P-value Method Research Articles

PREDICTING THE OCCURRENCE OF MAJOR ADVERSE CARDIAC EVENTS WITHIN 30 DAYS AFTER A PATIENT'S VASCULAR SURGERY: AN INDIVIDUAL PATIENT-DATA META-ANALYSIS

IntroductionMajor adverse cardiac events (MACE) – which include cardiac death and non-fatal myocardial infarction – are severe harmful outcomes that commonly arise after elective non-cardiac vascular surgeries. Current preoperative risk...

Gene-based Genomewide Association Analysis: A Comparison Study

The study of gene-based genetic associations has gained conceptual popularity recently. Biologic insight into the etiology of a complex disease can be gained by focusing on genes as testing units. Several gene-based methods (e.g., minimum p-value (or maximum test statistic) or entropy-based method) have been developed and have more power than a single nucleotide polymorphism (SNP)-based analysis. The objective of this study is to compare the performance of the entropy-based method with the minimum p-value and single SNP–based analysis and to explore their strengths and weaknesses. Simulation studies show that: 1) all three methods can reasonably control the false-positive rate; 2) the minimum p-value method outperforms the entropy-based and the single SNP–based method when only one disease-related SNP occurs within the gene; 3) the entropy-based method outperforms the other methods when there are more than two disease-related SNPs in the gene; and 4) the entropy-based method is computationally more efficient than the minimum p-value method. Application to a real data set shows that more significant genes were identified by the entropy-based method than by the other two methods.

942 THE RELATIONSHIP BETWEEN LYMPH-NODE DENSITY AND CANCER-SPECIFIC SURVIVAL IN A CONTEMPORARY SERIES OF PATIENTS WITH PENILE CANCER

You have accessJournal of UrologyPenis/Testis/Urethra: Benign & Malignant Disease (III)1 Apr 2013942 THE RELATIONSHIP BETWEEN LYMPH-NODE DENSITY AND CANCER-SPECIFIC SURVIVAL IN A CONTEMPORARY SERIES OF PATIENTS WITH PENILE CANCER Giovanni Lughezzani, Mario Catanzaro, Nicola Nicolai, Tullio Torelli, Luigi Piva, Davide Biasoni, Silvia Stagni, Andrea Necchi, Patrizia Giannatempo, Daniele Raggi, Elena Farè, Maurizio Colecchia, and Roberto Salvioni Giovanni LughezzaniGiovanni Lughezzani Milano, Italy More articles by this author , Mario CatanzaroMario Catanzaro Milano, Italy More articles by this author , Nicola NicolaiNicola Nicolai Milano, Italy More articles by this author , Tullio TorelliTullio Torelli Milano, Italy More articles by this author , Luigi PivaLuigi Piva Milano, Italy More articles by this author , Davide BiasoniDavide Biasoni Milano, Italy More articles by this author , Silvia StagniSilvia Stagni Milano, Italy More articles by this author , Andrea NecchiAndrea Necchi Milano, Italy More articles by this author , Patrizia GiannatempoPatrizia Giannatempo Milano, Italy More articles by this author , Daniele RaggiDaniele Raggi Milano, Italy More articles by this author , Elena FarèElena Farè Milano, Italy More articles by this author , Maurizio ColecchiaMaurizio Colecchia Milano, Italy More articles by this author , and Roberto SalvioniRoberto Salvioni Milano, Italy More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.521AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Lymph node density (LND; ratio of positive lymph nodes to the total number of lymph nodes removed) is an established predictor of survival in several malignancies. In the current study, we evaluated the association between LND and cancer-specific survival (CSS) in a population of patients with penile cancer and pathologically-determined lymph node metastases. METHODS We retrieved data from 81 patients with pathologically-determined LNM who were surgically treated at our Institution between 2000 and 2012. The minimum p-value method according to Mazumdar-Glassman was used to determine the most significant LND cut-off value. LND was considered both as a continuously-coded and as a categorically-coded variable. The Kaplan-Meier method was used to determine CSS rates. Univariable and multivariable Cox regression models were fitted to test the predictors of CSS. RESULTS The median number of positive and removed lymph nodes were 2 (IQR: 1-4) and 22 (IQR: 13-30), respectively. Median LND was 10.3% (IQR: 6.3 -16.6) and the most significant LND cut-off value was 22%. Median follow-up was 26 months (Interquartile range; IQR: 16-62). Overall, 5-year CSS rate was 50.5%. After stratification according to LND, 5-year CSS rates was 65.2 vs. 9.6% in patients with LND < vs. >= 22, respectively (p<0.001). At univariable Cox regression models, both continuously-coded (HR: 1.02, 95% CI: 1.01-1.03; p=0.006) and categorically-coded LND (HR: 4.55; 95% CI: 2.20-9.38; p<0.001) were significant predictors of CSS. At multivariable Cox regression models, after adjusting for several established prognostic factors, categorically-coded LND emerged as the only independent predictor of CSS (HR: 4.28; 95% CI: 1.65-11.08; p=0.003). CONCLUSIONS Lymph node density is a powerful predictor of CSS in patients with penile cancer and pathologically-determined LNM. Although further investigations are needed to evaluate the relationship between tumor burden and treatment intensity, LND may be of immediate use in clinical practice to better stratify the prognosis of these patients. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e387 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information Giovanni Lughezzani Milano, Italy More articles by this author Mario Catanzaro Milano, Italy More articles by this author Nicola Nicolai Milano, Italy More articles by this author Tullio Torelli Milano, Italy More articles by this author Luigi Piva Milano, Italy More articles by this author Davide Biasoni Milano, Italy More articles by this author Silvia Stagni Milano, Italy More articles by this author Andrea Necchi Milano, Italy More articles by this author Patrizia Giannatempo Milano, Italy More articles by this author Daniele Raggi Milano, Italy More articles by this author Elena Farè Milano, Italy More articles by this author Maurizio Colecchia Milano, Italy More articles by this author Roberto Salvioni Milano, Italy More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Comparison of Model Selection Method Using Data from Classical Designed Experiment

