Spatial covariation of fish population vital rates in a stream network

Animal populations are spatially structured in heterogeneous landscapes, in which local patches with differing vital rates are connected by dispersal of individuals to varying degrees. Although there is evidence that vital rates differ among local populations, much less is understood about how vital rates covary among local patches in spatially heterogeneous landscapes. In this study, we conducted a nine-year annual mark–recapture survey to characterize spatial covariation of survival and growth for two Japanese native salmonids, white-spotted charr Salvelinus leucomaenis japonicus and red-spotted masu salmon Oncorhynchus masou ishikawae, in a headwater stream network composed of distinctly different tributary and mainstem habitats. Spatial structure of survival and growth differed by species and age class, but results provided support for negative covariation between vital rates, where survival was higher in the tributary habitat but growth was higher in the mainstem habitat. Thus, neither habitat was apparently more important than the other, and local habitats with complementary vital rates may make this spatially structured population less vulnerable to environmental change (i.e. portfolio effect). Despite the spatial structure of vital rates and possibilities that fish can exploit spatially distributed resources, movement of fish was limited due partly to a series of low-head dams that prevented upstream movement of fish in the study area. This study shows that spatial structure of vital rates can be complex and depend on species and age class, and this knowledge is likely paramount to elucidating dynamics of spatially structured populations.     

Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint

Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.     

Bayesian design of biosimilars clinical programs involving multiple therapeutic indications

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

PA-CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials

Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting patient accrual and slowing down the trial. We developed a pediatric-continuous reassessment method (PA-CRM) to streamline this process, providing a more efficient and rigorous method to find the maximum tolerated dose for pediatric phase I oncology trials. We use a discounted joint likelihood of the adult and pediatric data, with a discount parameter controlling information borrowing between pediatric and adult trials. According to the interim adult and pediatric data, the discount parameter is adaptively updated using the Bayesian model averaging method. Numerical study shows that the PA-CRM improves the efficiency and accuracy of the pediatric trial and is robust to various model assumptions.     

A Bayesian extension of phylogenetic generalized least squares: Incorporating uncertainty in the comparative study of trait relationships and evolutionary rates

Phylogenetic comparative methods use tree topology, branch lengths, and models of phenotypic change to take into account nonindependence in statistical analysis. However, these methods normally assume that trees and models are known without error. Approaches relying on evolutionary regimes also assume specific distributions of character states across a tree, which often result from ancestral state reconstructions that are subject to uncertainty. Several methods have been proposed to deal with some of these sources of uncertainty, but approaches accounting for all of them are less common. Here, we show how Bayesian statistics facilitates this task while relaxing the homogeneous rate assumption of the well-known phylogenetic generalized least squares (PGLS) framework. This Bayesian formulation allows uncertainty about phylogeny, evolutionary regimes, or other statistical parameters to be taken into account for studies as simple as testing for coevolution in two traits or as complex as testing whether bursts of phenotypic change are associated with evolutionary shifts in intertrait correlations. A mixture of validation approaches indicates that the approach has good inferential properties and predictive performance. We provide suggestions for implementation and show its usefulness by exploring the coevolution of ankle posture and forefoot proportions in Carnivora.     

Environment predicts repeated body size shifts in a recent radiation of Australian mammals*

Closely related species that occur across steep environmental gradients often display clear body size differences, and examining this pattern is crucial to understanding how environmental variation shapes diversity. Australian endemic rodents in the Pseudomys Division (Muridae: Murinae) have repeatedly colonized the arid, monsoon, and mesic biomes over the last 5 million years. Using occurrence records, body mass data, and Bayesian phylogenetic models, we test whether body mass of 31 species in the Pseudomys Division can be predicted by their biome association. We also model the effect of eight environmental variables on body mass. Despite high phylogenetic signal in body mass evolution across the phylogeny, we find that mass predictably increases in the mesic biome and decreases in arid and monsoon biomes. As per Bergmann's rule, temperature is strongly correlated with body mass, as well as several other variables. Our results highlight two important findings. First, body size in Australian rodents has tracked with climate through the Pleistocene, likely due to several environmental variables rather than a single factor. Second, support for both Brownian motion and predictable change at different taxonomic levels in the Pseudomys Division phylogeny demonstrates how the level at which we test hypotheses can alter interpretation of evolutionary processes.     

Growth and derived life-history characteristics of the Brazilian electric ray Narcine brasiliensis

The majority of batoids are listed as Threatened (20.4%) or Data Deficient (41%) by the IUCN Red List. A key challenge to assessing Data-Deficient species is obtaining estimates of key life-history characteristics. Here, a Bayesian approach was used to estimate derived life-history characteristics from a growth model applied to the Data-Deficient Brazilian electric ray Narcine brasiliensis. The age of 170 specimens (107 females, 63 males) was estimated from vertebral centra, and total length, disc width, total weight and birth size were used in a joint estimation of sex-specific length-weight models and two-dimensional von Bertalanffy growth models. Estimates of age at length zero, age at maturity, longevity and mortality at age were derived simultaneously. The Bayesian joint modelling approach was robust to small sample sizes by adding a likelihood to constrain L₀ and sharing parameters, such as Brody growth coefficient between length measurements. The median growth parameter estimates were a shared L₀ = 38.8 mm, female L_∞ = 515 mm, 𝑘 = 0.125 and male L_∞ = 387 mm, 𝑘 = 0.194. Age at maturity was estimated to be 7.40–7.49 years for females and 4.45–4.47 years for males, whereas longevity was 22.5–22.6 years for females and 14.2 years for males depending on length measurement. Age-1 natural mortality was estimated to be 0.199–0.207 for females and 0.211–0.213 for males. The derived life-history characteristics indicate N. brasiliensis is earlier maturing, but slower growing relative to other Torpediniformes. These characteristics along with the species’ endemism to southern Brazil and high by-catch rates indicate that one of the IUCN Red List threatened categories may be more appropriate for the currently Data-Deficient status. The Bayesian approach used for N. brasiliensis can prove useful for utilizing limited age-growth data in other Data-Deficient batoid species to inform necessary life characteristics for conservation and management.     

A new genus of Entiminae from North Africa,supported by a phylogenetic analysis (Coleoptera: Curculionidae: Entiminae)

The genus Holcophloeus gen. nov. is here proposed to include Trachyphloeus cruciatus Seidlitz, 1868, and two new species native to North Africa, based on a phylogenetic analysis and an evaluation of the diagnostic characters. The taxonomic position of Holcophloeus in relation to the tribes Trachyphloeini Lacordaire, 1863, and Holcorhinini Desbrochers, 1898, is discussed, and the new genus is attributed to the Holcorhinini. Holcophloeus laurae sp. nov. from south‐eastern Morocco and Holcophloeus weilli sp. nov. from northern Libya are described and illustrated and a key to the species of the new genus is given. The lectotype of Trachyphloeus cruciatus Seidlitz, 1868, is designated. The genus Massimiellus Borovec, 2009, is transferred from Trachyphloeini to Holcorhinini. © 2013 The Linnean Society of London