial Results
Description
These are objects of class hct
for the example an ALS clinical trials
Format
Two hct
objects hct220ALSFRS,hct220Delta.
These are fit
from the collection of ALS clinical trials without and with covariate
predictions, respectively. They allow the use of the
functions criteria
and power
functions to
calculate statistics of interest, such as p-values for an
historically controlled ALS trial
Examples
data(ALShctObjects)
#calculate the p-value of an ALS clinical trial where he
#slope of the ALSRS was -2 with standard Error 0.2
hct220ALSFRS$power(-.2,0,.2)
#Calculate the criteria for a p-value of 0.10 one sided to us in #a pilot study
hct220ALSFRS$criteria(0.1,.2)
Comparison of a single armed study to a collection of study results from multiple previous clinical trials.
Description
When comparing a single armed study to historical controls it is necessary to take into account that there may be variation in the underlying treatment/placebo effect from study to study. If this among-study variability is not accounted for the type one and two errors may be inaccurate. Given a historical database of study data, such as one might have in a meta-analysis the program calculates the criteria for significance for a new study that uses the database as an historical control and calculates the power of such as study as a function of sample size and difference to be detected.
Usage
hct(data, estimate, standardError, N, iter = 2000, rseed = NA,
silent=TRUE,constantStderr=TRUE)
Arguments
data |
A data frame of historical data one study per row. |
estimate |
The name or column number of the variable in
|
standardError |
The name or column number of the variable in |
N |
The name or column number of the variable in |
iter |
The number of interations to use in the MCMC to calculate the posterior distribution of the among-study variation and mean outcome measure. |
rseed |
Seed for random number generator |
silent |
Suppresses STAN output to the console |
constantStderr |
If |
Value
A hct
object which is a list of four elements.
criteria |
A function with signature |
power |
A function to calculate the power of a study with signature |
effective.SD |
Which is the effective standard deviation of the outcome measure.
It is calculated as
|
fit |
An object of class |
A generic summary function prints out the value of effective.SD
and uses the data
frame summary
function for
data.frame(extract(fit,c("mu"","sig"","y_pred")))
. The generic print
function prints this summary.
Author(s)
David A. Schoenfeld
References
Design and analysis of a clinical trial using previous trials as historical control
Examples
als=data.frame(estimate=c(3.5,2.6,2.3),SE=c(.4,.3,.6),N=c(100,150,76))
ts=hct(als,'estimate','SE','N')
print(ts)
us=ts$criteria(0.025,.3)
ts$power(us,5,.4)
Print command for an hct object.
Description
Generic print command for hct object. Passes object to summary and then prints summary.
Usage
print(x,...)
Arguments
x |
A hct object produced by hct. |
... |
Arguements passed to |
Value
Prints list created by summary.hct
Author(s)
David A. Schoenfeld
See Also
Summary function for an hct object.
Description
Summary of a hct object.
Usage
summary(object,...)
Arguments
object |
hct object. |
... |
arguments passed to |
Value
A list with entries effective.SD
and prior.distribution
, the
first is the effective.SD and the second is the summary.data.frame
applied to the
prior.distribution
element of the hct object. Note that the prior.distribution
can be considered a posterior.distribution as it is the posterior distribution of the parameters with the input data base, however it is used as a prior distribution for the purpose of analyzing the historical trial.
Author(s)
David A. Schoenfeld