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The geographical exchange of non-native insects can be highly asymmetrical, with some world regions ‘exporting’ or ‘importing’ more species than others. Several hypotheses have been proposed to explain such asymmetries, including differences in propagule pressure, environmental features in recipient regions, or biological traits of invaders. We tested aspects of these hypotheses in the context of the exchange of non-native insects between North America, Europe, and Australasia. Europe was the dominant exporter of non-native insect species between the three regions, with most of this asymmetry arising prior to 1950. The European dominance could not be explained by differences in import value, source species pool sizes, or native plant richness in the recipient regions. We identified that the introduction of non-native plants, driven in part by European colonization, best explains the asymmetrical exchange of non-native insects between our focal regions.

Bayesian analysis of a non-inferiority trial is advantageous in allowing direct probability statements to be made about the relative treatment difference rather than relying on an arbitrary and often poorly justified non-inferiority margin. When the primary analysis will be Bayesian, a Bayesian approach to sample size determination will often be appropriate for consistency with the analysis. We demonstrate three Bayesian approaches to choosing sample size for non-inferiority trials with binary outcomes and review their advantages and disadvantages. First, we present a predictive power approach for determining sample size using the probability that the trial will produce a convincing result in the final analysis. Next, we determine sample size by considering the expected posterior probability of non-inferiority in the trial. Finally, we demonstrate a precision-based approach. We apply these methods to a non-inferiority trial in antiretroviral therapy for treatment of HIV-infected children. A predictive power approach would be most accessible in practical settings, because it is analogous to the standard frequentist approach. Sample sizes are larger than with frequentist calculations unless an informative analysis prior is specified, because appropriate allowance is made for uncertainty in the assumed design parameters, ignored in frequentist calculations. An expected posterior probability approach will lead to a smaller sample size and is appropriate when the focus is on estimating posterior probability rather than on testing. A precision-based approach would be useful when sample size is restricted by limits on recruitment or costs, but it would be difficult to decide on sample size using this approach alone.

Classical biological control is being attempted for the giant willow aphid, Tuberolachnus salignus (Gmelin) (Hemiptera: Aphididae: Lachninae), an invasive pest first recorded in New Zealand in 2013. Giant willow aphid (GWA) feeds primarily on species of Salix, and is also occasionally recorded on species of Populus, Malus and Pyrus in New Zealand where it is causing problems ranging from detrimental impacts on willow trees and honey bees, to honey losses, rising vespid wasp populations and risks to fruit exports. A biological control programme against GWA was initiated by importing into containment a parasitoid wasp, Pauesia nigrovaria (Provancher) (Hymenoptera: Braconidae: Aphidiinae), reared from GWA in California, USA. Host range testing was conducted using five non-target aphid species, including representatives of all aphid subfamilies and tribes in New Zealand that contain any native species (all are distantly related to the target host, GWA), as well as a closely related exotic pest species, Cinara fresai, in the same subfamily as the target host (Lachninae). No-choice tests were negative for all non-target species. A single P. nigrovaria individual attempted to attack two C. fresai during behavioural assays. However these actions did not result in reproductive success. Our results demonstrate that P. nigrovaria is a host specific parasitoid and its release into the New Zealand environment poses negligible environmental risk.

Antimicrobial resistance is impacting treatment decisions for, and patient outcomes from, bacterial infections worldwide, with particular threats from infections with carbapenem-resistant Enterobacteriaceae, Acinetobacter baumanii, or Pseudomonas aeruginosa. Numerous areas of clinical uncertainty surround the treatment of these highly resistant infections, yet substantial obstacles exist to the design and conduct of treatment trials for carbapenem-resistant bacterial infections. These include the lack of a widely acceptable optimised standard of care and control regimens, varying antimicrobial susceptibilities and clinical contraindications making specific intervention regimens infeasible, and diagnostic and recruitment challenges. The current single comparator trials are not designed to answer the urgent public health question, identified as a high priority by WHO, of what are the best regimens out of the available options that will significantly reduce morbidity, costs, and mortality. This scenario has an analogy in network meta-analysis, which compares multiple treatments in an evidence synthesis to rank the best of a set of available treatments. To address these obstacles, we propose extending the network meta-analysis approach to individual randomisation of patients. We refer to this approach as a Personalised RAndomised Controlled Trial (PRACTical) design that compares multiple treatments in an evidence synthesis, to identify, overall, which is the best treatment out of a set of available treatments to recommend, or how these different treatments rank against each other. In this Personal View, we summarise the design principles of personalised randomised controlled trial designs. Specifically, of a network of different potential regimens for life-threatening carbapenem-resistant infections, each patient would be randomly assigned only to regimens considered clinically reasonable for that patient at that time, incorporating antimicrobial susceptibility, toxicity profile, pharmacometric properties, availability, and physician assessment. Analysis can use both direct and indirect comparisons across the network, analogous to network meta-analysis. This new trial design will maximise the relevance of the findings to each individual patient, and enable the top-ranked regimens from any personalised randomisation list to be identified, in terms of both efficacy and safety.

