Visualization of species pairwise associations: a case study of surrogacy in bird assemblages.

Quantifying and visualizing species associations are important to many areas of ecology and conservation biology. Species networks are one way to analyze species associations, with a growing number of applications such as food webs, nesting webs, plant-animal mutualisms, and interlinked extinctions. We present a new method for assessing and visualizing patterns of co-occurrence of species. The method depicts interactions and associations in an analogous way with existing network diagrams for studying pollination and trophic interactions, but adds the assessment of sign, strength, and direction of the associations. This provides a distinct advantage over existing methods of quantifying and visualizing co-occurrence. We demonstrate the utility of our new approach by showing differences in associations among woodland bird species found in different habitats and by illustrating the way these can be interpreted in terms of underlying ecological mechanisms. Our new method is computationally feasible for large assemblages and provides readily interpretable effects with standard errors. It has wide applications for quantifying species associations within ecological communities, examining questions about particular species that occur with others, and how their associations can determine the structure and composition of communities.

Global meta-analysis reveals low consistency of biodiversity congruence relationships.

Knowledge of the number and distribution of species is fundamental to biodiversity conservation efforts, but this information is lacking for the majority of species on earth. Consequently, subsets of taxa are often used as proxies for biodiversity; but this assumes that different taxa display congruent distribution patterns. Here we use a global meta-analysis to show that studies of cross-taxon congruence rarely give consistent results. Instead, species richness congruence is highest at extreme spatial scales and close to the equator, while congruence in species composition is highest at large extents and grain sizes. Studies display highest variance in cross-taxon congruence when conducted in areas with dissimilar areal extents (for species richness) or latitudes (for species composition). These results undermine the assumption that a subset of taxa can be representative of biodiversity. Therefore, researchers whose goal is to prioritize locations or actions for conservation should use data from a range of taxa.

An empirical assessment and comparison of species-based and habitat-based surrogates: a case study of forest vertebrates and large old trees.

A holy grail of conservation is to find simple but reliable measures of environmental change to guide management. For example, particular species or particular habitat attributes are often used as proxies for the abundance or diversity of a subset of other taxa. However, the efficacy of such kinds of species-based surrogates and habitat-based surrogates is rarely assessed, nor are different kinds of surrogates compared in terms of their relative effectiveness. We use 30-year datasets on arboreal marsupials and vegetation structure to quantify the effectiveness of: (1) the abundance of a particular species of arboreal marsupial as a species-based surrogate for other arboreal marsupial taxa, (2) hollow-bearing tree abundance as a habitat-based surrogate for arboreal marsupial abundance, and (3) a combination of species- and habitat-based surrogates. We also quantify the robustness of species-based and habitat-based surrogates over time. We then use the same approach to model overall species richness of arboreal marsupials. We show that a species-based surrogate can appear to be a valid surrogate until a habitat-based surrogate is co-examined, after which the effectiveness of the former is lost. The addition of a species-based surrogate to a habitat-based surrogate made little difference in explaining arboreal marsupial abundance, but altered the co-occurrence relationship between species. Hence, there was limited value in simultaneously using a combination of kinds of surrogates. The habitat-based surrogate also generally performed significantly better and was easier and less costly to gather than the species-based surrogate. We found that over 30 years of study, the relationships which underpinned the habitat-based surrogate generally remained positive but variable over time. Our work highlights why it is important to compare the effectiveness of different broad classes of surrogates and identify situations when either species- or habitat-based surrogates are likely to be superior.

Meta-analysis of incidence of rare events.

