The Effect of Variability on the Optimal Size of a Feeding Territory

Previous empirical and theoretical work has focused on how feedingterritory size is governed by average levels of food availabilityand intrusion pressure; the potentially important effects ofvariability have not yet been studied in detail. Here I incorporatevariation in food availability and intrusion pressure in somesimple optimality models of territory size. The results showthat the possible effects of variability are diverse, includingboth increase and decrease in territory size. And in some cases,variation in food availability produces qualitatively differenteffects than variation in intrusion pressure.     

Treatment Trials for Neonatal Seizures: The Effect of Design on Sample Size

Neonatal seizures are common in the neonatal intensive care unit. Clinicians treat these seizures with several anti-epileptic drugs (AEDs) to reduce seizures in a neonate. Current AEDs exhibit sub-optimal efficacy and several randomized control trials (RCT) of novel AEDs are planned. The aim of this study was to measure the influence of trial design on the required sample size of a RCT. We used seizure time courses from 41 term neonates with hypoxic ischaemic encephalopathy to build seizure treatment trial simulations. We used five outcome measures, three AED protocols, eight treatment delays from seizure onset (T_d) and four levels of trial AED efficacy to simulate different RCTs. We performed power calculations for each RCT design and analysed the resultant sample size. We also assessed the rate of false positives, or placebo effect, in typical uncontrolled studies. We found that the false positive rate ranged from 5 to 85% of patients depending on RCT design. For controlled trials, the choice of outcome measure had the largest effect on sample size with median differences of 30.7 fold (IQR: 13.7–40.0) across a range of AED protocols, T_d and trial AED efficacy (p<0.001). RCTs that compared the trial AED with positive controls required sample sizes with a median fold increase of 3.2 (IQR: 1.9–11.9; p<0.001). Delays in AED administration from seizure onset also increased the required sample size 2.1 fold (IQR: 1.7–2.9; p<0.001). Subgroup analysis showed that RCTs in neonates treated with hypothermia required a median fold increase in sample size of 2.6 (IQR: 2.4–3.0) compared to trials in normothermic neonates (p<0.001). These results show that RCT design has a profound influence on the required sample size. Trials that use a control group, appropriate outcome measure, and control for differences in T_d between groups in analysis will be valid and minimise sample size.     

Sample Size Considerations for GEE Analyses of Three‐Level Cluster Randomized Trials

Summary Cluster randomized trials in health care may involve three instead of two levels, for instance, in trials where different interventions to improve quality of care are compared. In such trials, the intervention is implemented in health care units (“clusters”) and aims at changing the behavior of health care professionals working in this unit (“subjects”), while the effects are measured at the patient level (“evaluations”). Within the generalized estimating equations approach, we derive a sample size formula that accounts for two levels of clustering: that of subjects within clusters and that of evaluations within subjects. The formula reveals that sample size is inflated, relative to a design with completely independent evaluations, by a multiplicative term that can be expressed as a product of two variance inflation factors, one that quantifies the impact of within‐subject correlation of evaluations on the variance of subject‐level means and the other that quantifies the impact of the correlation between subject‐level means on the variance of the cluster means. Power levels as predicted by the sample size formula agreed well with the simulated power for more than 10 clusters in total, when data were analyzed using bias‐corrected estimating equations for the correlation parameters in combination with the model‐based covariance estimator or the sandwich estimator with a finite sample correction.     

Sample Size Estimation in Cluster Randomized Studies with Varying Cluster Size

Cluster randomized studies are common in community trials. The standard method for estimating sample size for cluster randomized studies assumes a common cluster size. However often in cluster randomized studies, size of the clusters vary. In this paper, we derive sample size estimation for continuous outcomes for cluster randomized studies while accounting for the variability due to cluster size. It is shown that the proposed formula for estimating total cluster size can be obtained by adding a correction term to the traditional formula which uses the average cluster size. Application of these results to the design of a health promotion educational intervention study is discussed.     