Effective decision making from randomised controlled clinical trials relies on robust interpretation of the numerical results. However, the language we use to describe clinical trials can cause confusion both in trial design and in comparing results across trials. ACceptability Curve Estimation using Probability Above Threshold (ACCEPT) aids comparison between trials (even where of different designs) by harmonising reporting of results, acknowledging different interpretations of the results may be valid in different situations, and moving the focus from comparison to a pre-specified value to interpretation of the trial data. ACCEPT can be applied to historical trials or incorporated into statistical analysis plans for future analyses. An online tool enables ACCEPT on up to three trials simultaneously.

This paper reports the true bugs (Heteroptera) collected during a large-scale study of saproxylic insects in exotic pine plantations in New Zealand, reared from Pinus radiata dead wood in the centr...

Background Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. Methods We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. Results The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing Conclusions Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

Systematic reviews with network meta-analysis that ignore potential dose effects could limit the applicability and validity of review findings. This article aims to help content experts (eg, clinicians), methodologists, and statisticians better understand how to incorporate dose effects in network meta-analysis. Three models are described that make different clinical and statistical assumptions about how to model dose effects. This article also illustrates the importance of dose effects in understanding the potential risk of harm in people with dementia from cerebrovascular events associated with atypical antipsychotic drug use (quetiapine, olanzapine, and risperidone) and the potential risk of harm in people with nausea and headache associated with cholinesterase inhibitor use (donepezil, galantamine, and rivastigmine). Finally, important considerations when choosing between different network meta-analysis models incorporating dose effects are discussed.

Contains fulltext : 283099.pdf (Publisher’s version ) (Open Access) BACKGROUND: Young children living with HIV have few treatment options. We aimed to assess the efficacy and safety of dolutegravir-based antiretroviral therapy (ART) in children weighing between 3 kg and less than 14 kg. METHODS: ODYSSEY is an open-label, randomised, non-inferiority trial (10% margin) comparing dolutegravir-based ART with standard of care and comprises two cohorts (children weighing ≥14 kg and

International audience; Aim The concept of "island disharmony" has been widely applied to describe the systematic over- and under-representation of taxa on islands compared to mainland regions. Here, we explore an extension of that concept to biological invasions. We compare biogeographical patterns in native and non-native beetle (Coleoptera) assemblages from around the world to test whether beetle invasions represent a random sample of species or whether some families are more prone to invade than others. Location Global. Methods Numbers of non-native beetle species established in ten regions worldwide were compared with the land area of each region. The distribution of species among families was compared with the distribution among families for all species native to the same region and with the distribution among families for the global pool of all known beetle species. Ordination analysis was used to characterize differences among native and non-native assemblages based upon the distribution of species among families. Results We report a total of 1,967 non-native beetle species across all ten regions, and a classic log-log relationship between numbers of species per region and land area though relationships are generally stronger for native assemblages. Some families (e.g., Dermestidae and Bostrichidae) are over-represented and others (e.g., Carabidae, Scarabaeidae and Buprestidae) are under-represented in non-native assemblages. The distribution of species among families is generally similar among native assemblages with greatest similarities among nearby regions. In contrast, non-native species assemblages are more similar to each other than to native species assemblages. Main conclusions Certain families are over-represented, and others are under-represented in non-native beetle assemblages compared to native assemblages, indicating "invasion disharmony" in the global representation of beetle families. Similarities in composition among non-native assemblages may reflect unobserved associations with invasion pathways and life-history traits that shape invasion success of different insect groups.

