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license: GPL-2.0-only
title: 'CoRpower: Power Calculations for Assessing Correlates of Risk in Clinical
  Efficacy Trials'
version: 1.0.4
doi: 10.32614/CRAN.package.CoRpower
abstract: 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.
authors:
- family-names: Wu
  given-names: Stephanie
  email: swu18@uw.edu
- family-names: Juraska
  given-names: Michal
  email: mjuraska@fredhutch.org
- family-names: Gilbert
  given-names: Peter
  email: pgilbert@scharp.org
- family-names: Huang
  given-names: Yunda
  email: yunda@scharp.org
repository: https://mjuraska.r-universe.dev
repository-code: https://github.com/mjuraska/CoRpower
commit: 0cbe2b801904f2849af6042f75db9fbc7644567c
url: https://github.com/mjuraska/CoRpower
contact:
- family-names: Juraska
  given-names: Michal
  email: mjuraska@fredhutch.org