Corrected estimator of sensitive population proportion using unknown repeated trials in the unrelated question randomized response model

In this paper, we have pointed out a major mistake in the research paper of Singh and Mathur [(2004). Unknown repeated trials in the unrelated question randomized response model, Biometrical Journal, 46:375–378]. We have corrected this mistake and proposed the corresponding corrected estimator of sensitive population proportion. Furthermore, we have obtained the variance of our proposed estimator. Likewise, Singh and Mathur, we have also compared the variance of our proposed estimator with that of the Greenberg et al.’s estimator theoretically as well as numerically.     

Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Robust approach to combining multiple markers to improve surrogacy

Identifying effective and valid surrogate markers to make inference about a treatment effect on long-term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long-term outcome with short-term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study.     

Exploiting plants for glutathione (GSH) production: Uncoupling GSH synthesis from cellular controls results in unprecedented GSH accumulation

Glutathione (GSH) is a key factor for cellular redox homeostasis and tolerance against abiotic and biotic stress ( May et al., 1998 ; Noctor et al., 1998a ). Previous attempts to increase GSH content in plants have met with moderate success ( Rennenberg et al., 2007 ), largely because of tight and multilevel control of its biosynthesis ( Rausch et al., 2007 ). Here, we report the in planta expression of the bifunctional γ‐glutamylcysteine ligase—glutathione synthetase enzyme from Streptococcus thermophilus (StGCL‐GS), which is shown to be neither redox‐regulated nor sensitive to feedback inhibition by GSH. Transgenic tobacco plants expressing StGCL‐GS under control of a constitutive promoter reveal an extreme accumulation of GSH in their leaves (up to 12 μmol GSH/gFW, depending on the developmental stage), which is more than 20‐ to 30‐fold above the levels observed in wild‐type (wt) plants and which can be even further increased by additional sulphate fertilization. Surprisingly, this dramatically increased GSH production has no impact on plant growth while enhancing plant tolerance to abiotic stress. Furthermore, StGCL‐GS‐expressing plants are a novel, cost‐saving source for GSH production, being competitive with current yeast‐based systems ( Li et al., 2004 ).     

Robustness of ANCOVA in randomized trials with unequal randomization

Randomized trials with continuous outcomes are often analyzed using analysis of covariance (ANCOVA), with adjustment for prognostic baseline covariates. The ANCOVA estimator of the treatment effect is consistent under arbitrary model misspecification. In an article recently published in the journal, Wang et al proved the model-based variance estimator for the treatment effect is also consistent under outcome model misspecification, assuming the probability of randomization to each treatment is 1/2. In this reader reaction, we derive explicit expressions which show that when randomization is unequal, the model-based variance estimator can be biased upwards or downwards. In contrast, robust sandwich variance estimators can provide asymptotically valid inferences under arbitrary misspecification, even when randomization probabilities are not equal.     

Assessing community and ecosystem sensitivity to climate change – toward a more comparative approach

Plant communities can vary widely in their sensitivity to changing precipitation regimes, as reported by Byrne et al., Mulhouse et al. and Sternberg et al. in this issue of Journal of Vegetation Science. But to understand why communities differ in their sensitivity, we argue that clearly defined metrics of sensitivity and coordinated research approaches are needed to elucidate mechanisms.     

Deciphering the biochemistry and identifying biomarkers to multiple sclerosis

Multiple sclerosis is an idiopathic demyelinating disease of the CNS. Despite being extensively studied during the last decades, many molecular aspects of the disease are still to be elucidated. Moreover, biomarkers for treatment and early diagnosis are major issues to be tackled. In this edition of Kroksveen et al. (Proteomics 2015, 15, 3361–3369) present biomarker candidates for the early detection of multiple sclerosis. Despite the need for validation in larger sets of samples, this dataset contributes to resolve open questions associated to multiple sclerosis.     