Effect of Risk of Bias on the Effect Size of Meta-Analytic Estimates in Randomized Controlled Trials in Periodontology and Implant Dentistry

BackgroundRisk of bias (ROB) may threaten the internal validity of a clinical trial by distorting the magnitude of treatment effect estimates, although some conflicting information on this assumption exists.ObjectiveThe objective of this study was evaluate the effect of ROB on the magnitude of treatment effect estimates in randomized controlled trials (RCTs) in periodontology and implant dentistry.MethodsA search for Cochrane systematic reviews (SRs), including meta-analyses of RCTs published in periodontology and implant dentistry fields, was performed in the Cochrane Library in September 2014. Random-effect meta-analyses were performed by grouping RCTs with different levels of ROBs in three domains (sequence generation, allocation concealment, and blinding of outcome assessment). To increase power and precision, only SRs with meta-analyses including at least 10 RCTs were included. Meta-regression was performed to investigate the association between ROB characteristics and the magnitudes of intervention effects in the meta-analyses.ResultsOf the 24 initially screened SRs, 21 SRs were excluded because they did not include at least 10 RCTs in the meta-analyses. Three SRs (two from periodontology field) generated information for conducting 27 meta-analyses. Meta-regression did not reveal significant differences in the relationship of the ROB level with the size of treatment effect estimates, although a trend for inflated estimates was observed in domains with unclear ROBs.ConclusionIn this sample of RCTs, high and (mainly) unclear risks of selection and detection biases did not seem to influence the size of treatment effect estimates, although several confounders might have influenced the strength of the association.     

Utilities of the P‐value Distribution Associated with Effect Size in Clinical Trials

The P‐value, which is widely used for assessing statistical evidence in randomized comparative clinical trials, is a function of the observed effect size of the experimental treatment relative to the control treatment. The relationship of the P‐value with the observed effect size at study completion and the effect size anticipated at the design stage has potential usefulness in providing guidance for planning and interpretation of a clinical trial. The post‐trial power associated with a statistically significant P‐value from a completed study is also a random variable and its use may assist in planning a follow‐up trial to confirm the statistically significant findings in an initial study. A measure of robustness is explored to quantify the degree of sensitivity of the observed P‐value to potential bias that may be contained in the observed effect size.     

The N-Pact Factor: Evaluating the Quality of Empirical Journals with Respect to Sample Size and Statistical Power

The authors evaluate the quality of research reported in major journals in social-personality psychology by ranking those journals with respect to their N-pact Factors (NF)—the statistical power of the empirical studies they publish to detect typical effect sizes. Power is a particularly important attribute for evaluating research quality because, relative to studies that have low power, studies that have high power are more likely to (a) to provide accurate estimates of effects, (b) to produce literatures with low false positive rates, and (c) to lead to replicable findings. The authors show that the average sample size in social-personality research is 104 and that the power to detect the typical effect size in the field is approximately 50%. Moreover, they show that there is considerable variation among journals in sample sizes and power of the studies they publish, with some journals consistently publishing higher power studies than others. The authors hope that these rankings will be of use to authors who are choosing where to submit their best work, provide hiring and promotion committees with a superior way of quantifying journal quality, and encourage competition among journals to improve their NF rankings.     

Effects of Density and Extinction Coefficient on Size Variability in Plant Populations

The effects of density and extinction coefficient on size variability,as measured by the coefficient of variation of plant weightin even-aged monocultures, were investigated theoretically usinga diffusion model of growth and size distribution and a canopyphotosynthesis model over the range of densities at which self-thinning(size-dependent mortality) does not occur. Size inequality (thecoefficient of variation of plant weight) increases with increasingdensity or leaf area index at each growth stage. Plants witherect leaves are prone to lower size inequality than plantswith horizontal leaves. These results agree well with existingobservations on even-aged plant monocultures and suggest thatcompetition between plants is mainly one-sided (competitionfor light). One sided competition affects size variability througha G(t, x) function (mean growth of plants of size x at timet per unit time). Two-sided competition (including competitionfor nutrients) affects size variability through a D(t, x) function(variance of growth of plants of size x at time t per unit time).In this case, size inequality decreases with increasing density.The importance of studying size variability is emphasized. Helianthus annus L., size variability, size inequality, coefficient of variation, competition, density effect, extinction coefficient, diffusion model, canopy photosynthesis model     

