Assessment of the efficacy of disinfectants on surfaces

The Literature on testing the efficacy of disinfectants covers a century. Most predominant and standardized are the so called suspension tests that allow for the quantitative estimation of the microbicidal activity (log reduction factors) of disinfectants on test organisms suspended in solutions of these products.Since the outcome of suspension tests might be a poor predictor for the efficacy of a disinfectant under practical circumstances, especially with regard to bacteria attached to surfaces, a variety of test procedures have been designed to mimic those conditions. Within the framework of CEN/TC 216 a quantitative surface test has been developed to assess the activity of disinfectants on bacteria or fungi attached to steenless steel surfaces. Preliminary data suggest that covering a dried inoculum with disinfectant without any further mechanical action to improve contact between organisms and disinfectant, will usually result in lower reduction factors than those obtained with suspension tests. Comparative testing further suggests that by applying mechanical action, with the effect of resuspending cells in the liquid on the surface,—similar to mopping, brushing etc.— will result in higher reduction rates. Although not unexpected these findings emphasize the importance of designing test methods based on practical applications of disinfectants.     

Misprescription and misuse of one‐tailed tests

One‐tailed statistical tests are often used in ecology, animal behaviour and in most other fields in the biological and social sciences. Here we review the frequency of their use in the 1989 and 2005 volumes of two journals (Animal Behaviour and Oecologia), their advantages and disadvantages, the extensive erroneous advice on them in both older and modern statistics texts and their utility in certain narrow areas of applied research. Of those articles with data sets susceptible to one‐tailed tests, at least 24% in Animal Behaviour and at least 13% in Oecologia used one‐tailed tests at least once. They were used 35% more frequently with nonparametric methods than with parametric ones and about twice as often in 1989 as in 2005. Debate in the psychological literature of the 1950s established the logical criterion that one‐tailed tests should be restricted to situations where there is interest only in results in one direction. ‘Interest’ should be defined; however, in terms of collective or societal interest and not by the individual investigator. By this ‘collective interest’ criterion, all uses of one‐tailed tests in the journals surveyed seem invalid. In his book Nonparametric Statistics, S. Siegel unrelentingly suggested the use of one‐tailed tests whenever the investigator predicts the direction of a result. That work has been a major proximate source of confusion on this issue, but so are most recent statistics textbooks. The utility of one‐tailed tests in research aimed at obtaining regulatory approval of new drugs and new pesticides is briefly described, to exemplify the narrow range of research situations where such tests can be appropriate. These situations are characterized by null hypotheses stating that the difference or effect size does not exceed, or is at least as great as, some ‘amount of practical interest’. One‐tailed tests rarely should be used for basic or applied research in ecology, animal behaviour or any other science.     

Testing the Independence of Two Diagnostic Tests

Consider two diagnostic procedures having binary outcomes. If one of the tests results in a positive finding, a more definitive diagnostic procedure will be administered to establish the presence or absence of a disease. The use of both tests will improve the overall screening sensitivity when the two tests are independent, compared with employing two tests that are positively correlated. We estimate the correlation coefficient of the two tests and derive statistical methods for testing the independence of the two diagnostic procedures conditional on disease status. The statistical tests are used to investigate the independence of mammography and clinical breast exams aimed at establishing the benefit of early detection of breast cancer. The data used in the analysis are obtained from periodic screening examinations of three randomized clinical trials of breast cancer screening. Analysis of each of these trials confirms the independence of the clinical breast and mammography examinations. Based on these three large clinical trials, we conclude that a clinical breast exam considerably increases the overall sensitivity relative to screening with mammography alone and should be routinely included in early breast cancer detection programs.     

Non-Parametric Methods for the 2-Period-Cross-Over Design Under Weak Model Assumptions

A nonparametric analysis for the two period cross-over design has first been suggested by Koch (1972) and has been discussed by Hills and Armitage (1979). As known rank tests on sums or differences of the data are applied in this procedure, the results on the one hand are not invariant under monotonous transformations and on the other hand the procedure is only correct for models with additive effects. Therefore, in the present article generalized effects will first be defined in the 2-period cross-over design without the assumption of a linear model and then rank test will be presented which test tese effects without the need of sums or differences of the data. In the appendix the equivalence of the hypothesis for the generalized effects to the known hypotheses for the effects in the linear model will be shown. The application of the procedures will be demonstrated by means of an example in literature.     