Background Systematic reviews and meta-analysis of time-to-event outcomes are frequently published within the Cochrane Database of Systematic Reviews (CDSR). However, these outcomes are handled differently across meta-analyses. They can be analysed on the hazard ratio (HR) scale or can be dichotomized and analysed as binary outcomes using effect measures such as odds ratios (OR) or risk ratios (RR). We investigated the impact of reanalysing meta-analyses from the CDSR that used these different effect measures. Methods We extracted two types of meta-analysis data from the CDSR: either recorded in a binary form only (“binary”), or in binary form together with observed minus expected and variance statistics (“OEV”). We explored how results for time-to-event outcomes originally analysed as “binary” change when analysed using the complementary log–log (clog-log) link on a HR scale. For the data originally analysed as HRs (“OEV”), we compared these results to analysing them as binary on a HR scale using the clog-log link or using a logit link on an OR scale. Results The pooled HR estimates were closer to 1 than the OR estimates in the majority of meta-analyses. Important differences in between-study heterogeneity between the HR and OR analyses were also observed. These changes led to discrepant conclusions between the OR and HR scales in some meta-analyses. Situations under which the clog-log link performed better than logit link and vice versa were apparent, indicating that the correct choice of the method does matter. Differences between scales arise mainly when event probability is high and may occur via differences in between-study heterogeneity or via increased within-study standard error in the OR relative to the HR analyses. Conclusions We identified that dichotomising time-to-event outcomes may be adequate for low event probabilities but not for high event probabilities. In meta-analyses where only binary data are available, the complementary log–log link may be a useful alternative when analysing time-to-event outcomes as binary, however the exact conditions need further exploration. These findings provide guidance on the appropriate methodology that should be used when conducting such meta-analyses.

Surrogate endpoints are very important in regulatory decision-making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on the pairwise methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods which combine data on treatment effects on the surrogate and final outcomes, from trials investigating heterogeneous treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the surrogacy patterns across multiple trials (different populations) within a treatment contrast and across treatment contrasts, thus enabling predictions of the treatment effect on the final outcome for a new study in a new population or investigating a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using simulated data and an illustrative example in advanced colorectal cancer. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatments for which surrogacy holds, thus leading to better predictions.

As part of national biosecurity programs, cargo imports, passenger baggage and international mail are inspected at ports of entry to verify compliance with phytosanitary regulations and to directly intercept potentially damaging non-native species to prevent their introduction. Detection of organisms during inspections may also provide crucial information about the species composition and relative arrival rates in invasion pathways that can inform the implementation of other biosecurity practices such as quarantines and surveillance. In most regions, insects are the main taxonomic group encountered during inspections. We gathered insect interception data from nine world regions collected from 1995 - 2019 to compare the composition of species arriving at ports in these regions. Collectively, 8,716 insect species were intercepted in these regions over the last 25 years, with the combined international dataset comprising 1,899,573 interception events, of which 863,972 were identified to species level. Rarefaction analysis indicated that interceptions comprise only a small fraction of species present in invasion pathways. Despite differences in inspection methodologies, as well as differences in the composition of import source regions and imported commodities, we found strong positive correlations in species interception frequencies between regions, particularly within the Hemiptera and Thysanoptera. There were also significant differences in species frequencies among insects intercepted in different regions. Nevertheless, integrating interception data among multiple regions would be valuable for estimating invasion risks for insect species with high likelihoods of introduction as well as for identifying rare but potentially damaging species.

Background. Non-inferiority (NI) trials are increasingly used to evaluate new treatments expected to have secondary advantages over standard of care, but similar efficacy on the primary outcome. When designing a NI trial with a binary primary outcome, the choice of effect measure for the NI margin has an important effect on sample size calculations; furthermore, if the control event risk observed is markedly different from that assumed, the trial can quickly lose power or the results become difficult to interpret. Methods. We propose a new way of designing NI trials to overcome the issues raised by unexpected control event risks by specifying a NI frontier, i.e. a curve defining the most appropriate non-inferiority margin for each possible value of control event risk. We propose a fixed arcsine difference frontier, the power-stabilising transformation for binary outcomes. We propose and compare three ways of designing a trial using this frontier. Results. Testing and reporting on the arcsine scale leads to results which are challenging to interpret clinically. Working on the arcsine scale generally requires a larger sample size compared to the risk difference scale. Therefore, working on the risk difference scale, modifying the margin after observing the control event risk, might be preferable, as it requires a smaller sample size. However, this approach tends to slightly inflate type I error rate; a solution is to use a lower significance level for testing. When working on the risk ratio scale, the same approach leads to power levels above the nominal one, maintaining type I error under control. Conclusions. Our proposed methods of designing NI trials using power-stabilising frontiers make trial design more resilient to unexpected values of the control event risk, at the only cost of requiring larger sample sizes when the goal is to report results on the risk difference scale., Comment: ~4000 words + 5 figures + 1 table + additional material (2 appendices, 1 table, 4 figures)

