The micronucleus test: Statistical design and analysis

Alternative statistical procedures are discussed which may be employed to compare the incidences among treatment groups of micronucleated polychromatic and normochromatic erythrocytes and their ratios. Comparison of incidences of micronucleated polychromatic erythrocytes using a sequential sampling strategy based on the negative binomial distribution is shown to require fewer animals for the same sensitivity of test than a similar procedure based on the binomial distribution. The sequential test is superior, both in power and number of animals required, to an alternative 1-stage test based on the same distribution. The procedure described permits the investigator to optimize the number of animals in each group and the number of cells counted per animal to detect a predetermined increase in the incidence of micronucleated cells over that observed in the control population within chosen limits of type I and type II error. An alternative sequential approach based on the binomial distribution is presented, which is applicable when the number of cells analyzed per animal is variable.     

The micronucleus test: statistical design and analysis.

Alternative statistical procedures are discussed which may be employed to compare the incidences among treatment groups of micronucleated polychromatic and normochromatic erythrocytes and their ratios. Comparison of incidences of micronucleated polychromatic erythrocytes using a sequential sampling strategy based on the negative binomial distribution is shown to require fewer animals for the same sensitivity of test than a similar procedure based on the binomial distribution. The sequential test is superior, both in power and number of animals required, to an alternative 1-stage test based on the same distribution. The procedure described permits the investigator to optimize the number of animals in each test group and the number of cells counted per animal to detect a predetermined increase in the incidence of micronucleated cells over that observed in the control population within chosen limits of type I and type II error. An alternative sequential approach based on the binomial distribution is presented, which is applicable when the number of cells analyzed per animal is variable.     

On the statistical analysis of multiple-choice feeding preference experiments

A stopping rule for an experiment defines when (under what conditions) the experiment is terminated. I investigated the stopping rules used in numerous multiple–choice feeding-preference experiments and also examined a recently proposed method for analyzing the data arising from such experiments. All of the surveyed experiments imposed stopping rules which result in a random total food consumption. If an acceptable quantification of preference is relative consumption of different food types, then the proposed analysis will likely misstate the information about preference conveyed by the data. This is due to the fact that the method may confound differences in preferences among food types with differences in the total consumption across trials. I discuss this issue in detail and present an alternative procedure which is appropriate under all stopping regimes when preference is quantified through relative consumption. The procedure I suggest uses an index which is a multivariate generalization of the preference index suggested by Kogan and Goeden (Ann Entomol Soc 1970; 63: 1175–1180) and Kogan (Ann Entomol Soc 1972; 65: 675–683) and which is analogous to a selection index for discrete food units proposed by Manly (Biometrics 1974; 30: 281–294). Received: 29 November 1997 / Accepted: 20 April 1998     

On identification of the number of best treatments using the Newman-Keuls test

In this paper, we provide a stochastic ordering of the Studentized range statistics under a balanced one-way ANOVA model. Based on this result we show that, when restricted to the multiple comparisons with the best, the Newman-Keuls (NK) procedure strongly controls experimentwise error rate for a sequence of null hypotheses regarding the number of largest treatment means. In other words, the NK procedure provides an upper confidence bound for the number of best treatments.     

Flexible implementations of group sequential stopping rules using constrained boundaries

Group sequential stopping rules are often used during the conduct of clinical trials in order to attain more ethical treatment of patients and to better address efficiency concerns. Because the use of such stopping rules materially affects the frequentist operating characteristics of the hypothesis test, it is necessary to choose an appropriate stopping rule during the planning of the study. It is often the case, however, that the number and timing of interim analyses are not precisely known at the time of trial design, and thus the implementation of a particular stopping rule must allow for flexible determination of the schedule of interim analyses. In this article, we consider the use of constrained stopping boundaries in the implementation of stopping rules. We compare this approach when used on various scales for the test statistic. When implemented on the scale of boundary crossing probabilities, this approach is identical to the error spending function approach of Lan and DeMets (1983).     

