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