A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests

A recent article in this journal by Tabak (2006) highlighted a potentially serious source of bias that can arise when multiple statistical tests of a damages theory are performed and even one test rejecting the null hypothesis is regarded as supporting the damages theory. Repeated testing will eventually produce a “false discovery,” that is, rejection of the null hypothesis in favor of the alternative hypothesis when the null hypothesis is true, which statisticians refer to as type I error. Consequently, performing multiple tests without adjusting the critical value can be problematical because it can lead to improperly accepting statistical evidence that apparently supports rejection of the null hypothesis as reliable when it is not. Tabak (2006) recommends making the Sidak (1968, 1971) multiple-comparison adjustment to the standard statistical t-test to correct for the false-discovery bias inherent in multiple-comparison testing. In particular, he recommends making this adjustment when performing 10b-5 securities fraud event studies when more than one corrective disclosure date is involved. This article clarifies the circumstances in which a multiple-comparison adjustment is appropriate and explains why the correction is normally not needed in securities fraud event-study testing. More generally, I explain why it is not required when each of several tests is performed and its results are reported separately, as for example, where the objective is simply to test the statistical significance of the abnormal stock return on each day on which a new and distinct curative disclosure occurred. I show that the Sidak multiple-comparison adjustment is nearly as stringent as the classical Bonferroni procedure (Simes, 1986), which can increase the risk of type II error. This article discusses a more powerful alternative to the Sidak adjustment due to Benjamini and Hochberg (1993), which directly corrects for the false-discovery bias in multiple-comparison testing and reduces the risk of type II error. II. Application of Multiple-Comparison Adjustments to Securities Fraud Event Studies Multiple-comparison-false-discovery bias can arise when (a) several statistical tests are performed on subsets of the same larger data set in an effort to

In this paper, a new combination method for sound field prediction is proposed. An optimization approach based on the genetic algorithm is employed for optimizing the transition frequency of the combined sound field prediction method in classrooms. The selected optimization approach can identify the optimal transition frequency so that the combined sound field prediction can obtain more efficient and accurate prediction results. The proposed combined sound field prediction method consists of a wave-based method and geometric acoustic methods that are separated by the transition frequency. In low frequency domain (below the transition frequency), the sound field is calculated by the finite element method (FEM), while a hybrid geometric acoustic method is employed in the high frequency domain (above the transition frequency). The proposed combined prediction models are validated by comparing them with previous results and experimental measurements. The optimization approach is illustrated by several examples and compared with traditional combination results. Compared to existed sound field prediction simulations in classrooms, the proposed combination methods take the sound field in low frequencies into account. The results demonstrate the effectiveness of the proposed model. Practical applications: This study proposes a combined sound field prediction method separated by transition frequency. A genetic algorithm optimization method is employed for searching the optimal transition frequency. The outcomes of this paper are essential for acoustical designs and acoustical environmental assessments.

Gene-gene interaction plays an important role in association studies for complex diseases. There have been different approaches to incorporating gene-gene interactions in candidate gene or genome-wide association studies, especially for those genes with no marginal effects but with interaction effects. However, there is no general agreement on how interaction should be tested and how main effects and interaction effects act on a significance signal. In this paper, we propose a test of the null hypothesis of no association in terms of interaction effects for two unlinked loci, which is a 4 degrees of freedom (df) chi-square for two SNPs. The test, derived by contrasting inter-locus disequilibrium measures between cases and controls, can be viewed as the interaction component of the total Pearson chi-square. The remaining portion of the total chi-square can also be used for association analysis, which emphasizes main effects. Simulation studies show that in most situations our interaction test is similar in power to the test based on a logistic regression model but has more power when the genes have no marginal effects. Results also show that single-locus marginal tests can lose much power if interaction effects dominate main effects. For some specific genetic models, the interaction test may be further partitioned into four 1-df chi-squares for individual interaction effect. The interaction pattern can best be demonstrated by the 1-df chi-square components. Simulation results show that there is substantial power gain if interaction patterns are properly incorporated in association analysis.