This is a review of methods for the meta-analysis of incidence of rare events using summary-level data. It is motivated and illustrated by the dataset used in a published analysis of cardiovascular safety in rosiglitazone trials. This review compares available methods for binary data, considering risk-difference, relative-risk and odds-ratio scales, fixed-effect and random-effects models, and frequentist and Bayesian approaches. Particular issues in this dataset include low incidence rates, the occurrence of studies with no events under one or all treatments, and discrepancy among results achieved using different statistical methodologies. The common method of adding a correction factor to handle zeroes may introduce bias where the incidence of events is small, as in this case. Alternative analyses on the log-odds scale are shown to give similar results, but the choice between them is less important than the potential sources of bias in any meta-analysis arising from limitations in the underlying dataset. It is important to present results carefully, including numerical and graphical summaries on the natural scale of risk when the analysis is on a statistically appropriate scale such as log-odds: the incidence rates should accompany an estimated ratio (of odds or risk) to put the analysis into the proper context. Beyond the statistical methodologies which are the focus of this paper, this dataset highlights the importance of understanding the limitations of the data being combined. Because the rosiglitazone dataset contains clinically heterogeneous trials with low event rates that were not designed or intended to assess cardiovascular outcomes, the findings of any meta-analysis of such trials should be considered hypothesis-generating.

Methodological quality of meta-analyses: matched-pairs comparison over time and between industry-sponsored and academic-sponsored reports.

CONTEXT: Meta-analyses are regularly used to inform healthcare decisions. Concerns have been expressed about the quality of meta-analyses and, in particular, about those supported by the pharmaceutical industry. OBJECTIVE: The objective of this study is to compare the quality of pharmaceutical-industry-supported meta-analyses with academic meta-analyses and of meta-analyses published before and after companies started to disclose their data. DATA SOURCES: We identified industry-supported meta-analyses by searching the Scopus bibliographic database, using author affiliations. We matched each industry-supported meta-analysis with an academic meta-analysis using high-level MeSH terms in PubMed. STUDY SELECTION: We included meta-analyses of randomized trials assessing the efficacy or safety of any pharmaceutical intervention in humans, published in 2002-2004 or 2008-2009. Cochrane reviews were excluded. Two individuals independently selected papers, with discrepancies resolved by two further individuals. ASSESSMENT: We developed and piloted a quality-assessment tool, consisting of 43 questions in four domains, with a key summary question covering each domain. Two individuals independently assessed each meta-analysis. RESULTS: We examined 126 meta-analysis publications in 63 matched pairs. The average quality was low, with fewer than 50% adequate in three of the four domains. Industry-supported meta-analyses less often demonstrated adequate methods for locating studies and assessing their quality (odds ratio 0.44, 95% confidence interval 0.21 to 0.92), for analysing the included studies (0.52, 0.25 to 1.06), for undertaking meta-analyses (0.82, 0.40 to 1.68) and in reaching sound conclusions (0.62, 0.30 to 1.28). Quality generally improved over time, particularly for some aspects of industry reports. CONCLUSIONS: Academic meta-analysis papers are generally of higher quality than industry-supported ones. This is largely due to less detailed reporting in industry-supported meta-analyses and a tendency for them to take the included studies at face value, probably arising from the implicit assumption that these studies already have high methodological standards to meet licensing requirements. The improved quality over time does not appear to be due to the use of data disclosed by industry. The main limitations of this study are the small sample of papers and the subjective nature of some of the assessment processes.

A tool to assess the quality of a meta-analysis.

BACKGROUND: Because meta-analyses are increasingly prevalent and cited in the medical literature, it is important that tools are available to assess their methodological quality. When performing an empirical study of the quality of published meta-analyses, we found that existing tools did not place a strong emphasis on statistical and interpretational issues. METHODS: We developed a quality-assessment tool using existing materials and expert judgment as a starting point, followed by multiple iterations of input from our working group, piloting, and discussion. After having used the tool for our empirical study, agreement for four key items in the tool was measured using weighted kappa coefficients. RESULTS: Our tool contained 43 items divided into four key areas (data sources, analysis of individual studies, meta-analysis methods, and interpretation), and each area ended with a summary question. We also produced guidance for completing the tool. Agreement between raters was fair to moderate. CONCLUSIONS: The tool should usefully inform subsequent initiatives to develop quality-assessment tools for meta-analysis. We advocate use of consensus between independent raters when assessing statistical appropriateness and adequacy of interpretation in meta-analyses.

