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    An improved Bayesian pick-the-winner (IBPW) design for randomized phase II clinical trials

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    Phase II clinical trials play a pivotal role in drug development by screening a large number of drug candidates to identify those with promising preliminary efficacy for phase III testing. Trial designs that enable efficient decision-making with small sample sizes and early futility stopping while controlling for type I and II errors in hypothesis testing, such as Simonโ€™s two-stage design, are preferred. Randomized multi-arm trials are increasingly used in phase II settings to overcome the limitations associated with using historical controls as the reference. However, how to effectively balance efficiency and accurate decision-making continues to be an important research topic. A notable development in phase II randomized design methodology is the Bayesian pick-the-winner (BPW) design that extends a Simonโ€™s two-stage based multi-arm design with a Bayesian winner-selection strategy. Despite multiple appealing features, this method cannot easily control for overall type I and II errors for winner selection. Here, we introduce an improved randomized two-stage Bayesian pick-the-winner (IBPW) design that formalizes the winner-selection based hypothesis testing, optimizes sample sizes and decision cut-offs by strictly controlling the type I and type II errors under a set of flexible hypotheses for winner-selection across two treatment arms. Simulation studies demonstrate that our new design offers improved operating characteristics for winner selection while retaining the desirable features of the BPW design

    The InBIO barcoding initiative database: DNA barcodes of Iberian Trichoptera, documenting biodiversity for freshwater biomonitoring in a Mediterranean hotspot

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    Background The Trichoptera are an important component of freshwater ecosystems. In the Iberian Peninsula, 380 taxa of caddis๏ฌ‚ies are known, with nearly 1/3 of the total species being endemic in the region. A reference collection of morphologically identified Trichoptera specimens, representing 142 Iberian taxa, was constructed. The InBIO Barcoding Initiative (IBI) Trichoptera 01 dataset contains records of 438 sequenced specimens. The species of this dataset correspond to about 37% of Iberian Trichoptera species diversity. Specimens were collected between 1975 and 2018 and are deposited in the IBI collection at the CIBIO (Research Center in Biodiversity and Genetic Resources, Portugal) or in the collection Marcos A. Gonzรกlez at the University of Santiago de Compostela (Spain). New information Twenty-nine species, from nine different families, were new additions to the Barcode of Life Data System (BOLD). A success identification rate of over 80% was achieved when comparing morphological identifications and DNA barcodes for the species analysed. This encouraging step advances incorporation of informed Environmental DNA tools in biomonitoring schemes, given the shortcomings of morphological identifications of larvae and adult Caddis๏ฌ‚ies in such studies. DNA barcoding was not successful in identifying species in six Trichoptera genera: Hydropsyche (Hydropsychidae), Athripsodes (Leptoceridae), Wormaldia (Philopotamidae), Polycentropus (Polycentropodidae) Rhyacophila (Rhyacophilidae) and Sericostoma (Sericostomatidae). The high levels of intraspecific genetic variability found, combined with a lack of a barcode gap and a challenging morphological identification, rendered these species as needing additional studies to resolve their taxonomy

    \u3cem\u3eMicromeria tenensis\u3c/em\u3e (Lamiaceae), a new species from Tenerife, Canary Islands

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    Based on molecular and morphological evidence, a new species of Micromeria is described for Tenerife, Canary Islands. Micromeria tenensis is endemic to the massif of Teno, occurring between 100โ€“1000 m elevation. It is characterized by having more or less pendulous branches, tomentose stems and leaves, hispid calyx, and calyx apices triangular to lanceolate, densely white hispid

    \u3cem\u3eCalceolaria nivalis\u3c/em\u3e subsp. \u3cem\u3elanatifolia\u3c/em\u3e, a new subspecies of Calceolariaceae from Northern Peru

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    A new subspecies of Calceolaria nivalis, C. nivalis subsp. lanatifolia is described and illustrated. The new subspecies was collected in Uchumarca district, department of La Libertad, Peru, between 2300โ€“3850 m elevation. Calceolaria nivalis subsp. lanatifolia is characterized by the lanate indumentum in the lower surface of the leaves, villous petioles, peduncles, and pedicels, and sepals with a strigose margin internally