Two-stage clinical trial stopping rules

Two-stage stopping rules for clinical trials are considered. The nominal significance level needed for the second-stage test, for any choice of first-stage significance level, is derived for rules with overall significance levels of .01 and .05 and for studies with either half or two-thirds of the patients analyzed in the first stage. A graphical demonstration is given of the inherent tradeoff between power and expected sample size (or probability of early termination). A specific rule, intermediate to those advocated by Pocock (1977, Biometrika 64, 191-199) and O'Brien and Fleming (1979, Biometrics 5, 549-556), is recommended.     

The antithrombogenic potential of a polyhedral oligomeric silsesquioxane (POSS) nanocomposite

We have developed a nanocomposite using a silica nanocomposite polyhedral oligomeric silsesquioxane (POSS) and poly(carbonate-urea)urethane (PCU) for potential use in cardiovascular bypass grafts and the microvascular component of artificial capillary beds. In this study, we sought to compare its antithrombogenicity to that of conventional polymers used in vascular bypass grafts so as to improve upon current patency rates, particularly in the microvascular setting. Using atomic force microscopy (AFM) and transmission electron microscopy (TEM), surface topography and composition were studied, respectively. The ability of the nanocomposite surface to repel both proteins and platelets in vitro was assessed using thromboelastography (TEG), fibrinogen ELISA assays, antifactor Xa assays, scanning electron microscopy (SEM), and platelet adsorption tests. TEG analysis showed a significant decrease in clot strength (one-way ANOVA, p < 0.001) and increase in clot lysis (one-way ANOVA, p < 0.0001) on the nanocomposite when compared to both poly(tetrafluoroethylene) (PTFE) and PCU. ELISA assays indicate lower adsorption of fibrinogen to the nanocomposite compared to PTFE (one-way ANOVA, p < 0.01). Interestingly, increasing the concentration of POSS nanocages within these polymers was shown to proportionately inhibit factor X activity. Platelet adsorption at 120 min was also lower compared to PTFE and PCU (two-way ANOVA, p < 0.05). SEM images showed a "speckled" morphologic pattern with Cooper grades I platelet adsorption morphology on the nanocomposite compared to PTFE with grade IV morphology. On the basis of these results, we concluded that POSS nanocomposites possess greater thromboresistance than PTFE and PCU, making it an ideal material for the construction of both bypass grafts and microvessels.     

Targeted development of informative microsatellite (SSR) markers

We describe a novel approach, selectively amplified microsatellite (SAM) analysis, for the targeted development of informative simple sequence repeat (SSR) markers. A modified selectively amplified microsatellite polymorphic loci assay is used to generate multi-locus SSR fingerprints that provide a source of polymorphic DNA markers (SAMs) for use in genetic studies. These polymorphisms capture the repeat length variation associated with SSRs and allow their chromosomal location to be determined prior to the expense of isolating and characterising individual loci. SAMs can then be converted to locus-specific SSR markers with the design and synthesis of a single primer specific to the conserved region flanking the repeat. This approach offers a cost-efficient and rapid method for developing SSR markers for predetermined chromosomal locations and of potential informativeness. The high recovery rate of useful SSR markers makes this strategy a valuable tool for population and genetic mapping studies. The utility of SAM analysis was demonstrated by the development of SSR markers in bread wheat.     

Time-variant spectral analysis of heart rate variability during parabolic flight with and without LBNP

Modifications of autonomic activity during parabolic flight were studied by a time-variant model able to estimate low (LF, 0.04-0.14 Hz) and high (HF, 0.14-0.35 Hz) frequency spectral components on a beat-to-beat basis. Ten subjects were studied with and without lower body negative pressure (LBNP). ECG and Gz load were digitized (500 Hz) and RR interval variability series extracted. Beat-to-beat mean RR, variance, LF and HF power were obtained. One-way ANOVA (p<0.01) was used to compare values obtained during starting 1Gz (I), first 1.8Gz (II), 0Gz (III), second 1.8Gz (IV), ending 1Gz (V). Without LBNP, total and LF power increased during 0Gz to 1.69 +/- 1.41 and 2.87 +/- 4.66 respectively (mean +/- SD, normalized by phase I value). With LBNP, their change during 0Gz (1.38 +/- 1.37 and 1.54 +/- l.04 respectively) reached significance only with phase II and phase V. Phase I HF power was higher than in the other phases, both without and with LBNP.     

