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SUMMARY:BSU Seminar: &quot\;A new frequentist implementation of the Daniel
 s and Hughes bivariate meta-analysis model for surrogate outcomes&quot\; -
  Dan Jackson\, AstraZeneca
DTSTART:20231121T140000Z
DTEND:20231121T150000Z
UID:TALK206248@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Surrogate outcomes are used when the primary endpoint is diffi
 cult to measure accurately. Determining if an outcome is suitable to use a
 s a surrogate is a challenging task and a variety of meta-analysis models 
 have been proposed for this purpose. The Daniels and Hughes bivariate mode
 l for surrogate outcomes is gaining traction but presents difficulties for
  frequentist estimation and hitherto only Bayesian solutions have been ava
 ilable. This is because it is non-linear and the number of unknown paramet
 ers increases at the same rate as the number of studies. This second prope
 rty raises immediate concerns that the maximum likelihood estimator of the
  between-study variance may be downwardly biased. We derive maximum likeli
 hood estimating equations to motivate a bias-adjusted estimator of this pa
 rameter. The bias-correction terms in our proposed estimating equation are
  easily computed and have an intuitively appealing algebraic form. From si
 mulation studies and empirical examples\, we conclude that our new estimat
 ion method enables satisfactory maximum likelihood-based estimation of the
  Daniels and Hughes bivariate model.
LOCATION:MRC Biostatistics Unit\, East Forvie Building\, Forvie Site Robin
 son Way Cambridge CB2 0SR.
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