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Statistical methods are developed for analysis of clinical and virus geneticsdata from phase 3 randomized, placebo-controlled trials of vaccines againstnovel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to preventCOVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may varyby strain. The problem of assessing differential VE by viral genetics can beformulated under a competing risks model where the endpoint is virologicallyconfirmed COVID-19 and the cause-of-failure is the infecting SARS-CoV-2genotype. Strain-specific VE is defined as one minus the cause-specific hazardratio (vaccine/placebo). For the COVID-19 VE trials, the time to COVID-19 isright-censored, and a substantial percentage of failure cases are missing theinfecting virus genotype. We develop estimation and hypothesis testingprocedures for strain-specific VE when the failure time is subject to rightcensoring and the cause-of-failure is subject to missingness, focusing on $J\ge 2$ discrete categorical unordered or ordered virus genotypes. Thestratified Cox proportional hazards model is used to relate the cause-specificoutcomes to explanatory variables. The inverse probability weightedcomplete-case (IPW) estimator and the augmented inverse probability weightedcomplete-case (AIPW) estimator are investigated. Hypothesis tests are developedto assess whether the vaccine provides at least a specified level of efficacyagainst some viral genotypes and whether VE varies across genotypes, adjustingfor covariates. The finite-sample properties of the proposed tests are studiedthrough simulations and are shown to have good performances. In preparation forthe real data analyses, the developed methods are applied to a pseudo datasetmimicking the Moderna COVE trial.

Estimation and Hypothesis Testing of Strain-Specific Vaccine Efficacy with Missing Strain Types, with Applications to a COVID-19 Vaccine Trial