CoRpower
’s Algorithms for Simulating Placebo Group and Baseline Immunogenicity Predictor DataThe CoRpower
package assumes that \(P(Y^{\tau}(1)=Y^{\tau}(0))=1\) for the biomarker sampling timepoint \(\tau\), which renders the CoR parameter \(P(Y=1 \mid S=s_1, Z=1, Y^{\tau}=0)\) equal to \(P(Y=1 \mid S=s_1, Z=1, Y^{\tau}(1)=Y^{\tau}(0)=0)\), which links the CoR and biomarker-specific treatment efficacy (TE) parameters. Estimation of the latter requires outcome data in placebo recipients, and some estimation methods additionally require availability of a baseline immunogenicity predictor (BIP) of \(S(1)\), the biomarker response at \(\tau\) under assignment to treatment. In order to link power calculations for detecting a correlate of risk (CoR) and a correlate of TE (coTE), CoRpower
allows to export simulated data sets that are used in CoRpower
’s calculations and that are extended to include placebo-group and BIP data for harmonized use by methods assessing biomarker-specific TE. This vignette aims to describe CoRpower
’s algorithms, and the underlying assumptions, for simulating placebo-group and BIP data. The exported data sets include full rectangular data to allow the user to consider various biomarker sub-sampling designs, e.g., different biomarker case:control sampling ratios, or case-control vs. case-cohort designs.
rnorm(Ncomplete, mean=0, sd=sqrt(sigma2e))
Note: All variables with * are continuous.