Correlation between Genetic Variability and Body Size in Vertebrates

An analysis of allozymic variation carried out in the main groups of vertebrate animals revealed a tendency towards the increased level of genetic polymorphism in the species of small animals compared to the large ones. This tendency was clearly followed in caudate amphibians, fishes, and mammals. The data are discussed in terms of the integration of monogenic and polygenic systems in the populations. It is hypothesized that this relationship between heterozygosity and body size confirms more general regularity consisting in highly statistically significant correlation between polygenic heterozygosity, maturation rate and life span. It is suggested that high rate of development in small animals resulting in early sexual maturation, can serve as a mechanism determining correlation between heterozygosity and body size at the species level. As a result, compared to large animals, small animals display higher rates of generation change, resulting in accelerated growth of population size and faster accumulation of genetic variability.     

Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials

Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of time-to-event endpoints is not easily handled by the standard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event. We show that the final test statistic may ignore a substantial subset of the observed event times. An alternative test incorporating all event times is found, where a conservative assumption must be made in order to guarantee type I error control. We examine the power of this approach using the example of a clinical trial comparing two cancer therapies.     

The Effect of Change in Population Size on DNA Polymorphism

The expected number of segregating sites and the expectation of the average number of nucleotide differences among DNA sequences randomly sampled from a population, which is not in equilibrium, have been developed. The results obtained indicate that, in the case where the population size has changed drastically, the number of segregating sites is influenced by the size of the current population more strongly than is the average number of nucleotide differences, while the average number of nucleotide differences is affected by the size of the original population more severely than is the number of segregating sites. The results also indicate that the average number of nucleotide differences is affected by a population bottleneck more strongly than is the number of segregating sites.     

Some Recent Advances on Statistical Approaches for Planning Multi-regional Clinical Trials

Many multi-regional clinical trials are faced with possible heterogeneity in treatment effect among regions and consequently the interpretation of the trial results is quite challenging. Regional heterogeneity can be caused by the differences in intrinsic factors, extrinsic factors or trial/data quality among regions. An apparent regional difference in treatment effect can be caused by a play of chance or sampling variability. In another aspect, regional heterogeneity may have substantial ramifications on sample size estimation for planning a multi-regional trial, as stipulated in Hung et al. (Pharm Stat 9:173–178, 2010), Lan and Pinheiro (Stat Biosci 14:235–244, 2012) and Quan et al. (Stat Med 33:2191–2205, 2014). Analyses of multi-regional trials are commonly based on a fixed-effect model assuming that the true treatment effects in all regions are equal in magnitude which may or may not be practical. Random-effect modelling has been considered as an alternative to deal with regional heterogeneity. In this research work we shall discuss the statistical implications of the statistical modelling approaches on the type I error probability and statistical power associated with testing the global treatment effect. We also develop formulae to assess the probability that some regions may falsely show an apparent negative trend by chance or sampling variability. As another alternative, a fixed-effect model that accounts for acceptable regional heterogeneity will be introduced.     

Variability and Bimodal Distribution of Size in Microspores of Aesculus hippocastanum

Size variability of uninucleate microspores was studied in horse chestnut (Aesculus hippocastanum L.). Microspores were isolated from buds of different size (3, 4, and 5 mm) taken from lower, middle and upper segments of inflorescences. All analyzed buds showed bimodal distribution of microspore size which confirmed the presence of pollen dimorphism. This revised version was published online in July 2006 with corrections to the Cover Date.     

