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M. J. Faddy and D. M. Smith          1112
Modeling the Dependence between the Number of Trials and the Success Probability in Binary Trials
Un modèle pour essais binaires reposant sur une généralisation bivariée du processus de Poisson pour le nombre de succès et le nombre d'essais avec des probabilités de transition dépendantes des nombres cumulés de succès et d'essais est utilisé pour ré-analyser des données récemment publiées de Zhu, Eickhoff et Kaiser (2003, Biometrics 59, 955–961). Cette modélisation admet des distributions différentes pour les nombres d'essais et les nombres de succès conditionnellement au nombre d'essais qui généralisent les distributions binomiales et de Poisson, sans certaines des restrictions apparentes dans le modèle mixte beta-binomial-Poisson de Zhu et coll. (2003). Quelques différences très marquées apparaissent entre les résultats de cette analyse et ceux décrits dans Zhu et coll. (2003).     

On models for binomial data with random numbers of trials

A binomial outcome is a count s of the number of successes out of the total number of independent trials n=s+f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability pi of success that cannot be directly incorporated by the logistic regression model. Observations where n= 0 are excluded from the binomial analysis yet may be important to understanding how pi is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study.     

Estimating the variance of disease-prevalence estimates from population-based registries

We propose a new Poisson method to estimate the variance for prevalence estimates obtained by the counting method described by Gail et al. (1999, Biometrics 55, 1137-1144) and to construct a confidence interval for the prevalence. We evaluate both the Poisson procedure and the procedure based on the bootstrap proposed by Gail et al. in simulated samples generated by resampling real data. These studies show that both variance estimators usually perform well and yield coverages of confidence intervals at nominal levels. When the number of disease survivors is very small, however, confidence intervals based on the Poisson method have supranominal coverage, whereas those based on the procedure of Gail et al. tend to have below-nominal coverage. For these reasons, we recommend the Poisson method, which also reduces the computational burden considerably.     

Some Approximations of the Borel-Tanner or the Generalized Poisson Distribution

This paper considers some approximations for the Borel-Tanner (Generalized Poisson) sums by using (i) Gram-Charlier Poisson expansion, (ii) Mixture of two Poisson distributions, (iii) Variance stabilizing technique, and (iv) negative binomial distribution. It has been found that the approximation obtained by using the negative binomial distribution seems to be more efficient than the other approximation.     

Sample size calculations for noninferiority trials with Poisson distributed count data

Clinical trials with Poisson distributed count data as the primary outcome are common in various medical areas such as relapse counts in multiple sclerosis trials or the number of attacks in trials for the treatment of migraine. In this article, we present approximate sample size formulae for testing noninferiority using asymptotic tests which are based on restricted or unrestricted maximum likelihood estimators of the Poisson rates. The Poisson outcomes are allowed to be observed for unequal follow‐up schemes, and both the situations that the noninferiority margin is expressed in terms of the difference and the ratio are considered. The exact type I error rates and powers of these tests are evaluated and the accuracy of the approximate sample size formulae is examined. The test statistic using the restricted maximum likelihood estimators (for the difference test problem) and the test statistic that is based on the logarithmic transformation and employs the maximum likelihood estimators (for the ratio test problem) show favorable type I error control and can be recommended for practical application. The approximate sample size formulae show high accuracy even for small sample sizes and provide power values identical or close to the aspired ones. The methods are illustrated by a clinical trial example from anesthesia.     

Oviposition pattern of phytophagous insects: on the importance of host population heterogeneity

Frequency distributions of insect immatures per host are often fitted to contagious distributions, such as the negative binomial, to deduce oviposition pattern. However, different mechanisms can be involved for each theoretical distribution and additional biological information is needed to correctly interpret the fits. We chose the chestnut weevil Curculio elephas, a pest of the European chestnut Castanea sativa, as a model to illustrate the difficulties of inferring oviposition pattern from fits to theoretical distributions and from the variance/mean ratio. From field studies over 13–16 years, we show that 20 out of the 31 yearly distributions available fit a negative binomial and 25 a zero-inflated Poisson (ZIP). No distribution fits a Poisson distribution. The ZIP distribution assumes heterogeneity within the fruit population. There are two categories of host: the first comprises chestnuts unsuitable for weevil oviposition or in excess relative to the number of weevil females, and the second comprises suitable fruits in which oviposition behavior is random. Our results confirm this host heterogeneity. According to the ZIP distribution, the first category of hosts includes on average 74% of the chestnuts. A negative binomial distribution may be generated by either true or false contagion. We show that neither interference between weevil females, nor spatial variation in the infestation rate exist. Consequently, the observed distributions of immatures are not the result of false contagion. Nevertheless, we cannot totally exlude true contagion of immatures. In this paper we discuss the difficulty of testing true contagion in natural conditions. These results show that we cannot systematically conclude in favour of contagion when fitting a distribution such as the negative binomial or when a variance/mean ratio is higher than unity. Received: 22 September 1997 / Accepted: 15 December 1997     