Some Frequentist Properties of a Bayesian Method in Clinical Trials

Interim analysis in clinical trials involving two treatments are commonplace nowadays. Concerns from different points of view are widely seen in the literature. With a Bayesian approach there is no consideration of type I error and no power calculation. In contrast, there is no difficulty or arbitrariness in picking a prior distribution with a classical approach. In this paper, however, a stopping rule based on the Bayesian approach is discussed from a classical point of view. In specific, we consider application to normal sampling analyzed in stages and demonstrate the role of the prior distributions. In the first part of the paper, we define the stopping rules based on the posterior probabilities. We then develop the stopping boundaries in explicit forms, which can be easily computed with a hand calculator and a standard normal probability distribution table. We also summarize the frequency characteristics of this stopping rule into several results. The major question that is addressed in the second part of the paper is: how will a prior affect the results of a clinical trial study based on the posterior probabilities? The criteria for assessment will be strictly of a Neyman-Pearson kind. We use N(v, τ₂) as the prior distribution for the difference between treatments, δ. We show that the test is unbiased if v = 0 or τ = ∞. In addition, some rather obvious facts are again summarized into a couple of results. We also discuss, with a table and a figure, the power functions of non-trivial cases with extreme v and τ using a numerical example.     

Surface Topography Assessments of Spine Shape Change within the Day in Healthy Male Adults

Surface topography is a no-invasive, radiation-free method that can measure sufficient surface spine parameters by the structured back surface scan and a precise anatomical landmarks recognition. The purpose of the present study was to measure the spine shape parameter changes within the day via the DIERS Formetric 4D analysis system. Ten male healthy volunteers were recruited to participate in the experiment. All participants were sedentary people with the average sitting time during study or work t ≥ 8 h and without any back disease in the past six months. Data were analyzed by one-way ANOVA, which set time points within the day as variable and shape results as the dependent variable. The significant difference could be found for the trunk length VP-DM with a one-way ANOVA test of p = 0.011. There was a significant difference (p = 0.024) between time slots of 9 am and 7 pm with 95%CI (–15.83, –1.01) and MD –8.42. No significant difference statistically for the scoliosis angle and the p-value of the one-way ANOVA test is 0.715. There was no significant difference for trunk inclination VP-DM with a one-way ANOVA test of p = 0.284. Statistical analysis depicted no significant difference for the trunk imbalance VP-DM with a one-way ANOVA test of p = 0.730. Trunk length VP-DM was significantly decreased in the afternoon and evening. This may be a potential back pain risk for sedentary individuals. Regular physical activity and mild to moderate exercise are recommended to improve spinal stability and maintain spinal shape.     

Behavioral testing of antidepressant compounds: an analysis of crossover design for correlated binary data

The differential reinforcement of low-rate 72 seconds schedule (DRL-72) is a standard behavioral test procedure for screening potential antidepressant compounds. The protocol for the DRL-72 experiment, proposed by Evenden et al. (1993), consists of using a crossover design for the experiment and one-way ANOVA for the statistical analysis. In this paper we discuss the choice of several crossover designs for the DRL-72 experiment and propose to estimate the treatment effects using either generalized linear mixed models (GLMM) or generalized estimating equation (GEE) models for clustered binary data.     

Improved Repeated Confidence Bounds in Trials with a Maximal Goal

Repeated confidence intervals can be computed at every interim analysis of a flexible group sequential design without the need to stop the trial with a pre‐planned stopping rule. Often, however, there is a maximal goal such that the trial is surely stopped if this goal is reached. This induces a maximal stopping rule, and repeated confidence intervals are strictly conservative, when adhering to it. A modification is proposed which uniformly improves the one sided repeated confidence interval in such a situation. It preserves the monitoring character, and leads to uniformly smaller intervals, when reaching the maximal goal at an interim analysis. The modification is worked out for two stage designs and is indicated for multi‐stage trials. The extent of the improvement is quantified for two simple scenarios.     