Improving Bayesian isotope mixing models: a response to Jackson et al. (2009)

We recently described a Bayesian framework for stable isotope mixing models and provided a software tool, MixSIR, for conducting such analyses (Ecol. Lett., 2008; 11 :470). Jackson et al. (Ecol. Lett., 2009; 12:E1) criticized the performance of our software based on tests using simulated data. However, their simulation data were flawed, rendering claims of erroneous behaviour inaccurate. A re‐evaluation of the MixSIR source code did, however, uncover two minor coding errors, which we have fixed. When data are correctly simulated according to eqns  (1)–(4) in Jackson et al. (2009) , MixSIR consistently and accurately estimated the proportional contribution of prey to a predator diet, and was surprisingly robust to additional unquantified error. Jackson et al. (2009) also suggested we use a Dirichlet prior on the source proportion parameters, which we agree with. Finally, Jackson et al. (2009) propose adding additional error parameters to our mixing model framework. We caution that such increases in model complexity should be evaluated based on data support.     

Distance from forest edge affects bee pollinators in oilseed rape fields

Wild pollinators have been shown to enhance the pollination of Brassica napus (oilseed rape) and thus increase its market value. Several studies have previously shown that pollination services are greater in crops adjoining forest patches or other seminatural habitats than in crops completely surrounded by other crops. In this study, we investigated the specific importance of forest edges in providing potential pollinators in B. napus fields in two areas in France. Bees were caught with yellow pan traps at increasing distances from both warm and cold forest edges into B. napus fields during the blooming period. A total of 4594 individual bees, representing six families and 83 taxa, were collected. We found that both bee abundance and taxa richness were negatively affected by the distance from forest edge. However, responses varied between bee groups and edge orientations. The ITD (Inter‐Tegular distance) of the species, a good proxy for bee foraging range, seems to limit how far the bees can travel from the forest edge. We found a greater abundance of cuckoo bees (Nomada spp.) of Andrena spp. and Andrena spp. males at forest edges, which we assume indicate suitable nesting sites, or at least mating sites, for some abundant Andrena species and their parasites (Fig.  1 ). Synthesis and Applications. This study provides one of the first examples in temperate ecosystems of how forest edges may actually act as a reservoir of potential pollinators and directly benefit agricultural crops by providing nesting or mating sites for important early spring pollinators. Policy‐makers and land managers should take forest edges into account and encourage their protection in the agricultural matrix to promote wild bees and their pollination services.

Figure 1 Open in figure viewer PowerPoint Left, a Nomada sp male; right, an Andrena sp male. Caption Left, a Nomada sp male; right, an Andrena sp male.