Model selection procedures play important role in many researches especially quantitative research. . In several area of sciences, the analysis and model selection of experiments are often used and often contains two fundamental goals associated with the experimental response of interest which are to determine the best model. The way to address these goals is to implement a model selection procedure. Then, the objectives of this research are to determine whether or not the final models selected are in agreement or differ substantially across the three approaches to model selection: using Akaike’s Information Criterion, using a p-value criterion, and using a stepwise procedure.. Generally, results from these three models are usually compare to each other. All selected models are based on the heredity principle to design the possible model for each design. The actual data from literature, consisting of the 2x3 and 32 and 3x4 factorial designs are used to determine the final model. The results show that the P-Value WH and Stepwise methods give the highest percentage of matched model.

A COMPARISON OF BROAD IRON EMISSION LINES IN ARCHIVAL DATA OF NEUTRON STAR LOW-MASS X-RAY BINARIES

Relativistic X-ray disklines have been found in multiple neutron star low-mass X-ray binaries, in close analogy with black holes across the mass scale. These lines have tremendous diagnostic power and have been used to constrain stellar radii and magnetic fields, often finding values that are consistent with independent timing techniques. Here, we compare CCD-based data from Suzaku with Fe K line profiles from archival data taken with gas-based spectrometers. In general, we find good consistency between the gas-based line profiles from EXOSAT, BeppoSAX, and RXTE and the CCD data from Suzaku, demonstrating that the broad profiles seen are intrinsic to the line and not broad due to instrumental issues. However, we do find that when fitting with a Gaussian line profile, the width of the Gaussian can depend on the continuum model in instruments with low spectral resolution, though when the different models fit equally well the line widths generally agree. We also demonstrate that three BeppoSAX observations show evidence for asymmetric lines, with a relativistic diskline model providing a significantly better fit than a Gaussian. We test this by using the posterior predictive p-value method, and bootstrapping of the spectra to show that such deviations from a Gaussian are unlikely to be observed by chance.

A simple test for homogeneity of Hardy–Weinberg equilibrium and disequilibrium across strata

For an SNP (single-nucleotide polymorphism) sampled from several ethnic populations with different allele frequencies, verifying the homogeneity of Hardy–Weinberg equilibrium or disequilibrium across strata is a critical factor in measuring the quality of the SNP. We obtain a simple unified method that can be applied directly to test the homogeneity of Hardy–Weinberg equilibrium or disequilibrium across strata. The deviation of each ethnic population from homogeneity is measured via a new exact test that depends on a common homogeneity coefficient. Tippett’s minimum p-value method is then used to test for homogeneity across strata.