Assessing plant-nutrient relationships in highly invaded Californian grasslands using non-normal probability distributions

Question: Is native species occurrence related to soil nutrients in highly invaded Californian annual grasslands? What is the best method to analyze this relationship, given that native species occur in very low numbers and are absent from many locations? Location: California, USA. Methods: We investigated the effects of soil characteristics and livestock grazing on native plant occurrence at 40 plots from six sites during the period 2003–2005. Low absolute cover (< 5.8%) of native species resulted in strongly skewed, zero-inflated data sets. To overcome problems in the analysis created by non-normality and correlations within plots, we used GLMs and GLMMs, either with a Poisson or a negative binomial distribution, to analyse native species richness and Nassella pulchra cover. Results: N. pulchra cover was strongly associated with low phosphorus in sandy soils, whereas native species richness was highest in soils with low available nitrogen (high C:N). Conclusion: Under current conditions, phosphorus seems to be a critical factor influencing abundance of N. pulchra. Low fertility soils may provide refugia for native species in highly invaded California grasslands as they may be below a threshold required for non-native annuals to completely dominate. By using non-normal distributions in linear models with random components, we report well fitted models with more accurately tested significant covariates.     

Spike propagation in dendrites with stochastic ion channels

We investigate the effects of the stochastic nature of ion channels on the faithfulness, precision and reproducibility of electrical signal transmission in weakly active, dendritic membrane under in vitro conditions. The properties of forward and backpropagating action potentials (BPAPs) in the dendritic tree of pyramidal cells are the subject of intense empirical work and theoretical speculation (Larkum et al., 1999; Zhu, 2000; Larkum et al., 2001; Larkum and Zhu, 2002; Schaefer et al., 2003; Williams, 2004; Waters et al., 2005). We numerically simulate the effects of stochastic ion channels on the forward and backward propagation of dendritic spikes in Monte-Carlo simulations on a reconstructed layer 5 pyramidal neuron. We report that in most instances there is little variation in timing or amplitude for a single BPAP, while variable backpropagation can occur for trains of action potentials. Additionally, we find that the generation and forward propagation of dendritic Ca²⁺ spikes are susceptible to channel variability. This indicates limitations on computations that depend on the precise timing of Ca²⁺ spikes. Action Editor : Alain Destexhe     

A Space–Time Scan Statistic for Detecting Emerging Outbreaks

Summary As a major analytical method for outbreak detection, Kulldorff's space–time scan statistic (2001, Journal of the Royal Statistical Society, Series A 164, 61–72) has been implemented in many syndromic surveillance systems. Since, however, it is based on circular windows in space, it has difficulty correctly detecting actual noncircular clusters. Takahashi et al. (2008, International Journal of Health Geographics 7 , 14) proposed a flexible space–time scan statistic with the capability of detecting noncircular areas. It seems to us, however, that the detection of the most likely cluster defined in these space–time scan statistics is not the same as the detection of localized emerging disease outbreaks because the former compares the observed number of cases with the conditional expected number of cases. In this article, we propose a new space–time scan statistic which compares the observed number of cases with the unconditional expected number of cases, takes a time‐to‐time variation of Poisson mean into account, and implements an outbreak model to capture localized emerging disease outbreaks more timely and correctly. The proposed models are illustrated with data from weekly surveillance of the number of absentees in primary schools in Kitakyushu‐shi, Japan, 2006.     