Introduction

Pollinators play an important functional role in most terrestrial ecosystems and provide a key ecosystem service (Ashman et al. 2004 ). Insects, particularly bees, are the primary pollinators for the majority of the world's angiosperms (Ollerton et al. 2012 ). Without this service, many interconnected species and processes functioning within both wild and agricultural ecosystems could collapse (Kearns et al. 1998 ). Brassica napus (oilseed rape, OSR) represents the most widespread entomophilous crop in France with almost 1.5 Mha in 2010 (FAOSTAT August 10th, 2012). Results differ between varieties, but even though it seems that OSR produces 70% of its fruits through self‐pollination (Downey et al. 1970 in Mesquida and Renard 1981 ), native bees are also known to contribute to its pollination (Morandin and Winston 2005 ; Jauker et al. 2012 ). Bee pollination leads to improved yields (Steffan‐Dewenter 2003b ; Sabbahi et al. 2005 ) and to a shorter blooming period (Sabbahi et al. 2006 ), thus increasing the crop's market value (Bommarco et al. 2012 ). The most widely used species in crop pollination is the honeybee (Apis mellifera L) which is sometimes assumed to be sufficient for worldwide crop pollination (Aebi and Neumann 2011 ). However, this assertion has been questioned by different authors (Ollerton et al. 2012 ), and several studies show that many wild bees are also efficient pollinators of crops (Klein et al. 2007 ; Winfree et al. 2008 ; Breeze et al. 2011 ). Recently, Garibaldi et al. ( 2013 ) found positive associations of fruit set with wild‐insect visits to flowers in 41 crop systems worldwide. They demonstrate that honeybees do not maximize pollination, nor can they fully replace the contributions of diverse, wild‐insect assemblages to fruit set for a broad range of crops and agricultural practices on all continents with farmland. Unfortunately, not only are honey bees declining due to a variety of different causes (vanEngelsdorp et al. 2009 ), wild bee populations are also dwindling (Potts et al. 2010 ). Their decline has been documented in two Western European countries (Britain and the Netherlands) by comparing data obtained before and after 1980 (Biesmeijer et al. 2006 ). These losses have mostly been attributed to the use of agrochemicals, the increase in monocultures, the loss of seminatural habitat and deforestation (Steffan‐Dewenter et al. 2002 ; Steffan‐Dewenter and Westphal 2008 ; Brittain and Potts 2011 ). Several studies have shown the importance of natural or seminatural habitats in sustaining pollinator populations or pollination services close to fruit crops (Steffan‐Dewenter 2003a ; Kremen et al. 2004 ; Greenleaf and Kremen 2006a ; Carvalheiro et al. 2010 ). Morandin and Winston ( 2006 ) presented a cost–benefit model that estimates profit in OSR agroecosystems with different proportions of uncultivated land. They calculated that yield and profit could be maximized with 30% of the land left uncultivated within 750 m of field edges. Other studies have demonstrated a negative impact of the distance from forests on pollination services or bee abundance and richness both in tropical ecosystems (De Marco and Coelho 2004 ; Blanche et al. 2006 ; Chacoff and Aizen 2006 ) and in temperate ecosystems (Hawkins 1965 ; Taki et al. 2007 ; Arthur et al. 2010 ; Watson et al. 2011 ). These studies all suggest that natural or seminatural habitats are important sources of pollinators, probably because they provide “partial habitats” (Westrich 1996 ) such as complementary mating, foraging, nesting, and nesting materials sites that bees need to complete their life cycle. In this study, we focused on the effect of distance to forest edge on bee assemblages in OSR ecosystems. Forest edges could provide one or more important partial habitats for different bee species in agricultural landscapes, in particular when associated with a mass‐flowering crop such as OSR (Le Feon et al. 2011 ). For example, the availability of untilled soil and dead branches might provide ground‐nesting and cavity‐nesting bee species with numerous nesting sites. Moreover, during spring at least, the understory and the forest edge can provide cover containing flowering plants and wild trees such as Prunus spp, Castanea sativa, or Salix spp and thereby allow bees to find alternative floral resources. During spring 2010 and 2011, in two areas in France, we examined wild bee abundance and taxa richness both along forest edges and inside OSR fields at different distances from the forest. Like other taxa, bees respond to environmental variables according to their biologic traits that determine access and requirements for nesting, mating, and forage resources, species mobility or physiological tolerance. Specifically, we hypothesized that (1) bee abundance, species richness, and composition of bee communities within the crop field are dependent on the distance from the forest edge (where complementary floral resources, nesting sites, shelters, etc. can be found) and on the orientation of the forest edge; (2) the identity of bees in the crop is related to their foraging range which we measured with the ITD (Inter‐Tegular distance); (3) the forest edge may be the nesting or mating sites for cavity‐nesting or ground‐nesting bees such as Osmia spp or Andrena spp which are important groups of potential early spring pollinators for OSR.     

Response to Delibes-Mateos et al. : Pellet size matters

In Rueda et al. [Rueda, M., Rebollo, S., Gálvez-Bravo, L., 2008. Age and season determine European rabbit habitat use in Mediterranean ecosystems. Acta Oecol. 34, 266–273] we used a threshold of 6 mm faecal pellet diameter to differentiate between adult and juvenile European rabbit (Oryctolagus cuniculus) habitat use. Delibes-Mateos et al. designed a housing experiment with 12 adult rabbits and criticised the choice of 6 mm as a threshold to separate adult and juvenile rabbit pellets, claiming that adults can produce pellets both larger and smaller than 6 mm in similar proportions. In response to their criticism we argue the following. The selection of a 6 mm threshold has a bibliographic basis, it is not a new method developed by Rueda et al. and produces consistent results when applied in the field. Assuming that Delibes-Mateos et al. results are accurate, we should have found a greater number of <6 mm pellets than >6 mm, overall and seasonally, which is not the case. We believe that the use of commercial pelleted food, keeping animals isolated in small cages for over a year, and the use of adult rabbits only, makes the experimental design used by these authors not suitable to refute the usefulness of separating rabbit pellets smaller and larger than 6 mm diameter as indicators of changes in the relative abundance of juvenile and adult rabbits in the field. Finally, we agree with the authors that the use of indirect methods of animal aging would require case-specific validation studies; however, we believe these studies should be correctly designed.     

