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The recent introductions of chikungunya virus (CHIKV) and Zika virus (ZIKV) in the Americas led to widespread epidemics with substantial health and economic effects. Small island developing states in the [...]

Randomized comparisons among several treatments give rise to an incomplete-blocks structure known as mixed treatment comparisons (MTCs). To analyze such data structures, it is crucial to assess whether the disparate evidence sources provide consistent information about the treatment contrasts. In this article we propose a general method for assessing evidence inconsistency in the framework of Bayesian hierarchical models. We begin with the distinction between basic parameters, which have prior distributions, and functional parameters, which are defined in terms of basic parameters. Based on a graphical analysis of MTC structures, evidence inconsistency is defined as a relation between a functional parameter and at least two basic parameters, supported by at least three evidence sources. The inconsistency degrees of freedom (ICDF) is the number of such inconsistencies. We represent evidence consistency as a set of linear relations between effect parameters on the log odds ratio scale, then relax these relations to allow for inconsistency by adding to the model random inconsistency factors (ICFs). The number of ICFs is determined by the ICDF. The overall consistency between evidence sources can be assessed by comparing models with and without ICFs, whereas their posterior distribution reflects the extent of inconsistency in particular evidence cycles. The methods are elucidated using two published datasets, implemented with standard Markov chain Monte Carlo software. KEY WORDS: Bayesian hierarchical model; Direct and indirect evidence; Evidence cycle; Inconsistency degrees of freedom; Inconsistency factor; Mixed treatment comparison; Random-effects model; WinBUGS.

Markov transition models are frequently used to model disease progression. The authors show how the solution to Kolmogorov's forward equations can be exploited to map between transition rates and probabilities [...]

This article shows that the interpretation of the random-effects-models used inmeta-analysis to summarize heterogeneous treatment effects can have a marked effect on the results from decision models. Sources of variation [...]

A new method of estimating the HIV transmission rate in mother-to-child cases and the age distribution of diagnosis, otherwise known as the pediatric AIDS incubation period, is introduced. Unlike other methods which lead to biased estimates, it employs the EM algorithm to determine the maximum likelihood estimates of vertical transmission rates, sensitivity of virus tests, age distribution at antibody loss and pediatric AIDS incubation period using clinical, immunological and virological information., This article considers the estimation of the rate of vertical human immunodeficiency virus (HIV) transmission and the pediatric acquired immune deficiency syndrome (AIDS) incubation period using data from birth cohort [...]

This letter draws attention to errors in our recent article on heterogeneity in meta-analysis (1) and provides a correction. The context for the original article was as follows: A random [...]

Background: When there are structural relationships between outcomes reported in different trials, separate analyses of each outcome do not provide a single coherent analysis, which is required for decision‐making. For example, trials of intrapartum anti‐bacterial prophylaxis (IAP) to prevent early onset group B streptococcal (EOGBS) disease can report three treatment effects: the effect on bacterial colonisation of the newborn, the effect on EOGBS, and the effect on EOGBS conditional on newborn colonisation. These outcomes are conditionally related, or nested, in a multi‐state model. This paper shows how to exploit these structural relationships, providing a single coherent synthesis of all the available data, while checking to ensure that different sources of evidence are consistent. Results: Overall, the use of IAP reduces the risk of EOGBS (RR: 0.03; 95% Credible Interval (CrI): 0.002–0.13). Most of the treatment effect is due to the prevention of colonisation in newborns of colonised mothers (RR: 0.08, 95% CrI: 0.04–0.14). Node‐splitting demonstrated that the treatment effect calculated using only direct evidence was consistent with that predicted from the remaining evidence (p = 0.15). The findings accorded with previously published separate meta‐analyses of the different outcomes, once these are re‐analysed correctly accounting for zero cells. Conclusion: Multiple outcomes should be synthesised together where possible, taking account of their structural relationships. This generates an internally coherent analysis, suitable for decision making, in which estimates of each of the treatment effects are based on all available evidence (direct and indirect). Separate meta‐analyses of each outcome have none of these properties. [ABSTRACT FROM AUTHOR]