Confidence limits on the branching order of phylogenetic trees.

We describe a confidence test for branching order that can aid protein phylogeny reconstruction as well as the evaluation of the optimal tree. It is proposed that the process resulting in the observed amino acid residue differences, which is the basis for the identification of the order and relative times of divergence events, is appropriately described by a modification of the negative binomial distribution. The relative total numbers of mutations (accepted and nonaccepted), which result in a given number of amino acid differences, may be obtained as the expectation of this distribution. The associated variances enable significant differences in tree branching order to be established. If the total rates of mutation of the genes encoding the compared proteins are equal, the expected total mutations and their associated variances map identically to their relative times of divergence. In addition, significantly different rates of change (due to differences in total mutation rate and/or acceptance rate) may be identified without the requirement of outlying reference group. The method is equally applicable to phylogenies derived from DNA or RNA sequence information.     

Confidence intervals for directly standardized rates using mid‐p gamma intervals

Directly standardized rates continue to be an integral tool for presenting rates for diseases that are highly dependent on age, such as cancer. Statistically, these rates are modeled as a weighted sum of Poisson random variables. This is a difficult statistical problem, because there are k observed Poisson variables and k unknown means. The gamma confidence interval has been shown through simulations to have at least nominal coverage in all simulated scenarios, but it can be overly conservative. Previous modifications to that method have closer to nominal coverage on average, but they do not achieve the nominal coverage bound in all situations. Further, those modifications are not central intervals, and the upper coverage error rate can be substantially more than half the nominal error. Here we apply a mid‐p modification to the gamma confidence interval. Typical mid‐p methods forsake guaranteed coverage to get coverage that is sometimes higher and sometimes lower than the nominal coverage rate, depending on the values of the parameters. The mid‐p gamma interval does not have guaranteed coverage in all situations; however, in the (not rare) situations where the gamma method is overly conservative, the mid‐p gamma interval often has at least nominal coverage. The mid‐p gamma interval is especially appropriate when one wants a central interval, since simulations show that in many situations both the upper and lower coverage error rates are on average less than or equal to half the nominal error rate.     

Confidence limits for probability of response in multistage phase II clinical trials

Herson (1979, Biometrics 35, 775-783) has given a method for designing one-arm k-stage phase II clinical trials, which permits early termination of the trial if the treatment is apparently ineffective, while retaining acceptable levels of power and significance. This paper gives a method for calculating the confidence limits on the response proportion, conditional on a particular study design.     

Confidence bands for low-dose risk estimation with quantal response data

We study the use of simultaneous confidence bands for low-dose risk estimation with quantal response data, and derive methods for estimating simultaneous upper confidence limits on predicted extra risk under a multistage model. By inverting the upper bands on extra risk, we obtain simultaneous lower bounds on the benchmark dose (BMD). Monte Carlo evaluations explore characteristics of the simultaneous limits under this setting, and a suite of actual data sets are used to compare existing methods for placing lower limits on the BMD.     

A general method for controlling the genome-wide type I error rate in linkage and association mapping experiments in plants

Control of the genome-wide type I error rate (GWER) is an important issue in association mapping and linkage mapping experiments. For the latter, different approaches, such as permutation procedures or Bonferroni correction, were proposed. The permutation test, however, cannot account for population structure present in most association mapping populations. This can lead to false positive associations. The Bonferroni correction is applicable, but usually on the conservative side, because correlation of tests cannot be exploited. Therefore, a new approach is proposed, which controls the genome-wide error rate, while accounting for population structure. This approach is based on a simulation procedure that is equally applicable in a linkage and an association-mapping context. Using the parameter settings of three real data sets, it is shown that the procedure provides control of the GWER and the generalized genome-wide type I error rate (GWER(k)).     