Background: Randomised trials included in meta-analyses are often affected by bias caused by methodological flaws or limitations, but the degree of bias is unknown. Two proposed methods adjust trial results for bias using: (1) empirical evidence from published meta-epidemiological studies; or (2) expert opinion.Methods: We investigated agreement between data-based and opinion-based approaches to assessing bias in each of four domains: sequence generation, allocation concealment, blinding and incomplete outcome data. From each sampled meta-analysis, a pair of trials with the highest and lowest empirical model-based bias estimates was selected. Independent assessors were asked which trial within each pair was judged more biased on the basis of detailed trial design summaries.Results: Assessors judged trials to be equally biased in 68% of pairs evaluated. When assessors judged one trial as more biased, the proportion of judgements agreeing with the model-based ranking was highest for allocation concealment (79%) and blinding (79%) and lower for sequence generation (59%) and incomplete outcome data (56%).Conclusions: Most trial pairs found to be discrepant empirically were judged to be equally biased by assessors. We found moderate agreement between opinion and data-based evidence in pairs where assessors ranked one trial as more biased.

Differences between arm-based (AB) and contrast-based (CB) models for network meta-analysis (NMA) are controversial. We compare the CB model of Lu and Ades (2006), the AB model of Hong et al(2016), and two intermediate models, using hypothetical data and a selected real data set. Differences between models arise primarily from study intercepts being fixed effects in the Lu-Ades model but random effects in the Hong model, and we identify four key difference. (1) If study intercepts are fixed effects then only within-study information is used, but if they are random effects then between-study information is also used and can cause important bias. (2) Models with random study intercepts are suitable for deriving a wider range of estimands, eg, the marginal risk difference, when underlying risk is derived from the NMA data; but underlying risk is usually best derived from external data, and then models with fixed intercepts are equally good. (3) The Hong model allows treatment effects to be related to study intercepts, but the Lu-Ades model does not. (4) The Hong model is valid under a more relaxed missing data assumption, that arms (rather than contrasts) are missing at random, but this does not appear to reduce bias. We also describe an AB model with fixed study intercepts and a CB model with random study intercepts. We conclude that both AB and CB models are suitable for the analysis of NMA data, but using random study intercepts requires a strong rationale such as relating treatment effects to study intercepts.

Assessing species establishment risk is an important task used for informing biosecurity activities aimed at preventing biological invasions. Propagule pressure is a major contributor to the probability of invading species establishment; however, direct assessment of numbers of individuals arriving is virtually never possible. Inspections conducted at borders by biosecurity officials record counts of species (or higher-level taxa) intercepted during inspections, which can be used as proxies for arrival rates. Such data may therefore be useful for predicting species establishments, though some species may establish despite never being intercepted. We present a stochastic process-based model of the arrival-interception-establishment process to predict species establishment risk from interception count data. The model can be used to estimate the probability of establishment, both for species that were intercepted and species that had no interceptions during a given observation period. We fit the stochastic model to data on two insect families, Cerambycidae and Aphididae, that were intercepted and/or established in the United States or New Zealand. We also explore the effects of variation in model parameters and the inclusion of an Allee effect in the establishment probability. Although interception data sets contain much noise due to variation in inspection policy, interception effort and among-species differences in detectability, our study shows that it is possible to use such data for predicting establishments and distinguishing differences in establishment risk profile between taxonomic groups. Our model provides a method for predicting the number of species that have breached border biosecurity, including both species detected during inspections but also "unseen arrivals" that have never been intercepted, but have not yet established a viable population. These estimates could inform prioritization of different taxonomic groups, pathways or identification effort in biosecurity programs.

Summary Flaws in the conduct of randomized trials can lead to biased estimation of the intervention effect. Methods for adjustment of within-trial biases in meta-analysis include the use of empirical evidence from an external collection of meta-analyses, and the use of expert opinion informed by the assessment of detailed trial information. Our aim is to present methods to combine these two approaches to gain the advantages of both. We make use of the risk of bias information that is routinely available in Cochrane reviews, by obtaining empirical distributions for the bias associated with particular bias profiles (combinations of risk of bias judgements). We propose three methods: a formal combination of empirical evidence and opinion in a Bayesian analysis; asking experts to give an opinion on bias informed by both summary trial information and a bias distribution from the empirical evidence, either numerically or by selecting areas of the empirical distribution. The methods are demonstrated through application to two example binary outcome meta-analyses. Bias distributions based on opinion informed by trial information alone were most dispersed on average, and those based on opinions obtained by selecting areas of the empirical distribution were narrowest. Although the three methods for combining empirical evidence with opinion vary in ease and speed of implementation, they yielded similar results in the two examples.