Joint monitoring and prediction of accrual and event times in clinical trials

In many clinical trials, the primary endpoint is time to an event of interest, for example, time to cardiac attack or tumor progression, and the statistical power of these trials is primarily driven by the number of events observed during the trials. In such trials, the number of events observed is impacted not only by the number of subjects enrolled but also by other factors including the event rate and the follow‐up duration. Consequently, it is important for investigators to be able to monitor and predict accurately patient accrual and event times so as to predict the times of interim and final analyses and enable efficient allocation of research resources, which have long been recognized as important aspects of trial design and conduct. The existing methods for prediction of event times all assume that patient accrual follows a Poisson process with a constant Poisson rate over time; however, it is fairly common in real‐life clinical trials that the Poisson rate changes over time. In this paper, we propose a Bayesian joint modeling approach for monitoring and prediction of accrual and event times in clinical trials. We employ a nonhomogeneous Poisson process to model patient accrual and a parametric or nonparametric model for the event and loss to follow‐up processes. Compared to existing methods, our proposed methods are more flexible and robust in that we model accrual and event/loss‐to‐follow‐up times jointly and allow the underlying accrual rates to change over time. We evaluate the performance of the proposed methods through simulation studies and illustrate the methods using data from a real oncology trial.     

Optimal response-adaptive designs for continuous responses in phase III trials

Optimal response-adaptive designs in phase III clinical trial set up are gaining more interest. Most of the available designs are not based on any optimal consideration. An optimal design for binary responses is given by Rosenberger et al. (2001) and one for continuous responses is provided by Biswas and Mandal (2004). Recently, Zhang and Rosenberger (2006) proposed another design for normal responses. This paper illustrates that the Zhang and Rosenberger (2006) design is not suitable for normally distributed responses, in general. The approach cannot be extended for other continuous response cases, such as exponential or gamma. In this paper, we first describe when the optimal design of Zhang and Rosenberger (2006) fails. We then suggest the appropriate adjustments for designs in different continuous distributions. A unified framework to find optimal response-adaptive designs for two competing treatments is proposed. The proposed methods are illustrated using some real data.     

Likelihood-Based Modeling and Analysis of Data Underdispersed Relative to the Poisson Distribution

By using a generalization of the Poisson process, distributions can be constructed that show appropriate amounts of underdispersion relative to the Poisson distribution that may be apparent from observed data. These are then used to examine the differences between the distributions of numbers of fetal implants in mice corresponding to different doses of the herbicide 2,4,5-T.     

Registration of noncommercial randomised clinical trials: the feasibility of using trial registries to monitor the number of trials

ABSTRACT: BACKGROUND: A 2003 survey suggested the number of noncommercial trials in the UK was declining. Formation of the NIHR in 2006 and increased research spending by the Department of Health may have increased the number of noncommercial trials but no data are available. METHODS: Available data on UK noncommercial trials were obtained from the two relevant registries: ISRCTN register for the UK, and US ClinicalTrials.gov. Data on each trial were sorted by start year, and compared with the : 2003 survey, and UKCRN portfolio database from 2007. RESULTS: The number of UK noncommercial trials registered rose from 25 in 1990 to 188 in 1999, peaked at 533 in 2003, and fell back to 334 in 2009. Total trials registered was similar to but slightly above those in the 2003 survey up to 1998, then rose sharply to 2003 before falling to 2007. From 2007 to 2009 the number registered to start each year was similar to but slightly above the UKCRN database. Less than 10% of UK noncommercial trials registered with ClinGov for most years before 2005, but this rose to 35% by 2009. CONCLUSIONS: For the periods of overlap, trial registration data provide fairly similar totals to other sources on the number of noncommercial trials starting each year. The rise and fall in the number of trials registered between 1999 and 2007 was due to those registered in the ISRCTN database as funded by NHS Trusts. After 2007, the number of trials registered as funded by NHS Trusts has fallen in the ISRCTN register but these trials may have migrated to the US ClinGov register. The total number of noncommercial trial starts, excluding those funded by NHS Trusts, has been upward since around 2002. By 2009 the two main funders were NIHR and charities. Feasibility of using registration data to monitor the number of noncommercial trials has been demonstrated but is complicated by the use of two registers and difficulties in accessing the data. We recommend an annual report on the number of noncommercial trials registering each year.     

A Check-List of the Spiders (Araneae) of the Bolshekhekhtsyrski Nature Reserve, Khabarovsk Province, the Russian Far East

326 species of spiders belonging to 26 families are recorded from the Bolshekhekhtsyrski State Nature Reserve,of them 70 are new records for the reserve and six are new to the fauna of Russia:Asperthorax borealis Ono et Saito,2001; Cyclosa kumadai Tanikawa,1992; Cyclosa okumae Tanikawa,1992(earlier it was identified as C. argenteoalba Bosenberg et Strand,1906); Haplodrassus taepaikensis Paik,1992; Hypselistes fossilobus Fei et Zhu,1993; and Pachygnatha gaoi Zhu et al.,2003. The name Pronous minutus (S. Saito,1939) is synonymized with Pronoides brunneus Schenkel,1936. The male of H. taepaikensis is illustrated for the first time. Composition of the fauna is briefly discussed; 41% of the recorded species have their ranges confined to the SE Palaearctics. By its species diversity,the reserve's fauna is the second largest local fauna eastward of the Urals. An expected spider diversity of this reserve is likely to be over 400 species.     