One of the most important parts of all applications trying to discriminate between a person's different mental tasks using their recorded EEG data is the process of feature construction. A common practice for this is to exploit an apriori knowledge about the nature of the mental processes of interest and their impact on the EEG signals. However, the use of features constructed in this way is restricted to applications concerning the corresponding mental processes. We present here a novel method for EEG data classification which is very general as it makes no assumptions about the nature of the EEG signals. It is based on the construction of a characteristic signal for each class which remains as invariant as possible over the trials belonging to that class. We use the proposed method in combination with a novel method for channel selection in an oddball experiment to predict a person's quick or late response.

Using color information in object tracking is a prudent choice, but the vast variety of choices and difficulties of obtaining a desirable stable result, unnerves many scholars. Color histograms, as a compact and robust representation is the center of attention while it suffers from lack of spatial information about colors. Besides, comparison and updating such histograms in a meaningful and efficient manner is challenging. In this paper, we proposed the idea of gridding for color histogram, which grants specific statistical property to the histogram through a decomposition phase followed by a recombination stage. Additionally, a thorough comparison of the modern similarity functions and model update techniques in RGB colorspace is presented. This comparison reveals that our proposed method in combination with established similarity measures, enhances the tracking performance.

Наведено опис методу TOPSIS, призначеного для ранжування альтернатив, та результати його аналізу. Показано, що відомому методу TOPSIS притаманний недолік, пов’язаний із впливом ефекту компенсації на результати ранжування альтернатив. Зазначено, що в методах VIKOR та MOORA для зменшення впливу цього ефекту використовується додатковий показник. Відповідно комбінація методу TOPSIS та підходу, використаного у методах VIKOR та MOORA, дозволяє запропонувати новий метод – метод трикритеріального евклідового ранжування, який забезпечить підвищення обґрунтованості результатів за рахунок підвищення повноти порівняння альтернатив та зменшення впливу ефекту компенсації на них. Для цього нового методу наведені формульні залежності, а також надано поетапний опис порядку проведення розрахунків. Можливість практичного застосування запропонованого методу продемонстрована на розрахунковому прикладі, для якого у літературних джерелах наводяться результати, отримані за відомими багатокритеріальними методами. Порівняння результатів розрахунків за відомими та новим методами свідчить, що використання запропонованого методу в комбінації з іншими методами дає можливість підвищити обґрунтованість розроблюваних рекомендації, а також визначити перспективні альтернативи, які залишились поза увагою інших методів. Ці результати дозволяють зробити висновок, що використання розробленого методу трикритеріального евклідового ранжування може бути достатньо перспективним під час багатокритеріальної оптимізації складних об’єктів.

Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latentfingerprint reconstruction model based on conditional generative adversarial networks $(cGANs)$.Two modifications are applied to the cGANto adapt itfor the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual IDpreservation approach is developed to force the generator to preserve the IDinformation during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. Weachieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

BackgroundPower for assessing interactions during data analysis is often poor in epidemiologic studies. This is because epidemiologic studies are frequently powered primarily to assess main effects only. In light of this, some investigators raise the Type I error rate, thereby increasing power, when testing interactions. However, this is a poor analysis strategy if the study is chronically under-powered (e.g. in a small study) or already adequately powered (e.g. in a very large study). To demonstrate this point, this study quantified the gain in power for testing interactions when the Type I error rate is raised, for a variety of study sizes and types of interaction.MethodsPower was computed for the Wald test for interaction, the likelihood ratio test for interaction, and the Breslow-Day test for heterogeneity of the odds ratio. Ten types of interaction, ranging from sub-additive through to super-multiplicative, were investigated in the simple scenario of two binary risk factors. Case-control studies of various sizes were investigated (75 cases & 150 controls, 300 cases & 600 controls, and 1200 cases & 2400 controls).ResultsThe strategy of raising the Type I error rate from 5% to 20% resulted in a useful power gain (a gain of at least 10%, resulting in power of at least 70%) in only 7 of the 27 interaction type/study size scenarios studied (26%). In the other 20 scenarios, power was either already adequate (n = 8; 30%), or else so low that it was still weak (below 70%) even after raising the Type I error rate to 20% (n = 12; 44%).ConclusionRelaxing the Type I error rate did not usefully improve the power for tests of interaction in many of the scenarios studied. In many studies, the small power gains obtained by raising the Type I error will be more than offset by the disadvantage of increased "false positives". I recommend investigators should not routinely raise the Type I error rate when assessing tests of interaction.