Motivated by two case studies using primary care records from the Clinical Practice Research Datalink, we describe statistical methods that facilitate the analysis of tall data, with very large numbers of observations. Our focus is on investigating the association between patient characteristics and an outcome of interest, while allowing for variation among general practices. We explore ways to fit mixed effects models to tall data, including predictors of interest and confounding factors as covariates, and including random intercepts to allow for heterogeneity in outcome among practices. We introduce: (1) weighted regression and (2) meta-analysis of estimated regression coefficients from each practice. Both methods reduce the size of the dataset, thus decreasing the time required for statistical analysis. We compare the methods to an existing subsampling approach. All methods give similar point estimates, and weighted regression and meta-analysis give similar standard errors for point estimates to analysis of the entire dataset, but the subsampling method gives larger standard errors. Where all data are discrete, weighted regression is equivalent to fitting the mixed model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful. Both methods are easy to implement in standard statistical software, The authors are grateful to the CPRD team at the University of Cambridge. In particular, we thank Carol Wilson and Anna Cassel for providing access to the case study datasets that they spent much time preparing for analysis. Kirsty Rhodes was funded by Medical Research Council Unit Programmes U105260558 and MC_UU_00002/5. Rebecca Turner and Ian White were funded by Medical Research Council Unit Programmes U105260558 and MC_UU_12023/21

Many animals can navigate from unfamiliar locations to a familiar target location with no outward route information or direct sensory contact with the target or any familiar landmarks. Several models have been proposed to explain this phenomenon, one possibility being a literal interpretation of a grid map. In this paper we systematically compare four such models, which we label: Correct Bicoordinate navigation, both Target and Release site based, Approximate Bicoordinate navigation, and Directional navigation. Predictions of spatial patterns of initial orientation errors and efficiencies depend on a combination of assumptions about the navigation mechanism and the geometry of the environmental coordinate fields used as model inputs. When coordinates axes are orthogonal at the target the predictions from the Correct Bicoordinate (Target based) model and Approximate Bicoordinate model are identical. However, if the coordinate axes are non-orthogonal different regional patterns of initial orientation errors and efficiencies can be expected from these two models. Field anomalies produce high magnitudes of orientation errors close to the target, while region-wide nonlinearity leads to orientation errors increasing with distance from the target. In general, initial orientation error patterns are more useful for distinguishing between different assumption combinations than efficiencies. We discuss how consideration of model predictions may be helpful in the design of experiments.

This paper investigates how inconsistency (as measured by the I2 statistic) among studies in a meta‐analysis may differ, according to the type of outcome data and effect measure. We used hierarchical models to analyse data from 3873 binary, 5132 continuous and 880 mixed outcome meta‐analyses within the Cochrane Database of Systematic Reviews. Predictive distributions for inconsistency expected in future meta‐analyses were obtained, which can inform priors for between‐study variance. Inconsistency estimates were highest on average for binary outcome meta‐analyses of risk differences and continuous outcome meta‐analyses. For a planned binary outcome meta‐analysis in a general research setting, the predictive distribution for inconsistency among log odds ratios had median 22% and 95% CI: 12% to 39%. For a continuous outcome meta‐analysis, the predictive distribution for inconsistency among standardized mean differences had median 40% and 95% CI: 15% to 73%. Levels of inconsistency were similar for binary data measured by log odds ratios and log relative risks. Fitted distributions for inconsistency expected in continuous outcome meta‐analyses using mean differences were almost identical to those using standardized mean differences. The empirical evidence on inconsistency gives guidance on which outcome measures are most likely to be consistent in particular circumstances and facilitates Bayesian meta‐analysis with an informative prior for heterogeneity. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

In a network meta‐analysis, between‐study heterogeneity variances are often very imprecisely estimated because data are sparse, so standard errors of treatment differences can be highly unstable. External evidence can provide informative prior distributions for heterogeneity and, hence, improve inferences. We explore approaches for specifying informative priors for multiple heterogeneity variances in a network meta‐analysis. First, we assume equal heterogeneity variances across all pairwise intervention comparisons (approach 1); incorporating an informative prior for the common variance is then straightforward. Models allowing unequal heterogeneity variances are more realistic; however, care must be taken to ensure implied variance‐covariance matrices remain valid. We consider three strategies for specifying informative priors for multiple unequal heterogeneity variances. Initially, we choose different informative priors according to intervention comparison type and assume heterogeneity to be proportional across comparison types and equal within comparison type (approach 2). Next, we allow all heterogeneity variances in the network to differ, while specifying a common informative prior for each. We explore two different approaches to this: placing priors on variances and correlations separately (approach 3) or using an informative inverse Wishart distribution (approach 4). Our methods are exemplified through application to two network metaanalyses. Appropriate informative priors are obtained from previously published evidence‐based distributions for heterogeneity. Relevant prior information on between‐study heterogeneity can be incorporated into network meta‐analyses, without needing to assume equal heterogeneity across treatment comparisons. The approaches proposed will be beneficial in sparse data sets and provide more appropriate intervals for treatment differences than those based on imprecise heterogeneity estimates.

