Prediction intervals for all of M future observations based on linear random effects models

In many pharmaceutical and biomedical applications such as assay validation, assessment of historical control data, or the detection of anti-drug antibodies, the calculation and interpretation of prediction intervals (PI) is of interest. The present study provides two novel methods for the calculation of prediction intervals based on linear random effects models and restricted maximum likelihood (REML) estimation. Unlike other REML-based PI found in the literature, both intervals reflect the uncertainty related with the estimation of the prediction variance. The first PI is based on Satterthwaite approximation. For the other PI, a bootstrap calibration approach that we will call quantile-calibration was used. Due to the calibration process this PI can be easily computed for more than one future observation and based on balanced and unbalanced data as well. In order to compare the coverage probabilities of the proposed PI with those of four intervals found in the literature, Monte Carlo simulations were run for two relatively complex random effects models and a broad range of parameter settings. The quantile-calibrated PI was implemented in the statistical software R and is available in the predint package

Potential of small-scale and structurally diverse short-rotation coppice as habitat for large and medium-sized mammals

We surveyed occurrence and activity of large and medium-sized mammals on three experimental short-rotation coppice (SRC) and three afforestations by camera trapping. Both habitat types were surveyed simultaneously in spring. Additional wintertime surveys were performed on the SRC to consider seasonal aspects of habitat utilisation. In spring, SRC and afforestations were predominantly used by the same species. European hare (Lepus europaeus) and roe deer (Capreolus capreolus) were the most active species across all sites. Additionally, the European rabbit (Oryctolagus cuniculus) showed intense activity on one SRC site. Activity of carnivorous and omnivorous species was comparatively low in both habitat types, but even lower on the SRC. The only forest-associated species (European badger Meles meles), detected on all afforestations, was absent from the SRC. In winter, the surveyed SRC were used by the same species as in spring. Most species showed similar activity on the SRC in both seasons. We conclude that small-scale and structurally diverse SRC provide suitable habitat, in different seasons, especially for herbivorous mammals associated with farmland and forest-ecotones rather than forest species. The extent to which our results can be generalised to large-scale commercial SRC is unclear. However, the results indicate that SRC can be managed in a manner compatible with wildlife and may then have a habitat function for mammals comparable to that of young afforestations. Creation of within-plantation heterogeneity can be a suitable measure to improve habitat quality and should, therefore, be considered in the design and management of SRC. © 2021, The Author(s)

Improving Risk Assessment in Clinical Trials: Toward a Systematic Risk-Based Monitoring Approach

Regulatory authorities have encouraged the usage of a monitoring (RBM) system in clinical trials before trial initiation for detection of potential risks and inclusion of a mitigation plan in the monitoring strategy. Several RBM tools were developed after the International Council for Harmonization gave sponsors the flexibility to initiate an approach to enhance quality management in a clinical trial. However, various studies have demonstrated the need for improvement of the available RBM tools as each does not provide a comprehensive overview of the characteristics, focus, and application. This research lays out a rationale for a risk methodology assessment (RMA) within the RBM system. The core purpose of RMA is to deliver a scientifically based evaluation and decision of any potential risk in a clinical trial. Thereby, a monitoring plan can be developed to elude prior identified risk outcome. To demonstrate RMA's theoretical approach in practice, a Shiny web application (R Foundation for Statistical Computing) was designed to describe the assessment process of risk analysis and visualization tools that eventually aid in focusing monitoring activities. RMA focuses on the identification of an individual risk and visualizes its weight on the trial. The scoring algorithm of the presented approach computes the assessment of the individual risk in a radar plot and computes the overall score of the trial. Moreover, RMA's novelty lies in its ability to decrease biased decision making during risk assessment by categorizing risk influence and detectability; a characteristic pivotal to serve RBM in assessing risks, and in contributing to a better understanding in the monitoring technique necessary for developing a functional monitoring plan. Future research should focus on validating the power of RMAs to demonstrate its efficiency. This would facilitate the process of characterizing the strengths and weaknesses of RMA in practice. © 2021 The Author(s

Marginal and simultaneous confidence intervals for abundance data with applications to safety assessment of non-target species

[no abstract

The Tukey trend test: Multiplicity adjustment using multiple marginal models

In dose–response analysis, it is a challenge to choose appropriate linear or curvilinear shapes when considering multiple, differently scaled endpoints. It has been proposed to fit several marginal regression models that try sets of different transformations of the dose levels as explanatory variables for each endpoint. However, the multiple testing problem underlying this approach, involving correlated parameter estimates for the dose effect between and within endpoints, could only be adjusted heuristically. An asymptotic correction for multiple testing can be derived from the score functions of the marginal regression models. Based on a multivariate t-distribution, the correction provides a one-step adjustment of p-values that accounts for the correlation between estimates from different marginal models. The advantages of the proposed methodology are demonstrated through three example datasets, involving generalized linear models with differently scaled endpoints, differing covariates, and a mixed effect model and through simulation results. The methodology is implemented in an R package. © 2021 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society

Asymptotic Simultaneous Estimations for Contrasts of Quantiles

Although the expected value is popular, many researches in the health and social sciences involve skewed distributions and inferences concerning quantiles. Most standard multiple comparison procedures require the normality assumption. For example, few methods exist for comparing the medians of independent samples or quantiles of several distributions in general. To our knowledge, there is no general-purpose method for constructing simultaneous confidence intervals for multiple contrasts of quantiles. In this paper, we develop an asymptotic method for constructing such intervals and extend the idea to that of time-to-event data in survival analysis. Small-sample performance of the proposed method is assessed in terms of coverage probability and average width of the simultaneous confidence intervals. Good coverage probabilities are observed for most of the distributions considered in the simulations. The proposed method is applied to biomedical data and time-to-event data in survival analysis

Common pitfalls when testing additivity of treatment mixtures with chi-square analyses

Studying interactions of multiple pesticides applied simultaneously in a mixture is a common task in phytopathology. Statistical methods are employed to test whether the treatment components influence each other's efficacy in a promotive or inhibitory way (synergistic or antagonistic interaction) or rather act independent of one another (additivity). The trouble is that widely used procedures based on chi-square tests are often seriously flawed, either because people apply them in a preposterous way or because the method simply does not fit the problem at hand. Browsing recent volumes of entomological journals, we found that numerous researchers have (in all likelihood unwittingly) analysed their data as if they had had a sample size of 100 or, equally bad, a sample size of one! We show how to avoid such poor practices and further argue that chi-square testing is, even if applied correctly (meaning that no technical errors are made), a limited purpose tool for assessing treatment interactions