Evaluation of Animal Carcinogenicity Studies: Cochran‐Armitage Trend Test vs. Multiple Contrast Tests

Typical animal carcinogenicity studies involve the comparison of several dose groups to a negative control. The uncorrected asymptotic Cochran‐Armitage trend test with equally spaced dose scores is the most frequently used test in such set‐ups. However, this test based on a weighted linear regression on proportions. It is well known that the Cochran‐Armitage test lacks in power for other shapes than the assumed linear one. Therefore, dichotomous multiple contrast tests are introduced. These build the maximum over several single contrasts, where each of them is chosen appropriately to cover a specific dose‐response shape. An extensive power study has been conducted to compare multiple contrast tests with the approaches used so far. Crucial results will be presented in this paper. Moreover, exact tests and continuity corrected versions are introduced and compared to the traditional uncorrected approaches regarding size and power behaviour. A trend test for any shape of the dose‐response relationship for either crude tumour rates or mortality‐ adjusted rates based on the simple Poly‐3 transformation is proposed for evaluation of carcinogenicity studies.     

Nonparametric tests for homogeneity of species assemblages: a data depth approach

Testing homogeneity of species assemblages has important applications in ecology. Due to the unique structure of abundance data often collected in ecological studies, most classical statistical tests cannot be applied directly. In this article, we propose two novel nonparametric tests for comparing species assemblages based on the concept of data depth. They can be considered as a natural generalization of the Kolmogorov-Smirnov and the Cramér-von Mises tests (KS and CM) in this species assemblage comparison context. Our simulation studies show that the proposed test is more powerful than other existing methods under various settings. A real example is used to demonstrate how the proposed method is applied to compare species assemblages using plant community data from a highly diverse tropical forest at Barro Colorado Island, Panama.     

Analyzing time-to-event data in a clinical trial when an unknown proportion of subjects has experienced the event at entry

In some clinical trials, where the outcome is the time until development of a silent event, an unknown proportion of subjects who have already experienced the event will be unknowingly enrolled due to the imperfect nature of the diagnostic tests used to screen potential subjects. For example, commonly used diagnostic tests for evaluating HIV infection status in infants, such as DNA PCR and HIV Culture, have low sensitivity when given soon after infection. This can lead to the inclusion of an unknown proportion of HIV-infected infants into clinical trials aimed at the prevention of transmission from HIV-positive mothers to their infants through breastfeeding. The infection status of infants at the end of the trial, when they are more than a year of age, can be determined with certainty. For those infants found to be infected with HIV at the end of the trial, it cannot be determined whether this occurred during the study or whether they were already infected when they were enrolled. In these settings, estimates of the cumulative risk of the event by the end of the study will overestimate the true probability of event during the study period and hypothesis tests comparing two or more intervention strategies can also be biased. We present inference methods for the distribution of time until the event of interest in these settings, and investigate issues in the design of such trials when there is a choice of using both imperfect and perfect diagnostic tests.     

Biology and host specificity of Aulacobaris fallax (Coleoptera: Curculionidae), a potential biological control agent for dyer’s woad,Isatis tinctoria (Brassicaceae) in North America

Dyer’s woad, Isatis tinctoria, a plant of Eurasian origin is a problematic weed in western North America against which a classical biological weed control programme was initiated in 2004. Three European insect species were selected as candidate agents to control this invasive species, including the root‐mining weevil Aulacobaris fallax. To determine its suitability as an agent, the biology and host specificity of A. fallax were studied in outdoor plots and in the field between 2004 and 2006 in its native European range. Aulacobaris fallax is a univoltine species that lays its eggs from March to August into leaf stalks and roots of dyer’s woad. Larvae mine and pupate in the roots and adults emerge from August to October. Up to 62% of the dyer’s woad plants at the field sites investigated were attacked by this weevil. In no‐choice host‐specificity tests, A. fallax attacked 16 out of 39 species and varieties within the Family Brassicaceae. Twelve of these are native to North America. In subsequent multiple‐choice tests, seven species, all native to North America, suffered a similar level of attack as dyer’s woad, while none of the European species were attacked. Our results demonstrate the importance of including test plant species that have not co‐evolved with the respective candidate agent. In sum, we conclude that the risk of non‐target effects is too high for A. fallax to be considered as a biological control agent for dyer’s woad in the United States.     

Time-dependent Changes in the Host-acceptance Threshold of Insects: Implications for Host Specificity Testing of Candidate Biological Control Agents

Commonly, insects show a progressive decrease in acceptance threshold to sensory cues associated with food or oviposition sites when they are deprived of the opportunity to feed or oviposit. Such changes have been termed time-dependent. In biological control, candidate agents are tested in a variety of cage assays in order to determine the limits of their host range. These assays commonly include choice tests including the target species, simultaneous no-choice tests, and sequential no-choice tests. We consider the possible influence of time-dependent changes in acceptance threshold on the likely outcomes of each of these kinds of tests. Results that may under-estimate the field host range of candidate agents are deemed to be most likely from choice tests that include a highly ranked host, and any no-choice assay conducted over a short period. We recommend: (i) that choice tests including the target species and sequential no-choice tests with a short period of access to non-target species, should not be employed as the only type of test, or for any initial screening; and (ii) that more attention be paid to the temporal profiles of feeding and oviposition by the candidate agent, especially in no-choice tests.     

