| Name : R-BayesCTDesign
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| Version : 0.6.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp154.8.1
| Date : 2024-06-07 07:29:25
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| Group : Development/Libraries/Other
| Source RPM : R-BayesCTDesign-0.6.1-lp154.8.1.src.rpm
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| Size : 0.31 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data
|
Description :
A set of functions to help clinical trial researchers calculate power
and sample size for two-arm Bayesian randomized clinical trials that do
or do not incorporate historical control data. At some point during
the design process, a clinical trial researcher who is designing a
basic two-arm Bayesian randomized clinical trial needs to make
decisions about power and sample size within the context of
hypothesized treatment effects. Through simulation, the simple_sim()
function will estimate power and other user specified clinical trial
characteristics at user specified sample sizes given user defined
scenarios about treatment effect,control group characteristics, and
outcome. If the clinical trial researcher has access to historical
control data, then the researcher can design a two-arm Bayesian
randomized clinical trial that incorporates the historical data. In
such a case, the researcher needs to work through the potential
consequences of historical and randomized control differences on trial
characteristics, in addition to working through issues regarding power
in the context of sample size, treatment effect size, and outcome. If
a researcher designs a clinical trial that will incorporate historical
control data, the researcher needs the randomized controls to be from
the same population as the historical controls. What if this is not
the case when the designed trial is implemented? During the design
phase, the researcher needs to investigate the negative effects of
possible historic/randomized control differences on power, type one
error, and other trial characteristics. Using this information, the
researcher should design the trial to mitigate these negative effects.
Through simulation, the historic_sim() function will estimate power and
other user specified clinical trial characteristics at user specified
sample sizes given user defined scenarios about historical and
randomized control differences as well as treatment effects and
outcomes. The results from historic_sim() and simple_sim() can be
printed with print_table() and graphed with plot_table() methods.
Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal,
Weibull, and Piecewise Exponential. The methods are described in
Eggleston et al. (2021) < doi:10.18637/jss.v100.i21>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