Adaptive design and estimation in randomized clinical trials with correlated observations

Clinical trial designs involving correlated data often arise in biomedical research. The intracluster correlation needs to be taken into account to ensure the validity of sample size and power calculations. In contrast to the fixed-sample designs, we propose a flexible trial design with adaptive monitoring and inference procedures. The total sample size is not predetermined, but adaptively re-estimated using observed data via a systematic mechanism. The final inference is based on a weighted average of the block-wise test statistics using generalized estimating equations, where the weight for each block depends on cumulated data from the ongoing trial. When there are no significant treatment effects, the devised stopping rule allows for early termination of the trial and acceptance of the null hypothesis. The proposed design updates information regarding both the effect size and within-cluster correlation based on the cumulated data in order to achieve a desired power. Estimation of the parameter of interest and its confidence interval are proposed. We conduct simulation studies to examine the operating characteristics and illustrate the proposed method with an example.     

A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH‐DIMENSIONAL MULTIVARIATE DATA

Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high‐dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high‐dimensional datasets.     

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects.     

Grapevine Phenological Quantitative Trait SSR Genotyping Using High-Throughput HRM-PCR Analysis

Discrimination among grapevine varieties based on quantitative traits, such as flowering, veraison and ripening dates is crucial for variety selection in the context of climate change and in breeding programs. These traits are under complex genetic control for which 6 linked SSR loci (VVS2, VVIn16, VMC7G3, VrZAG29, VMC5G7, and VVIB23) have been identified. Using these markers in HRM-PCR analysis, we assessed genetic diversity among a large collection of 192 grapevine varieties. The grapevine germplasm used encompasses the majority of Greek vineyard with 181 varieties, 3 prominent foreign varieties and 11 varieties of Palestinian origin. The SSR markers used were highly polymorphic, displaying unique melting curves for unusually higher number of samples than generally observed in SSR analysis. This prompted us to examine sequence composition for selected samples and found that variation present as SNPs in the flanking sequences of SSR motifs was responsible for the observed polymorphism. Hence, HRM-PCR proved to be a tool of higher analytical power to distinguish genotypes surpassing the discrimination power of conventional gel-based SSR analysis. The study provides a better understanding of genetic variation of SSR marker loci associated to phenological traits in grapevine varieties, signifying an analytical methodology that may be of higher discrimination power in detection of polymorphism for utilization in breeding programs.     

Probabilistic tests and stopping rules associated with hierarchical classification techniques

A test of random noise and an objective stopping rule are derived for use in association with hierarchical classification techniques. The apparatus required is replicated sampling and combinatorial analysis. The test and rule are based on the conditional probabilities of the number of sites all of whose replicates belong to the same group at each stage of the classification. These devices were tested on presence/absence data simulated from dependent multinomial trials. Where the number of groups became large the classification techniques proved somewhat unreliable because the groups were too similar. Usually clearer distinctions between groups will occur in nature. However, the test for random noise proved entirely successful and the stopping rule was quite successful, although, as one might expect, it was less successful when the classification itself was less successful.     

A Rescue Strategy for Handling Unevaluable Patients in Simon’s Two Stage Design

For phase II oncology trials, Simon’s two-stage design is the most commonly used strategy. However, when clinically unevaluable patients occur, the total number of patients included at each stage differs from what was initially planned. Such situations raise concerns about the operating characteristics of the trial design. This paper evaluates three classical ad hoc strategies and a novel one proposed in this work for handling unevaluable patients. This latter is called the rescue strategy which adapts the critical stopping rules to the number of unevaluable patients at each stage without modifying the planned sample size. blue Simulations show that none of these strategies perfectly match the original target constraints for type I and II error rates. Our rescue strategy is nevertheless the one which best approaches the target error rates requirement. A re-analysis of one real phase II clinical trials on metastatic cancer illustrates the use of the proposed strategy.     

Visualizing the effects of rTMS in a patient sample: small N vs. group level analysis

The use of transcranial magnetic stimulation (TMS) to assess changes in cortical excitability is a tool used with increased prevalence in healthy and impaired populations. One factor of concern with this technique is how to achieve adequate statistical power given constraints of a small number of subjects and variability in responses. This paper compares a single pulse excitability measure using traditional group-level statistics vs single subject analyses in a patient population of subjects with focal hand dystonia, pre and post repetitive TMS (rTMS). Results show significant differences in cortical excitability for 4/5 subjects using a split middle line analysis on plots of individual subject data. Group level statistics (ANOVA), however, did not detect any significant findings. The consideration of single subject statistics for TMS excitability measures may assist researchers in describing the variably of rTMS outcome measures.