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An objective of phase 3 preventive COVID-19 vaccine efficacy trials is to assess immunological biomarkers measured post-vaccination as surrogate endpoints that can constitute the basis for traditional or accelerated approval for the tested vaccine or for other related vaccines. Evidence supporting reliable use of biomarkers for such approval approaches may be based on a synthesis of results from a variety of types of statistical analyses of phase 3 trial data, combined with external data on evidence for biomarkers as mechanisms of protection (e.g., from passive antibody transfer challenge studies in animals or humans). In this talk I will summarize current thinking of COVID-19 Prevention Trials Network statisticians on statistical methodological approaches for evaluating the validity of candidate surrogate endpoints from phase 3 trial data, which includes immunological biomarker sampling design, immunological biomarker selection and optimization, methods for combining biomarkers to develop best predictors of study outcomes, principal stratification approaches to assessing vaccine efficacy moderation, causal mediation approaches to assessing the proportion of vaccine efficacy mediated through one or more biomarkers, information-theoretic approaches for quantifying surrogate endpoint predictiveness, and meta-analysis.