The Influence of Matrix Size on Statistical Properties of Co-Occurrence and Limiting Similarity Null Models

Null models exploring species co-occurrence and trait-based limiting similarity are increasingly used to explore the influence of competition on community assembly; however, assessments of common models have not thoroughly explored the influence of variation in matrix size on error rates, in spite of the fact that studies have explored community matrices that vary considerably in size. To determine how smaller matrices, which are of greatest concern, perform statistically, we generated biologically realistic presence-absence matrices ranging in size from 3–50 species and sites, as well as associated trait matrices. We examined co-occurrence tests using the C-Score statistic and independent swap algorithm. For trait-based limiting similarity null models, we used the mean nearest neighbour trait distance (NN) and the standard deviation of nearest neighbour distances (SDNN) as test statistics, and considered two common randomization algorithms: abundance independent trait shuffling (AITS), and abundance weighted trait shuffling (AWTS). Matrices as small as three × three resulted in acceptable type I error rates (p < 0.05) for both the co-occurrence and trait-based limiting similarity null models when exclusive p-values were used. The commonly used inclusive p-value (≤ or ≥, as opposed to exclusive p-values; < or >) was associated with increased type I error rates, particularly for matrices with fewer than eight species. Type I error rates increased for limiting similarity tests using the AWTS randomization scheme when community matrices contained more than 35 sites; a similar randomization used in null models of phylogenetic dispersion has previously been viewed as robust. Notwithstanding other potential deficiencies related to the use of small matrices to represent communities, the application of both classes of null model should be restricted to matrices with 10 or more species to avoid the possibility of type II errors. Additionally, researchers should restrict the use of the AWTS randomization to matrices with fewer than 35 sites to avoid type I errors when testing for trait-based limiting similarity. The AITS randomization scheme performed better in terms of type I error rates, and therefore may be more appropriate when considering systems for which traits are not clustered by abundance.     

Cholinesterase Active Center. Statistical Analysis of Structure Variability

The cluster and factor analyses of the data on variability of active center structure of 41 cholinesterases from different animals were carried out. As characteristics of 32 variable amino acid residues, their hydrophobicity, isoelectric point, polarity, and volume were chosen. As a result of the factor analysis, seven factors were extracted (which, based on the factor scores analysis are called the factors of insect AChE, of nematode AChE-3,4, of nematode AChE-1, of nematode AChE-2,B,C, of mammalian BuChE, of squid AChE, and of tick AChE); they are responsible for 79% of the observed variability. The areas of the active center and the characteristics of the residues, which are the material substrate of these factors, are specified. The hierarchical cluster analysis has shown that dendrograms reflecting similarity in summarized reactivity of the active center do not coincide with the phylogenetic tree of the animals. The conclusion has been made that the direction of evolution of the ChE active center, on one hand, does not have a gradual and regular character and, on the other hand, does not correlate with evolution of ChE protein as a whole.     

Effect of Peer Health Workers on AIDS Care in Rakai,Uganda: A Cluster-Randomized Trial

Background

Human resource limitations are a challenge to the delivery of antiretroviral therapy (ART) in low-resource settings. We conducted a cluster randomized trial to assess the effect of community-based peer health workers (PHW) on AIDS care of adults in Rakai, Uganda.

Methodology/Principal Findings

15 AIDS clinics were randomized 2∶1 to receive the PHW intervention (n = 10) or control (n = 5). PHW tasks included clinic and home-based provision of counseling, clinical, adherence to ART, and social support. Primary outcomes were adherence and cumulative risk of virologic failure (>400 copies/mL). Secondary outcomes were virologic failure at each 24 week time point up to 192 weeks of ART. Analysis was by intention to treat. From May 2006 to July 2008, 1336 patients were followed. 444 (33%) of these patients were already on ART at the start of the study. No significant differences were found in lack of adherence (<95% pill count adherence risk ratio [RR] 0.55, 95% confidence interval [CI] 0.23–1.35; <100% adherence RR 1.10, 95% CI 0.94–1.30), cumulative risk of virologic failure (RR 0.81, 95% CI 0.61–1.08) or in shorter-term virologic outcomes (24 week virologic failure RR 0.93, 95% CI 0.65–1.32; 48 week, RR 0.83, 95% CI 0.47–1.48; 72 week, RR 0.81, 95% CI 0.44–1.49). However, virologic failure rates ≥96 weeks into ART were significantly decreased in the intervention arm compared to the control arm (96 week failure RR 0.50, 95% CI 0.31–0.81; 120 week, RR 0.59, 95% CI 0.22–1.60; 144 week, RR 0.39, 95% CI 0.16–0.95; 168 week, RR 0.30, 95% CI 0.097–0.92; 192 week, RR 0.067, 95% CI 0.0065–0.71).

