1,720,984 research outputs found
Discrete Approximation of a Mixture Distribution via Restricted Divergence
No full textMixture distributions arise in many application areas, for example, as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (divergence restricting conditional tesselation) algorithm is set up such that a prespecified precision, defined in terms of Kullback–Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is demonstrated in two examples. Supplementary materials for this article are available online.</p
Comparison of disability scores and annualised relapse rates as trial outcomes in randomized, placebo controlled trials in relapsing multiple sclerosis: a systematic review
No full textComparison of disability scores and annualised relapse rates as trial outcomes in randomized, placebo controlled trials in relapsing multiple sclerosis: a systematic review
No full textEvidence synthesis for count distributions based on heterogeneous and incomplete aggregated data
No full textThe analysis of count data is commonly done using Poisson models. Negative binomial models are a straightforward and readily motivated generalization for the case of overdispersed data, that is, when the observed variance is greater than expected under a Poissonian model. Rate and overdispersion parameters then need to be considered jointly, which in general is not trivial. Here, we are concerned with evidence synthesis in the case where the reporting of data is rather heterogeneous, that is, events are reported either in terms of mean event counts, the proportion of event-free patients, or rate estimates and standard errors. Either figure carries some information about the relevant parameters, and it is the joint modeling that allows for coherent inference on the parameters of interest. The methods are motivated and illustrated by a systematic review in chronic obstructive pulmonary disease.Oskar und Helene Medizinprei
Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies
Full text linkBackground: Random-effects meta-analysis is commonly performed by first deriving an estimate of the between-study variation, the heterogeneity, and subsequently using this as the basis for combining results, i.e., for estimating the effect, the figure of primary interest. The heterogeneity variance estimate however is commonly associated with substantial uncertainty, especially in contexts where there are only few studies available, such as in small populations and rare diseases. Methods: Confidence intervals and tests for the effect may be constructed via a simple normal approximation, or via a Student-t distribution, using the Hartung-Knapp-Sidik-Jonkman (HKSJ) approach, which additionally uses a refined estimator of variance of the effect estimator. The modified Knapp-Hartung method (mKH) applies an ad hoc correction and has been proposed to prevent counterintuitive effects and to yield more conservative inference. We performed a simulation study to investigate the behaviour of the standard HKSJ and modified mKH procedures in a range of circumstances, with a focus on the common case of meta-analysis based on only a few studies. Results: The standard HKSJ procedure works well when the treatment effect estimates to be combined are of comparable precision, but nominal error levels are exceeded when standard errors vary considerably between studies (e.g. due to variations in study size). Application of the modification on the other hand yields more conservative results with error rates closer to the nominal level. Differences are most pronounced in the common case of few studies of varying size or precision. Conclusions: Use of the modified mKH procedure is recommended, especially when only a few studies contribute to the meta-analysis and the involved studies' precisions (standard errors) vary.European Union [FP HEALTH 2013-602144, 44
Meta‐analysis of few small studies in orphan diseases
No full textMeta-analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random-effects meta-analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between-trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp-Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between-trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half-normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases. (C) 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd
Using phase II data for the analysis of phase III studies: An application in rare diseases
No full textBackground: Clinical research and drug development in orphan diseases are challenging, since large-scale randomized studies are difficult to conduct. Formally synthesizing the evidence is therefore of great value, yet this is rarely done in the drug-approval process. Phase III designs that make better use of phase II data can facilitate drug development in orphan diseases. Methods: A Bayesian meta-analytic approach is used to inform the phase III study with phase II data. It is particularly attractive, since uncertainty of between-trial heterogeneity can be dealt with probabilistically, which is critical if the number of studies is small. Furthermore, it allows quantifying and discounting the phase II data through the predictive distribution relevant for phase III. A phase III design is proposed which uses the phase II data and considers approval based on a phase III interim analysis. The design is illustrated with a non-inferiority case study from a Food and Drug Administration approval in herpetic keratitis (an orphan disease). Design operating characteristics are compared to those of a traditional design, which ignores the phase II data. Results: An analysis of the phase II data reveals good but insufficient evidence for non-inferiority, highlighting the need for a phase III study. For the phase III study supported by phase II data, the interim analysis is based on half of the patients. For this design, the meta-analytic interim results are conclusive and would justify approval. In contrast, based on the phase III data only, interim results are inconclusive and require further evidence. Conclusion: To accelerate drug development for orphan diseases, innovative study designs and appropriate methodology are needed. Taking advantage of randomized phase II data when analyzing phase III studies looks promising because the evidence from phase II supports informed decision-making. The implementation of the Bayesian design is straightforward with public software such as R. </jats:sec
Interleukin‐2 receptor antagonists for pediatric liver transplant recipients: A systematic review and meta‐analysis of controlled studies
No full textIL-2RA are frequently used as induction therapy in liver transplant recipients to decrease the risk of AR while allowing the reduction of concomitant immunosuppression. The exact association with the use of IL-2RA, however, is uncertain. We performed a systematic literature search for relevant studies. Random effects models were used to assess the incidence of AR, steroid-resistant rejection, graft loss, patient death, and adverse drug reaction, with or without IL-2RA. Six studies (two randomized and four non-randomized) met the eligibility criteria. Acute rejection at sixmonths or later favored the use of IL-2RA significantly (RR 0.38; 95% CI 0.22-0.66, p=0.0005). Although not statistically significant, IL-2RA showed a substantial reduction of the risk of steroid-resistant rejection (RR 0.32; CI 0.19-1.03, p=0.0594). Graft loss and patient death showed a reductive tendency through the use of IL-2RA. The use of IL-2RA is safe and is associated with a statistically significantly lower incidence of AR after transplantation and substantial reduction of steroid-resistant rejection, graft loss, and patient death
Time trends in baseline characteristics and eligibility criteria in trials in relapsing mutliple sclerosis - a systematic review and meta-analysis
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