Bmc Med Res Methodol
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Bmc Med Res Methodol · Jan 2013
Comparative StudyModelling heterogeneity variances in multiple treatment comparison meta-analysis--are informative priors the better solution?
Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial variance for all involved treatment comparisons are equal (i.e., the 'common variance' assumption). This approach 'borrows strength' for heterogeneity estimation across treatment comparisons, and thus, ads valuable precision when data is sparse. The homogeneous variance assumption, however, is unrealistic and can severely bias variance estimates. Consequently 95% credible intervals may not retain nominal coverage, and treatment rank probabilities may become distorted. Relaxing the homogeneous variance assumption may be equally problematic due to reduced precision. To regain good precision, moderately informative variance priors or additional mathematical assumptions may be necessary. ⋯ MTC models using a homogenous variance structure appear to perform sub-optimally when between-trial variances vary between comparisons. Using informative variance priors, assuming exchangeability or imposing consistency between heterogeneity variances can all ensure sufficiently reliable and realistic heterogeneity estimation, and thus more reliable MTC inferences. All four approaches should be viable candidates for replacing or supplementing the conventional homogeneous variance MTC model, which is currently the most widely used in practice.
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Bmc Med Res Methodol · Jan 2013
Comparative StudyComparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study.
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses. ⋯ GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
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Bmc Med Res Methodol · Jan 2013
ReviewInconsistency in the items included in tools used in general health research and physical therapy to evaluate the methodological quality of randomized controlled trials: a descriptive analysis.
Assessing the risk of bias of randomized controlled trials (RCTs) is crucial to understand how biases affect treatment effect estimates. A number of tools have been developed to evaluate risk of bias of RCTs; however, it is unknown how these tools compare to each other in the items included. The main objective of this study was to describe which individual items are included in RCT quality tools used in general health and physical therapy (PT) research, and how these items compare to those of the Cochrane Risk of Bias (RoB) tool. ⋯ There is extensive item variation across tools that evaluate the risk of bias of RCTs in health research. Results call for an in-depth analysis of items that should be used to assess risk of bias of RCTs. Further empirical evidence on the use of individual items and the psychometric properties of risk of bias tools is needed.
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Bmc Med Res Methodol · Jan 2013
Comparative StudyClinicalTrials.gov registration can supplement information in abstracts for systematic reviews: a comparison study.
The inclusion of randomized controlled trials (RCTs) reported in conference abstracts in systematic reviews is controversial, partly because study design information and risk of bias is often not fully reported in the abstract. The Association for Research in Vision and Ophthalmology (ARVO) requires trial registration of abstracts submitted for their annual conference as of 2007. Our goal was to assess the feasibility of obtaining study design information critical to systematic reviews, but not typically included in conference abstracts, from the trial registration record. ⋯ RCT design information not reported in conference abstracts is often available in the corresponding ClinicalTrials.gov registration record. Sometimes there is conflicting information reported in the two sources and further contact with the trial investigators may still be required.
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Bmc Med Res Methodol · Jan 2013
Assessing potential sources of clustering in individually randomised trials.
Recent reviews have shown that while clustering is extremely common in individually randomised trials (for example, clustering within centre, therapist, or surgeon), it is rarely accounted for in the trial analysis. Our aim is to develop a general framework for assessing whether potential sources of clustering must be accounted for in the trial analysis to obtain valid type I error rates (non-ignorable clustering), with a particular focus on individually randomised trials. ⋯ Clustering is common in individually randomised trials. Trialists should assess potential sources of clustering during the planning stages of a trial, and account for any sources of non-ignorable clustering in the trial analysis.