It is a typical feature of high dimensional data analysis, for example a microarray study, that a researcher allows thousands of statistical tests at a time. All inferences for the tests are determined using the p-values; a smaller p-value than the α-level of the test signifies a statistically significant test. As the number of tests increases, the chance of observing some small p-values is very high even when all null hypotheses are true. Consequently, we make wrong conclusions on the hypotheses. This type of potential problem frequently happens when we test several hypotheses simultaneously, i.e., the multiple testing problem. Adjustment of the p-values can redress the problem that arises in multiple hypothesis testing. P-value adjustment methods control error rates [type I error (i.e. false positive) and type II error (i.e. false negative)] for each hypothesis in order to achieve high statistical power while keeping the overall Family Wise Error Rate (FWER) no larger than α, where α is most often set to 0.05. However, researchers also consider the False Discovery Rate (FDR), or Positive False Discovery Rate (pFDR) instead of the type I error in multiple comparison problems for microarray studies. The methods involved in controlling the FDR always provide higher statistical power than the methods involved in controlling the type I error rate while keeping the type II error rate low. In practice, microarray studies involve dependent test statistics (or p-values) because genes can be fully dependent on each other in a complicated biological structure. However, some of the p-value adjustment methods only deal with independent test statistics. Thus, we carry out a simulation study with several methods involved in multiple hypothesis testing.

Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to insufficient spatial resolution and other phenomena. A challenging problem is how to automatically identify endmembers, as the presence of mixed pixels generally prevents the localization of pure spectral signatures in transition areas between different land-cover classes. A possible strategy to address this problem is to guide the endmember identification process to spatially homogeneous areas, expected to contain the purest signatures available in the scene. For this purpose, several spatial preprocessing methods have been used prior to endmember identification. However, the preprocessing methods developed thus far only exploit the spatial information and relegate the use of spectral information to the subsequent endmember identification stage. In this paper, we develop a new spatial-spectral preprocessing (SSPP) module which can be used prior to endmember identification and spectral unmixing. The method first derives a spatial homogeneity index for each pixel in the hyperspectral image. This index is relatively insensitive to the noise present in the data. At the same time, it performs unsupervised clustering to identify a set of clusters in spectral space. Finally, it fuses spatial and spectral information by selecting a subset of spatially homogeneous and spectrally pure pixels from each cluster. These pixels constitute the new set of candidates for endmember identification. An innovative contribution of this paper is the combination of spatial and spectral information at the preprocessing stage. Another contribution is the combination, for the first time in the literature, of preprocessing techniques with endmember identification algorithms that do not assume the presence of pure signatures in the scene. An experimental comparison of the proposed method in combination with different endmember identification techniques is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that endmember identification techniques (with and without the pure signature assumption) can greatly benefit from the proposed preprocessing approach, which considers both spatial and spectral information.