For HIV-infected children, formulation development, pharmacokinetic (PK) data, and evaluation of early toxicity are critical for licensing new antiretroviral drugs; direct evidence of efficacy in children may not be needed if acceptable safety and PK parameters are demonstrated in children. However, it is important to address questions where adult trial data cannot be extrapolated to children. In this fast-moving area, interventions need to be tailored to resource-limited settings where most HIV-infected children live and take account of decreasing numbers of younger HIV-infected children after successful prevention of mother-to-child HIV transmission. Innovative randomized controlled trial (RCT) designs enable several questions relevant to children's treatment and care to be answered within the same study. We reflect on key considerations, and, with examples, discuss the relative merits of different RCT designs for addressing multiple scientific questions including parallel multi-arm RCTs, factorial RCTs, and cross-over RCTs. We discuss inclusion of several populations (eg, untreated and pretreated children; children and adults) in “basket” trials; incorporation of secondary randomizations after enrollment and use of nested substudies (particularly PK and formulation acceptability) within large RCTs. We review the literature on trial designs across other disease areas in pediatrics and rare diseases and discuss their relevance for addressing questions relevant to HIV-infected children; we provide an example of a Bayesian trial design in prevention of mother-to-child HIV transmission and consider this approach for future pediatric trials. Finally, we discuss the relevance of these approaches to other areas, in particular, childhood tuberculosis and hepatitis.

The placenta is the key organ of maternal–fetal interactions, where nutrient, oxygen, and waste transfer take place. Differences in the morphology of the placental chorionic surface vascular network (PCSVN) have been associated with developmental disorders such as autism, hinting that the PCSVN could potentially serve as a biomarker for early diagnosis and treatment of autism. Studying PCSVN features in large cohorts requires a reliable and automated mechanism to extract the vascular networks. This paper presents two distinct methods for PCSVN enhancement and extraction. Our first algorithm, which builds upon a directional multiscale mathematical framework based on a combination of shearlets and Laplacian eigenmaps, is able to intensify the appearance of vessels with high success in high-contrast images such as those produced in CT scans. Our second algorithm, which applies a conditional generative adversarial neural network (cGAN), was trained to simulate a human-traced PCSVN given a digital photograph of the placental chorionic surface. This method surpasses any existing automated PCSVN extraction methods reported on digital photographs of placentas. We hypothesize that a suitable combination of the two methods could further improve PCSVN extraction results and should be studied in the future.

Placenta chorionic surface vascular networks differ in individuals at-risk for autism compared to controls in terms of longer, straighter, thicker vessels; less branching; smaller changes in flow directions; and better coverage to the placental boundary. What mechanism(s) could drive these differences and how these mechanisms would impact blood transport has not been widely investigated. We used a Monte-Carlo simulation to mimic three mechanisms for controlling vascular growth: vessels grow faster and longer, terminate more frequently before branching, and flow directions are more tightly controlled in the at-risk simulations. For each mechanism, we analyzed simulated vascular networks based on structural properties and blood flow, assuming Poiseuille’s law and distensible vessels. Our simulations showed that none of these mechanisms alone could reproduce all structural properties of vascular networks in placentas identified as at-risk for autism. Terminating vessels more frequently or growing longer vessels could each reproduce longer vessels and less branching, but not greater boundary coverage or smaller changes in flow directions. As for their influence on blood flow, longer vessels and less branching have large, opposing effects on network function. Networks with longer vessels are less efficient in terms of slower flow rates and higher total network volume; in contrast, networks with less branching are more efficient. Our results suggest either these mechanisms work together to drive observed differences in vascular networks of at-risk individuals by balancing their impacts on network function; or another mechanism not considered here might drive these differences.