Modeling animals' behavioral response by Markov chain models for capture-recapture experiments

A bivariate Markov chain approach that includes both enduring (long-term) and ephemeral (short-term) behavioral effects in models for capture-recapture experiments is proposed. The capture history of each animal is modeled as a Markov chain with a bivariate state space with states determined by the capture status (capture/noncapture) and marking status (marked/unmarked). In this framework, a conditional-likelihood method is used to estimate the population size and the transition probabilities. The classical behavioral model that assumes only an enduring behavioral effect is included as a special case of the bivariate Markovian model. Another special case that assumes only an ephemeral behavioral effect reduces to a univariate Markov chain based on capture/noncapture status. The model with the ephemeral behavioral effect is extended to incorporate time effects; in this model, in contrast to extensions of the classical behavioral model, all parameters are identifiable. A data set is analyzed to illustrate the use of the Markovian models in interpreting animals' behavioral response. Simulation results are reported to examine the performance of the estimators.     

滇西北石卡雪山2个针叶树种森林上限径向生长对温度和降水的响应

基于树木年轮学的理论和方法,建立滇西北高原石卡雪山森林上限丽江云杉(Picea likiangensis)和高山松(Pinus densata)差值年表,运用响应函数研究其与气候因子的关系,进而阐明影响滇西北高原针叶树种径向生长的主要气候因子,并利用冗余分析(RDA)进一步分析并验证树木生长与温度和降水的关系。研究结果表明:石卡雪山森林上限针叶树种径向生长主要受温度影响,温度和降水对树木生长有滞后效应,2个树种对气候响应存在差异。具体表现为(1)丽江云杉径向生长受温度和降水的共同作用,与上年10月平均最低温呈显著负相关,与上年11月平均最高温以及当年7月温度呈显著正相关,上年8月和当年5月降水抑制其生长;(2)高山松径向生长与上年10月平均温和平均最高温、11月平均温呈显著正相关,与当年7月平均温和平均最高温呈显著负相关,与降水未达到显著相关水平;(3)冗余分析与响应函数分析结果基本一致,进一步证明该方法能够有效量化树木径向生长与气候因子的关系。能够为气候变化背景下的滇西北高原森林生态系统管理与保护提供理论依据。     

Variable reporting and quantitative reviews: a comparison of three meta-analytical techniques

Variable reporting of results can influence quantitative reviews by limiting the number of studies for analysis, and thereby influencing both the type of analysis and the scope of the review. We performed a Monte Carlo simulation to determine statistical errors for three meta‐analytical approaches and related how such errors were affected by numbers of constituent studies. Hedges’d and effect sizes based on item response theory (IRT) had similarly improved error rates with increasing numbers of studies when there was no true effect, but IRT was conservative when there was a true effect. Log response ratio had low precision for detecting null effects as a result of overestimation of effect sizes, but high ability to detect true effects, largely irrespective of number of studies. Traditional meta‐analysis based on Hedges’d are preferred; however, quantitative reviews should use various methods in concert to improve representation and inferences from summaries of published data.     

Confidence intervals for the difference in the success rates of two treatments in the analysis of correlated binary responses

In clinical studies, we often compare the success rates of two treatment groups where post‐treatment responses of subjects within clusters are usually correlated. To estimate the difference between the success rates, interval estimation procedures that do not account for this intraclass correlation are likely inappropriate. To address this issue, we propose three interval procedures by direct extensions of recently proposed methods for independent binary data based on the concepts of design effect and effective sample size used in sample surveys. Each of them is then evaluated with four competing variance estimates. We also extend three existing methods recommended for complex survey data using different weighting schemes required for those three existing methods. An extensive simulation study is conducted for the purposes of evaluating and comparing the performance of the proposed methods in terms of coverage and expected width. The interval estimation procedures are illustrated using three examples in clinical and social science studies. Our analytic arguments and numerical studies suggest that the methods proposed in this work may be useful in clustered data analyses.     