    On assessing survival benefit of immunotherapy using long-term restricted mean survival time

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    The pattern of the difference between two survival curves we often observe in randomized clinical trials for evaluating immunotherapy is not proportional hazards; the treatment effect typically appears several months after the initiation of the treatment (i.e., delayed difference pattern). The commonly used logrank test and hazard ratio estimation approach will be suboptimal concerning testing and estimation for those trials. The long-term restricted mean survival time (LT-RMST) approach is a promising alternative for detecting the treatment effect that potentially appears later in the study. A challenge in employing the LT-RMST approach is that it must specify a lower end of the time window in addition to a truncation time point that the RMST requires. There are several investigations and suggestions regarding the choice of the truncation time point for the RMST. However, little has been investigated to address the choice of the lower end of the time window. In this paper, we propose a flexible LT-RMST-based test/estimation approach that does not require users to specify a lower end of the time window. Numerical studies demonstrated that the potential power loss by adopting this flexibility was minimal, compared to the standard LT-RMST approach using a prespecified lower end of the time window. The proposed method is flexible and can offer higher power than the RMST-based approach when the delayed treatment effect is expected. Also, it provides a robust estimate of the magnitude of the treatment effect and its confidence interval that corresponds to the test result

    Nonlinear Mixed-Effects Models for HIV Viral Load Trajectories Before and After Antiretroviral Therapy Interruption, Incorporating Left Censoring

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    Characterizing features of the viral rebound trajectories and identifying host, virological, and immunological factors that are predictive of the viral rebound trajectories are central to HIV cure research. In this paper, we investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. Nonlinear mixed effect (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement threestep method. We evaluate the performance of this method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point)

    A simple and robust alternative to Bland-Altman method of assessing clinical agreement

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    Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. These limits only describe the dispersion of disagreements in 95% cases and do not measure the degree of agreement. The interpretation regarding the presence or absence of agreement by this method is based on whether B-A limits are within the pre-specified externally determined clinical tolerance limits. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that the direct use of clinical tolerance limits for assessing agreement without the B-A limits is more effective and has tremendous merits. This nonparametric approach is simple, is robust to the distribution pattern and outliers, has more flexibility, and exactly measures the degree of clinical agreement. This is explained with the help of two examples, including setups where clinical tolerance limits can be set up to follow varying trends if required in the clinical context โ€“ a feature not available in the B-A method

    Marginal Proportional Hazards Models for Clustered Interval-Censored Data with Time-Dependent Covariates

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    The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswanaโ€™s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test-and-treat strategy now modeled as a time-dependent covariate

    West Virginia Herbaria: Status, Updates, and Plans

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    The West Virginia (U.S.A.) Herbarium Curators met on December 1st, 2021, with the intent of sharing updates on the collections, fostering collaboration, learning from each other\u27s experiences, and identifying priorities for the collections towards the future. This article presents a summary of the herbaria that were represented in this meeting

    Causal Mediation Analysis for Difference-in-Difference Design and Panel Data

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    Advantages of panel data, i.e., difference in difference (DID) design data, are a large sample size and easy availability. Therefore, panel data are widely used in epidemiology and in all social science fields. The literatures on causal inferences of panel data setting or DID design are growing, but no theory or mediation analysis method has been proposed for such settings. In this study, we propose a methodology for conducting causal mediation analysis in DID design and panel data setting. We provide formal counterfactual definitions for controlled direct effect and natural direct and indirect effect in panel data setting and DID design, including the identification and required assumptions. We also demonstrate that, under the assumptions of linearity and additivity, controlled direct effects can be estimated by contrasting marginal and conditional DID estimators whereas natural indirect effects can be estimated by calculating the product of the exposure-mediator DID estimator and the mediator-outcome DID estimator. A panel regression-based approach is also proposed. The proposed method is then used to investigate mechanisms of the effects of the Covid 19 pandemic on the mental health status of the population. The results revealed that mobility restrictions mediated approximately 45 % of the causal effect of Covid 19 on mental health status


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