The efficacy and safety of retigabine and other adjunctive treatments for refractory partial epilepsy: a systematic review and indirect comparison.

INTRODUCTION: Retigabine (RTG) is now approved in Europe and the US for the adjunctive treatment of partial-onset seizures in adults with epilepsy. To support submissions to EU reimbursement authorities, we explored its efficacy and tolerability relative to selected antiepileptic drugs (AEDs). METHODS: A systematic review was conducted to identify placebo-controlled trials of RTG and selected AEDs approved for use in a similar position in the management pathway of partial epilepsy (eslicarbazepine acetate [ESL], lacosamide [LCM], pregabalin [PGB], tiagabine [TGB] and zonisamide [ZNS]). Using conventional and network meta-analyses as appropriate, we report efficacy and tolerability outcomes for each AED versus placebo and the performance of RTG relative to other AEDs. RESULTS: Twenty studies met the inclusion criteria: three each for RTG, ESL, LCM, TGB and ZNS; five for PGB. Comparisons comprised 1-5 studies per AED. In the network meta-analysis, RTG was not found to be different from the other AEDs for responder rate (maintenance period), seizure freedom (maintenance period and double-blind period), withdrawals due to adverse events, and incidences of ataxia, dizziness, fatigue and nausea. Differences between RTG and other AEDs were found for a few comparisons, which did not reveal any trends: RTG was associated with a lower responder rate than PGB during the double-blind period, higher withdrawal rate due to any reason than ESL and a higher incidence of somnolence than TGB. CONCLUSIONS: Findings suggest that the risk/benefit for RTG is similar to that for comparator AEDs. However, results should be interpreted in the context of the limitations of the analyses.

Statistical approaches for conducting network meta-analysis in drug development.

We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS® software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity.

Graphical approaches to the analysis of safety data from clinical trials.

Patient safety has always been a primary focus in the development of new pharmaceutical products. The predominant method for statistical evaluation and interpretation of safety data collected in a clinical trial is the tabular display of descriptive statistics. There is a great opportunity to enhance evaluation of drug safety through the use of graphical displays, which can convey multiple pieces of information concisely and more effectively than can tables. Graphs can be used in an exploratory setting to help identify emerging safety signals, or in a confirmatory setting as a tool to elucidate known safety issues. We developed several graphical displays for routine safety data collected during a clinical trial, covering a broad range of graphical techniques, and illustrate here 10 specific graphical designs, many of which display the data along with statistics derived from them. Two are simple plots, comparing distributions in the form of boxplots or cumulative plots, and four more display data and summaries over time, comparing information from two groups in terms of distribution (with boxplots), cumulative incidence, hazard, or simply means with error bars. The other four are multi-panel displays: one-dimensional and two-dimensional arrays of scatterplots, a trellis of individual profiles, and a paired dotplot displaying risk together with relative risk. The displays focus on key safety endpoints in clinical trials including the QT interval from electrocardiograms, laboratory measurements for detecting hepatotoxicity, and adverse events of special interest. We discuss in detail the statistical and graphical principles underlying the production and interpretation of the displays.

Analysis of exacerbation rates in asthma and chronic obstructive pulmonary disease: example from the TRISTAN study.

Recurrent events in clinical trials have typically been analysed using either a multiple time-to-event method or a direct approach based on the distribution of the number of events. An area of application for these methods is exacerbation data from respiratory clinical trials. The different approaches to the analysis and the issues involved are illustrated for a large trial (n = 1465) in chronic obstructive pulmonary disease (COPD). For exacerbation rates, clinical interest centres on a direct comparison of rates for each treatment which favours the distribution-based analysis, rather than a time-to-event approach. Poisson regression has often been employed and has recently been recommended as the appropriate method of analysis for COPD exacerbations but the key assumptions often appear unreasonable for this analysis. By contrast use of a negative binomial model which corresponds to assuming a separate Poisson parameter for each subject offers a more appealing approach. Non-parametric methods avoid some of the assumptions required by these models, but do not provide appropriate estimates of treatment effects because of the discrete and bounded nature of the data.