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Activity Number: 605
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract - #309963
Title: Semiparametric Network Meta-Analysis of Survival Probabilities in Psychiatric Trials
Author(s): Samprit Banerjee*+
Companies: Weill Cornell Medical College
Keywords: meta-analysis ; survival probabilities ; gaussian process ; semiparametric ; mental health ; depression
Abstract:

Although direct randomized comparison is the most efficient way of comparing different treatment/therapies, many studies compare a new treatment (say A) with a gold standard (C) while other studies compare another treatment (B) with C. The AB comparison might be of interest when it is not directly available. Network Meta-analysis provides a way to get such indirect evidence by combining information from multiple studies with different treatments, instead of numerous pairwise comparisons. Often, time to remission of a psychiatric disorder (e.g. depression) is of interest for the comparison of various treatments/therapies. Weighted average of hazard ratios is the most common method for meta-analysis of survival outcomes. However, such methods are unable to combine survival curves from multiple studies and hence unable to conduct inference on risk progression over time. Here, I present a multivariate random-effects model to combine survival probabilities at multiple time-points. I generalize this to estimate cumulative hazard function non-parametrically by a Gaussian process in a flexible linear mixed model framework. I demonstrate the advantages of this approach via simulations.


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