BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Thursday 22nd October 2026Time: 15:00 - 16:00 GMTLocation : Online via ZoomWho is this event intended for? \;Statisticians with an interest in vaccine developmentWhat is the benefit of attending? \; To learn about the recent application of statistical immune correlate anal ysis in vaccine development. Specifically\, an application of such analyse s to evaluate hemagglutination inhibition titers for an influenza vaccineO verviewHemagglutination inhibition antibody (HAI) titers are widely utiliz ed as correlates of protection (CoP) and surrogate endpoints for predictin g influenza vaccine efficacy (VE). In the phase 3 FLUENT trial (NCT0660202 4)\, which enrolled over 40\,000 participants &ge\;50 years\, the mRNA-bas ed seasonal influenza vaccine\, mRNA-1010\, demonstrated superior VE and e licited stronger antibody responses than a licensed standard-dose inactiva ted influenza vaccine. Here\, in the immune correlate analysis of the FLUE NT trial\, we evaluated hemagglutination inhibition (HAI) titers against i nfluenza A/H1N1\, A/H3N2\, and B/Victoria strains as correlates of risk (C oR) and CoP for strain-specific influenza-like illness (ILI) following vac cination. Across multiple statistical and causal inference frameworks\, hi gher Day 29 influenza A strain-specific HAI titers were consistently assoc iated with lower ILI and higher relative VE. The estimated hazard ratios p er one standard deviation increase in HAI titers were 0.48 (95% CI: 0.43&n dash\;0.54\; P<\;0.001) for A/H1N1 and 0.66 (0.58&ndash\;0.74\; P<\;0. 001) for A/H3N2. Although B/Victoria results in FLUENT were inconclusive\, likely reflecting limited cases and regional variability\, pooled analyse s across phase 3 trials showed consistent inverse associations for both va ccines. These findings support post-vaccination HAI titers as a statistica l CoP and surrogate marker of clinical protection for mRNA-based influenza vaccines\, consistent with conventional inactivated influenza vaccines.&n bsp\; \;RegistrationThis webinar is free to attend for PSI Members &am p\; Non-Members.To register for this event\, please \;click here.Speak er detailsSpeaker\n Biography\n Chong Ma\, Associate Director\, \;Mode rna Therapeutics Inc.Chong Ma\, PhD\, is an Associate Director at Moderna\ , specializing in statistical methodology and biomarker research for respi ratory and latent virus vaccines and oncology therapeutics. His work focus es on developing and applying advanced statistical models to evaluate clin ical biomarkers as surrogate endpoints and correlates of risk and/or prote ction across multiple vaccine platforms\, including influenza\, COVID-19\, and RSV. Dr. Ma has authored several peer-reviewed publications on statis tical methodology and correlates of protection modeling.\n  \; DTEND:20261022T160000Z DTSTAMP:20260908T162620Z DTSTART:20261022T150000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Vaccines SIG Webinar: Statistical Immune Correlate Analysis of Hemagglutination Inhibition Titers for an mRNA-based Influenza Vaccine UID:RFCALITEM639244815805421751 X-ALT-DESC;FMTTYPE=text/html:

Dat e: Thursday 22nd October 2026
Time: 15:00 - 16:00 GMT
Location: Online via Zoom

Who is this event in tended for? \;Statisticians with an interest in vaccine development

< p>What is the benefit of attending?  \;To learn about the recent application of statistical immune correlate an alysis in vaccine development. Specifically\, an application of such analy ses to evaluate hemagglutination inhibition titers for an influenza vaccin e

Overview

Hemagglutination inhibition antibody (HAI) titers are widely utilized as correlates of protection (CoP) and surrogate endpo ints for predicting influenza vaccine efficacy (VE). In the phase 3 FLUENT trial (NCT06602024)\, which enrolled over 40\,000 participants &ge\;50 ye ars\, the mRNA-based seasonal influenza vaccine\, mRNA-1010\, demonstrated superior VE and elicited stronger antibody responses than a licensed stan dard-dose inactivated influenza vaccine.

Here\, in the immune corre late analysis of the FLUENT trial\, we evaluated hemagglutination inhibiti on (HAI) titers against influenza A/H1N1\, A/H3N2\, and B/Victoria strains as correlates of risk (CoR) and CoP for strain-specific influenza-like il lness (ILI) following vaccination. Across multiple statistical and causal inference frameworks\, higher Day 29 influenza A strain-specific HAI titer s were consistently associated with lower ILI and higher relative VE. The estimated hazard ratios per one standard deviation increase in HAI titers were 0.48 (95% CI: 0.43&ndash\;0.54\; P<\;0.001) for A/H1N1 and 0.66 (0. 58&ndash\;0.74\; P<\;0.001) for A/H3N2.

Although B/Victoria resul ts in FLUENT were inconclusive\, likely reflecting limited cases and regio nal variability\, pooled analyses across phase 3 trials showed consistent inverse associations for both vaccines. These findings support post-vaccin ation HAI titers as a statistical CoP and surrogate marker of clinical pro tection for mRNA-based influenza vaccines\, consistent with conventional i nactivated influenza vaccines. \; \;

Registration

This webinar is free to at tend for PSI Members &\; Non-Members.
To register for this event\, please \;click here.

Speaker details

 \;

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Speaker\n Biography\n

Chong Ma\, Associate Director\, \;Moderna Therapeutics Inc.

Chong M a\, PhD\, is an Associate Director at Moderna\, specializing in statistica l methodology and biomarker research for respiratory and latent virus vacc ines and oncology therapeutics. His work focuses on developing and applyin g advanced statistical models to evaluate clinical biomarkers as surrogate endpoints and correlates of risk and/or protection across multiple vaccin e platforms\, including influenza\, COVID-19\, and RSV. Dr. Ma has authore d several peer-reviewed publications on statistical methodology and correl ates of protection modeling.\n