Objectives Estimation of between-study heterogeneity is problematic in small meta-analyses. Bayesian meta-analysis is beneficial because it allows incorporation of external evidence on heterogeneity. To facilitate this, we provide empirical evidence on the likely heterogeneity between studies in meta-analyses relating to specific research settings. Study Design and Setting Our analyses included 6,492 continuous-outcome meta-analyses within the Cochrane Database of Systematic Reviews. We investigated the influence of meta-analysis settings on heterogeneity by modeling study data from all meta-analyses on the standardized mean difference scale. Meta-analysis setting was described according to outcome type, intervention comparison type, and medical area. Predictive distributions for between-study variance expected in future meta-analyses were obtained, which can be used directly as informative priors. Results Among outcome types, heterogeneity was found to be lowest in meta-analyses of obstetric outcomes. Among intervention comparison types, heterogeneity was lowest in meta-analyses comparing two pharmacologic interventions. Predictive distributions are reported for different settings. In two example meta-analyses, incorporating external evidence led to a more precise heterogeneity estimate. Conclusion Heterogeneity was influenced by meta-analysis characteristics. Informative priors for between-study variance were derived for each specific setting. Our analyses thus assist the incorporation of realistic prior information into meta-analyses including few studies.

Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-study heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

MultiBUGS (https://www.multibugs.org) is a new version of the general-purpose Bayesian modelling software BUGS that implements a generic algorithm for parallelising Markov chain Monte Carlo (MCMC) algorithms to speed up posterior inference of Bayesian models. The algorithm parallelises evaluation of the product-form likelihoods formed when a parameter has many children in the directed acyclic graph (DAG) representation; and parallelises sampling of conditionally-independent sets of parameters. A heuristic algorithm is used to decide which approach to use for each parameter and to apportion computation across computational cores. This enables MultiBUGS to automatically parallelise the broad range of statistical models that can be fitted using BUGS-language software, making the dramatic speed-ups of modern multi-core computing accessible to applied statisticians, without requiring any experience of parallel programming. We demonstrate the use of MultiBUGS on simulated data designed to mimic a hierarchical e-health linked-data study of methadone prescriptions including 425,112 observations and 20,426 random effects. Posterior inference for the e-health model takes several hours in existing software, but MultiBUGS can perform inference in only 28 minutes using 48 computational cores., 20 pages, 4 figures

Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics.

ObjectiveWe investigated the associations between risk of bias judgments from Cochrane reviews for sequence generation, allocation concealment and blinding and between-trial heterogeneity.Study Design and SettingBayesian hierarchical models were fitted to binary data from 117 meta-analyses, to estimate the ratio λ by which heterogeneity changes for trials at high/unclear risk of bias, compared to trials at low risk of bias. We estimated the proportion of between-trial heterogeneity in each meta-analysis that could be explained by the bias associated with specific design characteristics.ResultsUnivariable analyses showed that heterogeneity variances were, on average, increased among trials at high/unclear risk of bias for sequence generation (Math Eq 1.14, 95% interval: 0.57 to 2.30) and blinding (Math Eq 1.74, 95% interval: 0.85 to 3.47). Trials at high/unclear risk of bias for allocation concealment were on average less heterogeneous (Math Eq 0.75, 95% interval: 0.35 to 1.61). Multivariable analyses showed that a median of 37% (95% interval: 0% to 71%) heterogeneity variance could be explained by trials at high/unclear risk of bias for sequence generation, allocation concealment and/or blinding. All 95% intervals for changes in heterogeneity were wide and included the null of no difference.ConclusionOur interpretation of the results is limited by imprecise estimates. There is some indication that between-trial heterogeneity could be partially explained by reported design characteristics, and hence adjustment for bias could potentially improve accuracy of meta-analysis results.

Insect odorant receptors are heteromeric odorant-gated cation channels comprising a conventional odorant-sensitive tuning receptor (ORx) and a highly conserved co-receptor known as Orco. Orco is found only in insects, and very little is known about its structure and the mechanism leading to channel activation. In the absence of an ORx, Orco forms homomeric channels that can be activated by a synthetic agonist, VUAA1. Drosophila melanogaster Orco (DmelOrco) contains eight cysteine amino acid residues, six of which are highly conserved. In this study, we replaced individual cysteine residues with serine or alanine and expressed Orco mutants in Flp-In 293 T-Rex cells. Changes in intracellular Ca(2+) levels were used to determine responses to VUAA1. Replacement of two cysteines (Cys-429 and Cys-449) in a predicted intracellular loop (ICL3), individually or together, gave variants that all showed similar increases in the rate of response and sensitivity to VUAA1 compared with wild-type DmelOrco. Kinetic modeling indicated that the response of the Orco mutants to VUAA1 was faster than wild-type Orco. The enhanced sensitivity and faster response of the Cys mutants was confirmed by whole-cell voltage clamp electrophysiology. In contrast to the results from direct agonist activation of Orco, the two cysteine replacement mutants when co-expressed with a tuning receptor (DmelOR22a) showed an ∼10-fold decrease in potency for activation by 2-methyl hexanoate. Our work has shown that intracellular loop 3 is important for Orco channel activation. Importantly, this study also suggests differences in the structural requirements for the activation of homomeric and heteromeric Orco channel complexes.