A comparison of two methods for prediction of response and rates of inbreeding in selected populations with the results obtained in two selection experiments

Selection programmes are mainly concerned with increasing genetic gain. However, short-term progress should not be obtained at the expense of the within-population genetic variability. Different prediction models for the evolution within a small population of the genetic mean of a selected trait, its genetic variance and its inbreeding have been developed but have mainly been validated through Monte Carlo simulation studies. The purpose of this study was to compare theoretical predictions to experimental results. Two deterministic methods were considered, both grounded on a polygenic additive model. Differences between theoretical predictions and experimental results arise from differences between the true and the assumed genetic model, and from mathematical simplifications applied in the prediction methods. Two sets of experimental lines of chickens were used in this study: the Dutch lines undergoing true truncation mass selection, the other lines (French) undergoing mass selection with a restriction on the representation of the different families. This study confirmed, on an experimental basis, that modelling is an efficient approach to make useful predictions of the evolution of selected populations although the basic assumptions considered in the models (polygenic additive model, normality of the distribution, base population at the equilibrium, etc.) are not met in reality. The two deterministic methods compared yielded results that were close to those observed in real data, especially when the selection scheme followed the rules of strict mass selection: for instance, both predictions overestimated the genetic gain in the French experiment, whereas both predictions were close to the observed values in the Dutch experiment.     

P > .05: The incorrect interpretation of “not significant” results is a significant problem

Statistically nonsignificant (p > .05) results from a null hypothesis significance test (NHST) are often mistakenly interpreted as evidence that the null hypothesis is true—that there is “no effect” or “no difference.” However, many of these results occur because the study had low statistical power to detect an effect. Power below 50% is common, in which case a result of no statistical significance is more likely to be incorrect than correct. The inference of “no effect” is not valid even if power is high. NHST assumes that the null hypothesis is true; p is the probability of the data under the assumption that there is no effect. A statistical test cannot confirm what it assumes. These incorrect statistical inferences could be eliminated if decisions based on p values were replaced by a biological evaluation of effect sizes and their confidence intervals. For a single study, the observed effect size is the best estimate of the population effect size, regardless of the p value. Unlike p values, confidence intervals provide information about the precision of the observed effect. In the biomedical and pharmacology literature, methods have been developed to evaluate whether effects are “equivalent,” rather than zero, as tested with NHST. These methods could be used by biological anthropologists to evaluate the presence or absence of meaningful biological effects. Most of what appears to be known about no difference or no effect between sexes, between populations, between treatments, and other circumstances in the biological anthropology literature is based on invalid statistical inference.     

Intake rates and the functional response in shorebirds (Charadriiformes) eating macro-invertebrates

As field determinations take much effort, it would be useful to be able to predict easily the coefficients describing the functional response of free-living predators, the function relating food intake rate to the abundance of food organisms in the environment. As a means easily to parameterise an individual-based model of shorebird Charadriiformes populations, we attempted this for shorebirds eating macro-invertebrates. Intake rate is measured as the ash-free dry mass (AFDM) per second of active foraging; i.e. excluding time spent on digestive pauses and other activities, such as preening. The present and previous studies show that the general shape of the functional response in shorebirds eating approximately the same size of prey across the full range of prey density is a decelerating rise to a plateau, thus approximating the Holling type II ('disc equation') formulation. But field studies confirmed that the asymptote was not set by handling time, as assumed by the disc equation, because only about half the foraging time was spent in successfully or unsuccessfully attacking and handling prey, the rest being devoted to searching.A review of 30 functional responses showed that intake rate in free-living shorebirds varied independently of prey density over a wide range, with the asymptote being reached at very low prey densities (<150/m-2). Accordingly, most of the many studies of shorebird intake rate have probably been conducted at or near the asymptote of the functional response, suggesting that equations that predict intake rate should also predict the asymptote.A multivariate analysis of 468 'spot' estimates of intake rates from 26 shorebirds identified ten variables, representing prey and shorebird characteristics, that accounted for 81% of the variance in logarithm-transformed intake rate. But four-variables accounted for almost as much (77.3%), these being bird size, prey size, whether the bird was an oystercatcher Haematopus ostralegus eating mussels Mytilus edulis, or breeding. The four variable equation under-predicted, on average, the observed 30 estimates of the asymptote by 11.6%, but this discrepancy was reduced to 0.2% when two suspect estimates from one early study in the 1960s were removed. The equation therefore predicted the observed asymptote very successfully in 93% of cases. We conclude that the asymptote can be reliably predicted from just four easily measured variables. Indeed, if the birds are not breeding and are not oystercatchers eating mussels, reliable predictions can be obtained using just two variables, bird and prey sizes. A multivariate analysis of 23 estimates of the half-asymptote constant suggested they were smaller when prey were small but greater when the birds were large, especially in oystercatchers. The resulting equation could be used to predict the half-asymptote constant, but its predictive power has yet to be tested. As well as predicting the asymptote of the functional response, the equations will enable research workers engaged in many areas of shorebird ecology and behaviour to estimate intake rate without the need for conventional time-consuming field studies, including species for which it has not yet proved possible to measure intake rate in the field.     