A Combined Invariant Test for a Null Variance Ratio

This paper investigates the properties of a test that combines the Wald and LMPI (locally most powerful invariant) tests of variance components in unbalanced mixed models. The combined test statistic is easily computed and its null distribution may be approximated by a central F distribution with the degrees of freedom of the numerator adjusted in accordance with the degree of imbalance of the design. Numerical methods are used to show that the approximation is accurate over a wide range of conditions and that the efficiency of the combined test, relative to the power envelope, is satisfactorily high overall.     

Proposal of k Sample Tests for Bivariate Censored Data for Nondecreasing Ordered Alternatives

Tests are introduced which are designed to test for a nondecreasing ordered alternative among the survival functions of k populations consisting of multiple observations on each subject. Some of the observations could be right censored. A simulation study is conducted comparing the proposed tests on the basis of estimated power when the underlying distributions are multivariate normal. Equal sample sizes of 20 with 25% censoring, and 40 with both 25% and 50% censoring are considered for 3 and 4 populations. All of the tests hold their α‐values well. A recommendation is made as to the best overall test for the situations considered.     

Multiple Tests for Different Sets of Variables Using a Data‐Driven Ordering of Hypotheses,with an Application to Gene Expression Data

A multiple parametric test procedure is proposed, which considers tests of means of several variables. The single variables or subsets of variables are ordered according to a data‐dependent criterion and tested in this succession without alpha‐adjustment until the first non‐significant test. The test procedure needs the assumption of a multivariate normal distribution and utilizes the theory of spherical distributions. The basic version is particularly suited for variables with approximately equal variances. As a typical example, the procedure is applied to gene expression data from a commercial array.     

Comparisons of site- and haplotype-frequency methods for detecting positive selection

In this report, we compare the differences between various site- and haplotype-frequency tests in their power to detect positive selection by doing computer simulations. Our results are the following. 1) Although haplotype-frequency tests that are conditional on the number of haplotypes (K) were developed for nonrecombining haplotypes, these tests are insensitive to recombination. Such tests, including the Ewens-Watterson (EW) test, can therefore be applied to recombining haplotypes. 2) Tests conditional on the number of segregating sites (S) become overly conservative in the presence of recombination. 3) The EW test is usually the most powerful test during the sweep phase, especially when the local recombination rate is high. 4) The "extended haplotype homozygosity" test relies heavily on the prior knowledge of the target of selection. With that knowledge, it is the most powerful test, whereas in the absence of this prior information, the test has little power. We also study the sensitivities of the haplotype-frequency tests to background selection and various demographic forces. We find that these tests are sensitive to some forces other than positive selection. To alleviate the problem of low specificity, compound tests, such as the DH test (Zeng et al. 2006), may be a solution. In the companion paper (Zeng K, Shi S, Wu C-I, in preparation), we use the EW test to devise 2 compound tests, which are more powerful in detecting positive selection than DH, but are also relatively insensitive to demography.     

Applying the generalized partitioning principle to control the generalized familywise error rate

In multiple testing, strong control of the familywise error rate (FWER) may be unnecessarily stringent in some situations such as bioinformatic studies. An alternative approach, discussed by Hommel and Hoffmann (1988) and Lehmann and Romano (2005), is to control the generalized familywise error rate (gFWER), the probability of incorrectly rejecting more than m hypotheses. This article presents the generalized Partitioning Principle as a systematic technique of constructing gFWER-controlling tests that can take the joint distribution of test statistics into account. The paper is structured as follows. We first review classical partitioning principle, indicating its conditioning nature. Then the generalized partitioning principle is introduced, with a set of sufficient conditions that allows it to be executed as a computationally more feasible step-down test. Finally, we show the importance of having some knowledge of the distribution of the observations in multiple testing. In particular, we show that step-down permutation tests require an assumption on the joint distribution of the observations in order to control the familywise error rate.     