Small bending,big curvature

<正>The classical "Cholodny-Went theory" predicted that directional stimuli trigger the redistribution of auxin, which governs the differential growth of plant organs through potent effects on cell expansion, thereby establishing an"auxin-then-growth" paradigm; this theory has been validated for both gravitropism and phototropism in plants(reviewed in Muthert et al., 2020).     

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.     

Stochastic automaton models for interaction of excitatory and inhibitory impulse sequences in neurons

The mechanisms by which excitatory and inhibitory input impulse sequences interact in changing the spike probability in neurons are examined in the two mathematical neuron models; one is a real-time neuron model which is close to physiological reality, and the other a stochastic automaton model for the temporal pattern discrimination proposed in the previous paper (Tsukada et al., 1976), which is developed in this paper as neuron models for interaction of excitatory and inhibitory input impulse sequences. The interval distributions of the output spike train from these models tend to be multimodal and are compared with those used for experimental data, reported by Bishop et al. (1964) for geniculate neuron activity and Poisson process deleting model analyzed by Ten Hoopen et al. (1966). Special attention, moreover, should be paid to how different forms of inhibitory input are transformed into the output interval distributions through these neuron models. These results exhibit a clear correlation between inhibitory input form and output interval distribution. More detailed information on this mechanism is obtained from the computations of recurrence-time under the stationary condition to go from active state to itself for the first time, each of which is influenced by the inhibitory input forms. In addition to these facts, some resultant characteristics on interval histogram and serial correlation are discussed in relation to physiological data from the literature.     

Extracting DNA from the gut microbes of the termite (Zootermopsis nevadensis)

Termites are among the few animals known to have the capacity to subsist solely by consuming wood. The termite gut tract contains a dense and species-rich microbial population that assists in the degradation of lignocellulose predominantly into acetate, the key nutrient fueling termite metabolism (Odelson & Breznak, 1983). Within these microbial populations are bacteria, methanogenic archaea and, in some ("lower") termites, eukaryotic protozoa. Thus, termites are excellent research subjects for studying the interactions among microbial species and the numerous biochemical functions they perform to the benefit of their host. The species composition of microbial populations in termite guts as well as key genes involved in various biochemical processes has been explored using molecular techniques (Kudo et al., 1998; Schmit-Wagner et al., 2003; Salmassi & Leadbetter, 2003). These techniques depend on the extraction and purification of high-quality nucleic acids from the termite gut environment. The extraction technique described in this video is a modified compilation of protocols developed for extraction and purification of nucleic acids from environmental samples (Mor et al., 1994; Berthelet et al., 1996; Purdy et al., 1996; Salmassi & Leadbetter, 2003; Ottesen et al. 2006) and it produces DNA from termite hindgut material suitable for use as template for polymerase chain reaction (PCR).     

Validation of surrogate markers in multiple randomized clinical trials with repeated measurements: canonical correlation approach

Part of the recent literature on the evaluation of biomarkers as surrogate endpoints starts from a multitrial context, which leads to a definition of validity in terms of the quality of both trial-level and individual-level association between the surrogate and true endpoints (Buyse et al., 2000, Biostatistics1, 49-67). These authors concentrated on cross-sectional continuous responses. However, in many randomized clinical studies, repeated measurements are encountered on either or both endpoints. A challenge in this setting is the formulation of a simple and meaningful concept of "surrogacy."Alonso et al. (2003, Biometrical Journal45, 931-945) proposed the variance reduction factor (VRF) to evaluate surrogacy at the individual level. They also showed how and when this concept should be extended to study surrogacy at the trial level. Here, we approach the problem from the natural canonical correlation perspective. We define a class of canonical correlation functions that can be used to study surrogacy at the trial and individual level. We show that the VRF and the R2 measure defined by Buyse et al. (2000) follow as special cases. Simulations are conducted to evaluate the performance of different members of this family. The methodology is illustrated on data from a meta-analysis of five clinical trials comparing antipsychotic agents for the treatment of chronic schizophrenia.