Scaling up from individual behaviour of <Emphasis Type="Italic">Orius sauteri</Emphasis> foraging on <Emphasis Type="Italic">Thrips palmi</Emphasis> to its daily functional response

Functional responses of predators are generally measured under laboratory conditions at rather high prey densities. This is also true for the predation capability of the anthocorid predatory bug Orius sauteri (Poppius). To quantify the daily impact of one female Orius predator on its prey Thrips palmi Karny on greenhouse eggplants where the prey is present below the economic threshold density, we use its patch-leaving and feeding behaviour on eggplant leaves with different prey numbers and scale up to the larger spatio-temporal scale of the greenhouse and one foraging day by means of a simulation model. For this, we also use literature data on the distribution of T. palmi over eggplant leaves. The simulation results in a typical type II functional response for O. sauteri as a function of average T. palmi density: O. sauteri can find and eat approximately 10 prey items per day if T. palmi is present around its economic injury level. The daily mean number of prey eaten per O. sauteri predator, i.e., its predation capability, is highly sensitive to the actual baseline leaving tendency, the effect size of the presence of prey on the baseline leaving tendency and the effect size of the encounter rate with prey thereon.     

Confidence regions for treatment effects in subgroups in biomarker stratified designs

Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Subgroups are usually formed on the basis of a predictive biomarker such as age, sex, or some genetic marker. While, for example, age can be detected precisely, it is often only possible to detect the biomarker status with a certain probability. Because patients detected with a positive or negative biomarker may not be truly biomarker positive or negative, responses in the subgroups depend on the treatment therapy as well as on the sensitivity and specificity of the assay used in detecting the biomarkers. In this work, we show how (approximate) simultaneous confidence intervals and confidence ellipsoid for the treatment effects in subgroups can be found for biomarker stratified clinical trials using a normal framework with normally distributed or binary data. We show that these intervals maintain the nominal confidence level via simulations.     

基于环境DNA-宏条形码技术的水生生态系统入侵生物的早期监测与预警

外来生物入侵是继生境破坏后造成生物多样性丧失的第二大威胁因素, 已对入侵地的生态安全、经济和社会发展及人类健康等造成严重负面影响, 成为21世纪五大全球性环境问题之一。作为水产养殖、航运和水生宠物交易大国, 我国水生生态系统的生物入侵问题尤为严重。研究表明, 系统地构建并应用早期监测预警技术是防控水生生态系统生物入侵最有效的途径。和陆生生物相比, 水生生物群落的物种繁多、群落结构复杂、生物形体微小且在入侵初期群体规模极小、隐匿于水下、可用于物种鉴定的外部形态缺乏, 使得在水生生态系统中构建并应用早期监测和预警体系在技术层面更具挑战。随着高通量测序技术的快速发展, 环境DNA-宏条形码技术成为构建水生生态系统入侵生物早期监测与预警技术的首选。本文主要综述了基于环境DNA-宏条形码技术的水生生态系统入侵生物的早期监测与预警技术方法; 解析了环境DNA-宏条形码监测系统的应用现状、技术优势; 着重探讨了影响监测结果准确性的I型和II型错误及其产生原因, 并为避免两类错误提供了可行的优化/改进方案; 最后对该方法在水生入侵生物监测中的应用前景进行了展望。