Package: CoRpower 1.0.4

Set-up and notation | Without replacement sampling | Algorithm for trichotomous biomarker $S(1)$ | Without replacement sampling | Illustration: hypothetical randomized placebo-controlled VE trial | Trial design | Illustration: calculation of input parameters with computeN() | Assumptions | Number of vaccine recipients observed to be at risk at $\tau$ | Number of observed cases in vaccine recipients between $\tau$ and $\tau_{\mathrm{max}}$ | Number of observed controls in vaccine recipients between $\tau$ and $\tau_{\mathrm{max}}$ | Number of observed cases (controls) in vaccine recipients between $\tau$ and $\tau_{\mathrm{max}}$ with measured $S(1)$ | Compute $N_1, n_{cases,1}, n_{controls,1},$ and $n^S_{cases,1}$ with computeN() | Illustration: CoRpower for trichotomous (, S(1)) | Without replacement sampling | Scenario 1: vary control:case ratio (Approach 1) | Trichotomous $S(1)$, without replacement sampling | Run simulations and compute power with computePower() | Plot power curves with plotPowerTri() | Scenario 2: vary (, Sens) and (, Spec) (Approach 1) | Trichotomous (, S(1)), without replacement sampling | Scenario 3: vary (, P^{lat}_0, P^{lat}_2, P_0, P_2) (Approach 1) | Trichotomous (, S(1)), without replacement sampling | Scenario 4: vary (, \rho ) (Approach 2) | Trichotomous (, S(1)), without replacement sampling | Plot $RR_t$ vs. $RR_2^{lat}/RR_0^{lat}$ with plotRRgradVE() | Plot ROC curves with plotROCcurveTri() | Scenario 5: vary (, P^{lat}_0, P_0, P^{lat}_2, P_2 ) (Approach 2) | Trichotomous (, S(1)), without replacement sampling | Scenario 6: vary (, n_{cases,1}) (Approach 2) | Trichotomous (, S(1)), without replacement sampling | Illustration: CoRpower for binary (, S(1)) | Without replacement sampling | Scenario 7: vary (, n_{cases,1}) (Approach 2) | Binary (, S(1)), without replacement sampling | Algorithm for continuous biomarker (, S^{\ast}(1)) | Without replacement sampling | Illustration: CoRpower for continuous (, S^{\ast}(1)) | Without replacement sampling | Scenario 8: vary (, \rho ) | Continuous (, S^{\ast}(1)), without replacement sampling | Plot power curves with plotPowerCont() | Plot $VE^{lat}_{x^{\ast}}$ curves with plotVElatCont() | Scenario 9: vary (, P_{lowestVE}^{lat} ) | Continuous (, S^{\ast}(1)), without replacement sampling | Bernoulli / case-cohort sampling of (, S(1)) (or (, S^{\ast}(1))) | Illustration: CoRpower for trichotomous (, S(1)) and continuous (, S^{\ast}(1)) | Bernoulli sampling | Scenario 10: vary (, p ) (Approach 1) | Trichotomous (, S(1) ), Bernoulli sampling | Scenario 11: vary (, p ) | Continuous (, S^{\ast}(1)), Bernoulli sampling

Introduction | Algorithms for Simulating Placebo Group Data | Trichotomous (, X) and (, S(1)) Using Approach 1 | Trichotomous (, X) and (, S(1)) Using Approach 2 | Continuous (, X^) and (, S^(1)) | Algorithms for Simulating a Baseline Immunogenicity Predictor (BIP) | Trichotomous (, X, S(1),) and (, BIP) Using Approach 1 | Trichotomous (, X, S(1),) and (, BIP) Using Approach 2 | Continuous (, X^, S^(1),) and (, BIP^*)