[Show abstract][Hide abstract] ABSTRACT:
Cost-effectiveness analysis often requires information on the effectiveness of interventions on multiple outcomes, and commonly these take the form of competing risks. Nevertheless, methods for synthesis of randomized controlled trials with competing risk outcomes are limited.
The aim of this study was to develop and illustrate flexible evidence synthesis methods for trials reporting competing risk results, which allow for studies with different follow-up times, and that take account of the statistical dependencies between outcomes, regardless of the number of outcomes and treatments.
We propose a competing risk meta-analysis based on hazards, rather than probabilities, estimated in a Bayesian Markov chain Monte Carlo (MCMC) framework using WinBUGS software. Our approach builds on existing work on mixed treatment comparison (network) meta-analysis, which can be applied to any number of treatments, and any number of competing outcomes, and to data sets with varying follow-up times. We show how a fixed effect model can be estimated, and two random treatment effect models with alternative structures for between-trial variation. We suggest methods for choosing between these alternative models.
We illustrate the methods by applying them to a data set involving 17 trials comparing nine antipsychotic treatments for schizophrenia including placebo, on three competing outcomes: relapse, discontinuation because of intolerable side effects, and discontinuation for other reasons.
Bayesian MCMC provides a flexible framework for synthesis of competing risk outcomes with multiple treatments, particularly suitable for embedding within probabilistic cost-effectiveness analysis.
Value in Health 12/2010; 13(8):976-83. DOI:10.1111/j.1524-4733.2010.00784.x · 2.89 Impact Factor
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Recent reports indicate that statins are associated with an increased risk for new-onset diabetes mellitus (DM) compared with placebo and that this relation is dose dependent. The aim of this study was to perform a comprehensive network meta-analysis of randomized controlled trials (RCTs) investigating the impact of different types and doses of statins on new-onset DM. RCTs comparing different types and doses of statins with placebo were searched for using the MEDLINE, Embase, and Cochrane databases. A search of RCTs pertinent to this meta-analysis covering the period from November 1994 to October 2012 was conducted by 2 independent investigators using the MEDLINE, Cochrane, Google Scholar, and Embase databases as well as abstracts and presentations from major cardiovascular meetings. Seventeen RCTs reporting the incidence of new-onset DM during statin treatment and including a total of 113,394 patients were identified. The RCTs compared either a statin versus placebo or high-dose versus moderate-dose statin therapy. Among different statins, pravastatin 40 mg/day was associated with the lowest risk for new-onset DM compared with placebo (odds ratio 1.07, 95% credible interval 0.86 to 1.30). Conversely, rosuvastatin 20 mg/day was numerically associated with 25% increased risk for DM compared with placebo (odds ratio 1.25, 95% credible interval 0.82 to 1.90). The impact on DM appeared to be intermediate with atorvastatin 80 mg/day compared with placebo (odds ratio 1.15, 95% credible interval 0.90 to 1.50). These findings were replicated at moderate doses. In conclusion, different types and doses of statins show different potential to increase the incidence of DM.
The American journal of cardiology 01/2013; DOI:10.1016/j.amjcard.2012.12.037 · 3.58 Impact Factor
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