Conclusions/Significance

A PHW intervention was associated with decreased virologic failure rates occurring 96 weeks and longer into ART, but did not affect cumulative risk of virologic failure, adherence measures, or shorter-term virologic outcomes. PHWs may be an effective intervention to sustain long-term ART in low-resource settings.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00675389","term_id":"NCT00675389"}}NCT00675389     

The Effect of Molecular Size on Molecular Mobility in Amorphous Oligosaccharides

The physical properties and especially the molecular mobility of amorphous carbohydrate matrixes directly influence the stability of foods, feeds, and pharmaceuticals and the dessication tolerance of animals and plants during anhydrobiosis. Phosphorescence of the sodium salt of erythrosin B was used to investigate the local molecular mobility in pure amorphous solids of a homologous series of malto-oligosaccharides (maltose, G₂; maltotriose, G₃; maltotetraose, G₄; maltopentaose, G₅; maltohexaose, G₆; and maltoheptaose, G₇); sucrose and maltodextrin DE18 (a hydrolytic fraction of starch) were investigated for comparison. Measurements of the temperature-dependence of the phosphorescence emission energy, an indicator of the extent of local dipolar relaxation, and the phosphorescence emission lifetime, an indicator of the rate of collisional quenching of the excited state by matrix molecules, demonstrate that the local matrix molecular mobility increases with molecular size, and thus, with an increase in T _g, in these glucose oligosaccharides. Master curves of the spectroscopic measures of matrix mobility for each oligosaccharide, plotted against T–T _g, were not superimposable, suggesting that local properties of the amorphous sugar matrixes, rather than T _g per se, influence local matrix mobility. Indicators of spectral heterogeneity also varied with molecular size, indicating that dynamic site heterogeneity also increased with molecular size and thus, T _g. These results emphasize the importance of additional research in developing appropriate “molecular rules” for designing amorphous matrix systems with better long-term stability for foods, feeds, or pharmaceuticals.     

Cluster Randomised Trials in Cochrane Reviews: Evaluation of Methodological and Reporting Practice

Objective

Systematic reviews can include cluster-randomised controlled trials (C-RCTs), which require different analysis compared with standard individual-randomised controlled trials. However, it is not known whether review authors follow the methodological and reporting guidance when including these trials. The aim of this study was to assess the methodological and reporting practice of Cochrane reviews that included C-RCTs against criteria developed from existing guidance.

Methods

Criteria were developed, based on methodological literature and personal experience supervising review production and quality. Criteria were grouped into four themes: identifying, reporting, assessing risk of bias, and analysing C-RCTs. The Cochrane Database of Systematic Reviews was searched (2^nd December 2013), and the 50 most recent reviews that included C-RCTs were retrieved. Each review was then assessed using the criteria.

Results

The 50 reviews we identified were published by 26 Cochrane Review Groups between June 2013 and November 2013. For identifying C-RCTs, only 56% identified that C-RCTs were eligible for inclusion in the review in the eligibility criteria. For reporting C-RCTs, only eight (24%) of the 33 reviews reported the method of cluster adjustment for their included C-RCTs. For assessing risk of bias, only one review assessed all five C-RCT-specific risk-of-bias criteria. For analysing C-RCTs, of the 27 reviews that presented unadjusted data, only nine (33%) provided a warning that confidence intervals may be artificially narrow. Of the 34 reviews that reported data from unadjusted C-RCTs, only 13 (38%) excluded the unadjusted results from the meta-analyses.

Conclusions

The methodological and reporting practices in Cochrane reviews incorporating C-RCTs could be greatly improved, particularly with regard to analyses. Criteria developed as part of the current study could be used by review authors or editors to identify errors and improve the quality of published systematic reviews incorporating C-RCTs.