Early causal heterogeneity but developmental homogeneity before schizophrenia. A prospective study of early life in 15000 people from two general population birth cohorts

Examining a huge amount of data is a typical issue in any research process. However, different statistical processes and techniques play essential role to derive a meaningful conclusion from the presented enormous data. Control of type I error is highly essential for a researcher or statistician while dealing with comparisons test with more than two variables. Multiple testing statistical tests provides a structural system and minimizes the error rate by helping to derive meaningful accurate conclusions. Among the different multiple test procedures Tukey's honestly significant difference test (Tukey's HSD) is most common and popular techniques. The main objective of this study was to explore how significantly selection of confidence level or error rate can affect the rate of committing type I error while drawing conclusion. The effect of committing type I error with selection of confidence level or error rate was explored with citing suitable case study in a special education setting. The case study focuses cognitive effect of music on growth and enhancement of the motor behavior (running, jumping and sliding) of children with mild intellectual disability enrolled in the special school setting. ANOVA test was performed and the significance of selection of individual confidence level and simultaneous confidence level) in Tukey’s HSD test was described.

BackgroundIn microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.ResultsThis study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.ConclusionsMicroarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.

Multiple testing issues have long been considered almost exclusively in the context of General Linear Model, in which usually the significance of a quite limited number of contrasts is tested simultaneously. Most of the procedures used in this context have been designed to control the so-called Family-Wise Error Rate (FWER), defined as the probability of more than one erroneous rejection of a null hypothesis. In the last two decades, large-scale significance tests encountered for example in microarray data analysis have renewed the methodology on multiple testing by introducing novel definitions of Type-I error rates, such as the False Discovery Rate (FDR), to define less conservative procedures. High dimension has also highlighted the need for improvements, to guarantee the control of the error rates in various situations of dependent data. The present article gives motivations for a factor analysis modeling of the covariance between test statistics, both in the situation of simultaneous tests of a small set of contrasts in the General Linear Model and also in high-dimensional significance tests. Impact of the dependence on the power of multiple testing is first discussed and a new procedure controlling the FWER and based on factor-adjusted test statistics is presented as a solution to improve the Type-II error rate with respect to existing methods. Finally, the beneficial impact of the new method is shown on simulated datasets.

e16508 Background: Circulating tumor DNA (ctDNA) are short DNA sequences shed by tumor cells into systemic circulation. The sensitivity and specificity of single ctDNA test to predict relapse or recurrence in solid tumor vary widely and the utility of monitoring serial ctDNA trends has not been well-described in genitourinary cancers. Methods: We conducted a retrospective study including adult patients with genitourinary cancers at the hematology-oncology clinic at William Beaumont - Royal Oak and Troy Hospitals, Michigan from, August 2021 to May 2022, who had ctDNA testing done with imaging available for comparison. We evaluated the correlation between ctDNA test, both single test as well as dynamic trends in value over time, with imaging findings. We also calculated lead time for uptrending ctDNA to detect disease progression compared to imaging. Results: 21 total ctDNA test results from eight patient were included in the study; five had renal cell cancer, two had urothelial cancer and one had prostate cancer. The median age at diagnosis was 62 years (Range: 45-82); 90% were Caucasian and 50% were female. Majority (87.5%) had Stage III or IV disease. Out of the 21 total ctDNA samples, all 16 positive ctDNA results had evidence of disease on corresponding imaging: 13 showed progression while 3 showed regression with residual disease compared to prior imaging. Of the 5 negative ctDNA test results, 2 showed no evidence of disease and 3 showed regression with residual disease on corresponding imaging. Sensitivity and specificity of a single positive ctDNA test to detect evidence of disease on imaging was 84.2% and 100%, respectively. Six patients had multiple ctDNA test results with 12 pairs of serial ctDNA values, of which 9 were up-trending, and 3 were down-trending. Both sensitivity and specificity of up-trending ctDNA values to detect progression and down-trending ctDNA values to detect regression on imaging were 100%. Uptrending ctDNA values predicted disease progression with a median lead time of 55.5 days compared to imaging. Conclusions: Given the high sensitivity and specificity of serial ctDNA monitoring to detect disease progression and regression in our study, we conclude that this may be a valid way to reliably monitor for changes in disease status in genitourinary cancers before they become evident on imaging studies. Further clinical studies are needed to prove its utility in detecting immediate changes in disease status and guide therapeutic intervention in genitourinary cancers. [Table: see text] [Table: see text]