Influence of tightening procedures and lubrication conditions on titanium screw joints for lightweight applications

In recent years, the need for lighter vehicles led to the widespread of lightweight alloys, such as titanium, also in the field of threaded fasteners. Unluckily, the replacement of steel bolts with titanium ones, usually suggested because of their favourable ratio between strength and weight, is not quite straightforward. The coefficient of friction, a key parameter in bolted joints design, changes drastically when switching from steel to titanium. Some results concerning the frictional behaviour of bolted joints involving titanium screws are here presented: friction and torque coefficients were calculated, according to ISO 16047, for joints made up of a hexagon socket head screw made of titanium alloy (Ti–6Al–4V), a bush made of aluminium alloy (EN AW 7075 T6) and a steel nut (ISO 4032). Data were collected by performing several tightening tests on ad-hoc designed specimens, which allowed the evaluation of the different tribological behaviour of the same joint under three different conditions of lubrication (dry, Teflon® added oil, and ceramic paste). 20 repeated tightenings (re-tightenings) have been analysed in order to simulate some maintenance operations. The DOE method was applied to manage the tests, while the results were analysed by the ANOVA and P-value methods. Out of the two lubricants examined, the ceramic paste showed the best results in terms of friction coefficients constancy throughout the re-tightening operations, as well as the best protection of the thread and underhead surfaces against wear.

Optimization of Adaptive Designs: Efficiency Evaluation

The rising cost of clinical trials is impeding the development of new drugs. There is an acute need for critical evaluation and innovate thinking while designing the trial. Adaptive design has been repeatedly called upon in the last decade as one of the prescriptions for this intricate problem. From a pure statistical perspective, the adaptive design framework depends heavily on the appropriate selection of the type of test statistics and stopping boundaries. There are several methods proposed in the literature, based on different test statistics and stopping boundaries. All of these methods are rigorous in controlling type I error. In this paper, we group combination p-value methods into major categories along with their stopping boundaries. We review and compare these methods based on their operating characteristics, including average sample size and maximum sample size under null and alternative hypothesis, power, and early stopping probabilities. The optimal interim analysis timing and alpha spending function were used as the independent factors for this assessment. We propose an evaluation matrix and establish a framework to assess the most efficient design in order to assist in “one stop shopping.”

Meta-Analysis of Clinical Efficacy of Pulsed Radio Frequency Energy Treatment

To statistically evaluate published clinical efficacy data related to the use of pulsed radio frequency energy (PRFE) therapy in 3 clinical applications. Numerous clinical studies have reported efficacy outcomes for PRFE therapy use in the palliative treatment of both postoperative and nonpostoperative pain and edema, and for its use as an adjunctive wound-healing (WH) therapeutic. Although diverse in design and endpoint, these studies are amenable to systematic review using both a vote-counting and P-value combination meta-analytic technique. A meta-analysis of efficacy outcomes reported in clinical trials was performed using a vote-counting procedure. In addition, when possible, the sum of logs method of P-value combination was used to determine a significance level for the combined evidence within each endpoint and clinical area. Of the 186 clinical articles identified after application of selection criteria, there were 25 controlled trials that met criteria for inclusion in vote-counting and P-value combination methods and were used for formal statistical analysis. In total, 1332 patients receiving PRFE treatment were studied. Vote-counting procedure applied to clinical outcomes from controlled studies resulted in a greater number of positive outcomes than neutral outcomes, and zero negative outcomes, for each of the clinical application groups evaluated. The sum of logs P-value method found statistically significant improvement in pain, reduction in edema, and improvement in WH outcomes. On the basis of statistical evaluation of published clinical efficacy data, there is strong statistical evidence that PRFE therapy is effective in the treatment of postoperative and nonpostoperative pain and edema and in WH applications.