Meta‐Analysis of Diagnostic Test Accuracy Studies with Multiple Thresholds using Survival Methods

Diagnostic tests play an important role in clinical practice. The objective of a diagnostic test accuracy study is to compare an experimental diagnostic test with a reference standard. The majority of these studies dichotomize test results into two categories: negative and positive. But often the underlying test results may be categorized into more than two, ordered, categories. This article concerns the situation where multiple studies have evaluated the same diagnostic test with the same multiple thresholds in a population of non‐diseased and diseased individuals. Recently, bivariate meta‐analysis has been proposed for the pooling of sensitivity and specificity, which are likely to be negatively correlated within studies. These ideas have been extended to the situation of diagnostic tests with multiple thresholds, leading to a multinomial model with multivariate normal between‐study variation. This approach is efficient, but computer‐intensive and its convergence is highly dependent on starting values. Moreover, monotonicity of the sensitivities/specificities for increasing thresholds is not guaranteed. Here, we propose a Poisson‐correlated gamma frailty model, previously applied to a seemingly quite different situation, meta‐analysis of paired survival curves. Since the approach is based on hazards, it guarantees monotonicity of the sensitivities/specificities for increasing thresholds. The approach is less efficient than the multinomial/normal approach. On the other hand, the Poisson‐correlated gamma frailty model makes no assumptions on the relationship between sensitivity and specificity, gives consistent results, appears to be quite robust against different between‐study variation models, and is computationally very fast and reliable with regard to the overall sensitivities/specificities.     

In vitro analyses are not reliable predictors of the plant growth promotion capability of bacteria; a Pseudomonas fluorescens strain that promotes the growth and yield of wheat

Aims: In this study, we set out to identify bacteria that can be used to promote the growth of cereals, while concurrently investigating the merits of using a range of such tests to preselect bacteria for glasshouse studies. Methods and Results: A panel of 15 strains isolated from the rhizosphere and phyllosphere of cereals was tested for the ability to improve the germination of wheat seeds and for production of a range of factors associated with plant growth promotion. In parallel, all bacteria were tested for their ability to improve biomass and grain yield when applied as a soil amendment in glasshouse trials. Conclusions: There was no significant correlation between growth promotion potential in the glasshouse and the results of either the phenotypic or the germination tests. Glasshouse tests identified that only one strain, Pseudomonas fluorescens strain MKB37, gave a significant increase in head weight and grain yield. Significance and Impact of the Study: While this study has identified a candidate for further field tests, it has also highlighted the fact that the modes of action for plant growth‐promoting bacteria (PGPB) are still not fully understood, and that there is no efficient and effective screening method for identifying PGPB by laboratory tests.     

A multivariate family-based association test using generalized estimating equations: FBAT-GEE

In this paper we propose a multivariate extension of family-based association tests based on generalized estimating equations. The test can be applied to multiple phenotypes and to phenotypic data obtained in longitudinal studies without making any distributional assumptions for the phenotypic observations. Methods for handling missing phenotypic information are discussed. Further, we compare the power of the multivariate test with permutation tests and with using separate tests for each outcome which are adjusted for multiple testing. Application of the proposed test to an asthma study illustrates the power of the approach.     

Developing a statistical support system for environmental hazard evaluation

Estimating the hazard or risk to both human health and the environment has been based almost exclusively on single species toxicity tests low in environmental realism and without validation of their accuracy in more complex systems. While this may be quite appropriate for humans in a large variety of circumstances, there is no substantive body of direct experimental evidence indicating that precise predictions of harm from hazardous materials can be extrapolated from single species laboratory tests (or even multispecies laboratory tests) to the more complex highly variable natural systems. Now added to the hazardous chemical assessment problem is the accidental or deliberate release of genetically engineered microorganisms into the environment that have the additional capability of multiplying and expanding their numbers and also transferring genetic information to other organisms. This paper focuses entirely on hazard evaluation for organisms other than humans, namely predicting the potential risk or probability of harm to natural systems based on laboratory toxicity testing using single species. Not only will the basic risk assessment strategy itself be examined but also the question of determining the statistical reliability of various extrapolations from one level of biological organization to another. ‘For every complex problem, there is a simple, direct solution ... and it is invariably wrong!’ H. L. Mencken     

Does your species have memory? Analyzing capture–recapture data with memory models

We examine memory models for multisite capture–recapture data. This is an important topic, as animals may exhibit behavior that is more complex than simple first‐order Markov movement between sites, when it is necessary to devise and fit appropriate models to data. We consider the Arnason–Schwarz model for multisite capture–recapture data, which incorporates just first‐order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multisite capture–recapture data need to incorporate memory. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al. (JABES, 2009, pp 338–355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multisite memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy and recommend minimum sample sizes. Memory models for multisite capture–recapture data can be highly complex and difficult to fit to data. We emphasize the importance of a structured approach to modeling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multisite capture–recapture data need to incorporate memory.