Review and comparison of ROC curve estimators for a time‐dependent outcome with marker‐dependent censoring

To quantify the ability of a marker to predict the onset of a clinical outcome in the future, time‐dependent estimators of sensitivity, specificity, and ROC curve have been proposed accounting for censoring of the outcome. In this paper, we review these estimators, recall their assumptions about the censoring mechanism and highlight their relationships and properties. A simulation study shows that marker‐dependent censoring can lead to important biases for the ROC estimators not adapted to this case. A slight modification of the inverse probability of censoring weighting estimators proposed by Uno et al. (2007) and Hung and Chiang (2010a) performs as well as the nearest neighbor estimator of Heagerty et al. (2000) in the simulation study and has interesting practical properties. Finally, the estimators were used to evaluate abilities of a marker combining age and a cognitive test to predict dementia in the elderly. Data were obtained from the French PAQUID cohort. The censoring appears clearly marker‐dependent leading to appreciable differences between ROC curves estimated with the different methods.     

Estimation of the odds ratio in a proportional odds model with censored time-lagged outcome in a randomized clinical trial

In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow-up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the OR in a proportional odds model with censored, time-lagged categorical outcome that incorporates additional baseline and time-dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate-adjusted estimator for the OR based on the full data that would be available at a final analysis.     

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656–665) and Hou et al. (2020, Biostatistics, 21, 417–431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article.     

Estimation of separable direct and indirect effects in continuous time

Many research questions involve time-to-event outcomes that can be prevented from occurring due to competing events. In these settings, we must be careful about the causal interpretation of classical statistical estimands. In particular, estimands on the hazard scale, such as ratios of cause-specific or subdistribution hazards, are fundamentally hard to interpret causally. Estimands on the risk scale, such as contrasts of cumulative incidence functions, do have a clear causal interpretation, but they only capture the total effect of the treatment on the event of interest; that is, effects both through and outside of the competing event. To disentangle causal treatment effects on the event of interest and competing events, the separable direct and indirect effects were recently introduced. Here we provide new results on the estimation of direct and indirect separable effects in continuous time. In particular, we derive the nonparametric influence function in continuous time and use it to construct an estimator that has certain robustness properties. We also propose a simple estimator based on semiparametric models for the two cause-specific hazard functions. We describe the asymptotic properties of these estimators and present results from simulation studies, suggesting that the estimators behave satisfactorily in finite samples. Finally, we reanalyze the prostate cancer trial from Stensrud et al. (2020).     

A clarification of Pouydebat et al., 2008, evolution of grasping among anthropoids

Several statements by Pouydebat et al. (2008) do not adequately represent views of authors cited, in part because they reflect confusion in the literature about terminology regarding precision gripping. We address these problems, by tracing definitions of precision grips through the literature on manipulative behaviour and identifying the grip that is central to the Pouydebat et al. (2008) study. This allows us to offer a clarification of the statements by Pouydebat et al. (2008) regarding the sequence of appearance of human grip capabilities and possible morphological correlates to these capabilities in extant species.     

Discussion on “Instrumental variable estimation of the causal hazard ratio,” by Linbo Wang,Eric Tchetgen Tchetgen,Torben Martinussen,Stijn Vansteelandt

Wang, et al.'s estimator for causal hazard ratios for endogenous treatments makes an important addition to a researcher's toolkit for analyzing censored duration outcomes. Their method complements existing methods in the semiparametric treatment effects literature and suggests useful avenues for future research.     

Identification and characterization of microsatellites loci in Brycon opalinus (Cuvier, 1819) (Characiforme,Characidae, Bryconiae)

Brycon species are present in various basins in Brazil and were or still are part of commercial fisheries and aquaculture activities ( Ferreira et al. 1996 , Mendonça 1994 ). Despite the importance of this group of fish, natural populations of some Brycon species are endangered ( Lins et al. 1997 ). Here, we describe the characterization of seven microsatellite loci that will be useful for the genetic studies in natural and captive populations for these and other species of Brycon.     

A multifaceted view on the impacts of shrub encroachment

Shrub encroachment, a global phenomenon with management implications, is examined in two papers in the current issue of Applied Vegetation Science. Barbosa da Silva et al. show that encroachment simplifies the herbaceous community, and Pittarello et al. illustrate how pastoral practices can restore encroached grasslands. While detrimental effects of shrub encroachment on grassland vegetation are often reported, we argue for a more holistic view when assessing this land‐cover change.