Summary The ‘fruity’ attributes of ripe apples (Malus × domestica) arise from our perception of a combination of volatile ester compounds. Phenotypic variability in ester production was investigated using a segregating population from a ‘Royal Gala’ (RG; high ester production) × ‘Granny Smith’ (GS; low ester production) cross, as well as in transgenic RG plants in which expression of the alcohol acyl transferase 1 (AAT1) gene was reduced. In the RG × GS population, 46 quantitative trait loci (QTLs) for the production of esters and alcohols were identified on 15 linkage groups (LGs). The major QTL for 35 individual compounds was positioned on LG2 and co-located with AAT1. Multiple AAT1 gene variants were identified in RG and GS, but only two (AAT1-RGa and AAT1-GSa) were functional. AAT1-RGa and AAT1-GSa were both highly expressed in the cortex and skin of ripe fruit, but AAT1 protein was observed mainly in the skin. Transgenic RG specifically reduced in AAT1 expression showed reduced levels of most key esters in ripe fruit. Differences in the ripe fruit aroma could be perceived by sensory analysis. The transgenic lines also showed altered ratios of biosynthetic precursor alcohols and aldehydes, and expression of a number of ester biosynthetic genes increased, presumably in response to the increased substrate pool. These results indicate that the AAT1 locus is critical for the biosynthesis of esters contributing to a ‘ripe apple’ flavour.

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Background Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. Methods The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. Results We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses “ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. Conclusions The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported. Electronic supplementary material The online version of this article (doi:10.1186/s12874-016-0184-5) contains supplementary material, which is available to authorized users.

Network meta‐analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta‐analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi‐parametric, non‐iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Detection of hepatocellular carcinoma (HCC) through screening can improve outcomes. However, HCC surveillance remains costly, cumbersome and suboptimal. We tested whether and how serum Alpha-Fetoprotein (AFP) should be used in HCC surveillance. Record linkage, dedicated pathways for management and AFP data-storage identified i) consecutive highly characterised cases of HCC diagnosed in 2009–14 and ii) a cohort of ongoing HCC-free patients undergoing regular HCC surveillance from 2009. These two well-defined Scottish patient cohorts enabled us to test the utility of AFP surveillance. Of 304 cases of HCC diagnosed over 6 years, 42% (129) were identified by a dedicated HCC surveillance programme. Of these 129, 47% (61) had a detectable lesion first identified by screening ultrasound (US) but 38% (49) were prompted by elevated AFP. Despite pre-HCC diagnosis AFP >20kU/L being associated with poor outcome, ‘AFP-detected’ tumours were offered potentially curative management as frequently as ‘US-detected’ HCCs; and had comparable survival. Linearity of serial log10-transformed AFPs in HCC cases and in the screening ‘HCC-free’ cohort (n = 1509) provided indicators of high-risk AFP behaviour in HCC cases. An algorithm was devised in static mode, then tested dynamically. A case/control series in hepatitis C related disease demonstrated highly significant detection (p

Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-study heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of heterogeneity in particular areas of health care. Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-study heterogeneity variance. Predictive distributions were obtained for the heterogeneity expected in future meta-analyses. Results Between-study heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10-26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, heterogeneity was on average 75% (95% CI 58-95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for heterogeneity is a log-normal (-2.13, 1.58(2)) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller heterogeneity estimate and a narrower confidence interval for the combined intervention effect. Conclusions Meta-analysis characteristics were strongly associated with the degree of between-study heterogeneity, and predictive distributions for heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies.

Summary Combining information from multiple surveys can improve the quality of small area estimates. Customary approaches, such as the multiple-frame and statistical matching methods, require individual level data, whereas in practice often only multiple aggregate estimates are available. Commercial surveys usually produce such estimates without clear description of the methodology that is used. In this context, bias modelling is crucial, and we propose a series of Bayesian hierarchical models which allow for additive biases. Some of these models can also be fitted in a classical context, by using a mixed effects framework. We apply these methods to obtain estimates of smoking prevalence in local authorities across the east of England from seven surveys. All the surveys provide smoking prevalence estimates and confidence intervals at the local authority level, but they vary by time, sample size and transparency of methodology. Our models adjust for the biases in commercial surveys but incorporate information from all the sources to provide more accurate and precise estimates.

SummaryPolicy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.