Dear all,
The next meeting of the RSS Avon Local group, will be taking place on the 25th of May,
here at Bristol University
(Room 410, Graduate School of Education, 35 Berkeley Square, with refreshments served at 4.30 pm)
The theme of the meeting is on Evidence synthesis in medicine,
organised by Chris Rogers ([log in to unmask])
with speakers: Nicky Welton and Sofia Dias from the Department of
Primary Health Care, University of Bristol.
Details on how to get there can be found at
http://www.cmm.bristol.ac.uk/research/rss-users-group.shtml
Titles and abstracts of the talks are as follows:
Models for potentially biased evidence in meta-analysis using
empirically based priors
by Nicky Welton <http://www.bristol.ac.uk/primaryhealthcare/staff/nickyw.htm>
*Abstract:* We present models for the combined analysis of evidence from randomized controlled trials categorized as being at either low or high risk of bias due to a flaw in their conduct. We formulate a bias model that incorporates between-study and between-meta-analysis heterogeneity in bias, and uncertainty in overall mean bias.We obtain algebraic expressions for the posterior distribution of the bias-adjusted treatment effect, which provide limiting values for the information that can be obtained from studies at high risk of bias. The parameters of the bias model can be estimated from collections of previously published meta-analyses. We explore alternative models for such data, and alternative methods for introducing prior information on the bias parameters into a new meta-analysis. Results from an illustrative example show that the bias-adjusted treatment effect estimates are sensitive to the way in which the meta-epidemiological data are modelled, but that using point estimates for bias parameters provides an adequate approximation to using a full joint prior distribution. A sensitivity analysis shows that the gain in precision from including studies at high risk of bias is likely to be low, however numerous or large their size, and that little is gained by incorporating such studies, unless the information from studies at low risk of bias is limited.We discuss approaches that might increase the value of including studies at high risk of bias, and the acceptability of the methods in the evaluation of health care interventions.
Estimation and adjustment of bias in randomised evidence using
mixed treatment comparison meta-analysis
by Sofia Dias <http://www.bristol.ac.uk/primaryhealthcare/staff/sofiad.htm>
*Abstract:* There is good empirical evidence that specific flaws in the conduct of randomised controlled trials are associated with exaggeration of treatment effect estimates. Mixed Treatment Comparison (MTC) meta-analysis, which combines data from trials on several treatments that form a network of comparisons, has the potential to both estimate bias parameters within the synthesis, and to produce bias-adjusted estimates of treatment effects. We present a hierarchical model for bias with common mean across treatment comparisons of active treatment vs control. It is often unclear, from the information reported, whether a study is at risk of bias or not. We extend our model to estimate the probability that a particular study is biased, where the probabilities for the “unclear” studies are drawn from a common beta distribution. We illustrate these methods with a synthesis of 130 trials on 4 fluoride treatments and two control interventions for the prevention of dental caries in children. Whether there is adequate allocation concealment and/or blinding are considered as indicators of whether a study is at risk of bias. Bias adjustment reduces the estimated relative efficacy of the treatments and the extent of between-trial heterogeneity.
Looking forward to seeing you all there
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