| Name : R-CoRpower
| |
| Version : 1.0.4
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.13
| Date : 2024-06-14 11:14:27
|
| Group : Development/Libraries/Other
| Source RPM : R-CoRpower-1.0.4-lp153.2.13.src.rpm
|
| Size : 0.34 MB
| |
| Packager : (none)
| |
| Summary : Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials
|
Description :
Calculates power for assessment of intermediate biomarker responses as
correlates of risk in the active treatment group in clinical efficacy
trials, as described in Gilbert, Janes, and Huang, Power/Sample Size
Calculations for Assessing Correlates of Risk in Clinical Efficacy
Trials (2016, Statistics in Medicine). The methods differ from past
approaches by accounting for the level of clinical treatment efficacy
overall and in biomarker response subgroups, which enables the
correlates of risk results to be interpreted in terms of potential
correlates of efficacy/protection. The methods also account for
inter-individual variability of the observed biomarker response that is
not biologically relevant (e.g., due to technical measurement error of
the laboratory assay used to measure the biomarker response), which is
important because power to detect a specified correlate of risk effect
size is heavily affected by the biomarker\'s measurement error. The
methods can be used for a general binary clinical endpoint model with a
univariate dichotomous, trichotomous, or continuous biomarker response
measured in active treatment recipients at a fixed timepoint after
randomization, with either case-cohort Bernoulli sampling or
case-control without-replacement sampling of the biomarker (a baseline
biomarker is handled as a trivial special case). In a specified
two-group trial design, the computeN() function can initially be used
for calculating additional requisite design parameters pertaining to
the target population of active treatment recipients observed to be at
risk at the biomarker sampling timepoint. Subsequently, the power
calculation employs an inverse probability weighted logistic regression
model fitted by the tps() function in the \'osDesign\' package. Power
results as well as the relationship between the correlate of risk
effect size and treatment efficacy can be visualized using various
plotting functions. To link power calculations for detecting a
correlate of risk and a correlate of treatment efficacy, a baseline
immunogenicity predictor (BIP) can be simulated according to a
specified classification rule (for dichotomous or trichotomous BIPs) or
correlation with the biomarker response (for continuous BIPs), then
outputted along with biomarker response data under assignment to
treatment, and clinical endpoint data for both treatment and placebo
groups.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