Comprehensive Gene-Based Association Study of a Chromosome 20 Linked Region Implicates Novel Risk Loci for Depressive Symptoms in Psychotic Illness

BackgroundPrior genomewide scans of schizophrenia support evidence of linkage to regions of chromosome 20. However, association analyses have yet to provide support for any etiologically relevant variants.MethodsWe analyzed 2988 LD-tagging single nucleotide polymorphisms (SNPs) in 327 genes on chromosome 20, to test for association with schizophrenia in 270 Irish high-density families (ISHDSF, N = 270 families, 1408 subjects). These SNPs were genotyped using an Illumina iSelect genotyping array which employs the Infinium assay. Given a previous report of novel linkage with chromosome 20p using latent classes of psychotic illness in this sample, association analysis was also conducted for each of five factor-derived scores based on the Operational Criteria Checklist for Psychotic Illness (delusions, hallucinations, mania, depression, and negative symptoms). Tests of association were conducted using the PDTPHASE and QPDTPHASE packages of UNPHASED. Empirical estimates of gene-wise significance were obtained by adaptive permutation of a) the smallest observed P-value and b) the threshold-truncated product of P-values for each locus.ResultsWhile no single variant was significant after LD-corrected Bonferroni-correction, our gene-dropping analyses identified loci which exceeded empirical significance criteria for both gene-based tests. Namely, R3HDML and C20orf39 are significantly associated with depressive symptoms of schizophrenia (P emp<2×10−5) based on the minimum P-value and truncated-product methods, respectively.ConclusionsUsing a gene-based approach to family-based association, R3HDML and C20orf39 were found to be significantly associated with clinical dimensions of schizophrenia. These findings demonstrate the efficacy of gene-based analysis and support previous evidence that chromosome 20 may harbor schizophrenia susceptibility or modifier loci.

Region-based and pathway-based QTL mapping using a p-value combination method

Quantitative trait locus (QTL) mapping using deep DNA sequencing data is a challenging task. In this study we performed region-based and pathway-based QTL mappings using a p-value combination method to analyze the simulated quantitative traits Q1 and Q4 and the exome sequencing data. The aims were to evaluate the performance of the QTL mapping approaches that were used and to suggest plausible strategies for QTL mapping of DNA sequencing data. We conducted single-locus QTL mappings using a linear regression model with adjustments for age and smoking status, and we also conducted region-based and pathway-based QTL mappings using a truncated product method for combining p-values from the single-locus QTL mapping. To account for the features of rare variants and common single-nucleotide polymorphisms (SNPs), we considered independently rare-variant-only, common-SNP-only, and combined analyses. An analysis of 200 simulated replications showed that the three region-based methods reasonably controlled type I error, whereas the combined analysis yielded the greatest statistical power. Rare-variant-only, common-SNP-only, and combined analyses were also applied to pathway-based QTL mappings. We found that pathway-based QTL mappings had a power of approximately 100% when the significance of the vascular endothelial growth factor pathway was evaluated, but type I errors were slightly inflated. Our approach complements single-locus QTL mapping. An integrated approach using single-locus, combined region-based, and combined pathway-based analyses should yield promising results for QTL mapping of DNA sequencing data.

Gene-based Higher Criticism methods for large-scale exonic single-nucleotide polymorphism data

In genome-wide association studies, gene-based methods measure potential joint genetic effects of loci within genes and are promising for detecting causative genetic variations. Following recent theoretical research in statistical multiple-hypothesis testing, we propose to adapt the Higher Criticism procedures to develop novel gene-based methods that use the information of linkage disequilibrium for detecting weak and sparse genetic signals. With the large-scale exonic single-nucleotide polymorphism data from Genetic Analysis Workshop 17, we show that the new Higher-Criticism-type gene-based methods have higher statistical power to detect causative genes than the minimal P-value method, ridge regression, and the prototypes of Higher Criticism do.

A new generalized p-value approach for testing equality of coefficients of variation in k normal populations

A new generalized p-value method is proposed for testing the equality of coefficients of variation in k normal populations. Simulation studies show that the type I error probabilities are close to the nominal level. The proposed test is also compared with likelihood ratio test, modified Bennett's test and score test through Monte Carlo simulation, the results demonstrate that the generalized p-value method has satisfactory performance in terms of sizes and powers.

Inferences on the lifetime performance index for Weibull distribution based on censored observations using the max p-value method

In the service (or manufacturing) industries, process capability indices (PCIs) are utilised to assess whether product quality meets the required level. And the lifetime performance index (or larger-the-better PCI) CL is frequently used as a means of measuring product performance, where L is the lower specification limit. Hence, this study first uses the max p-value method to select the optimum value of the shape parameter β of the Weibull distribution and β is given. Second, we also construct the maximum likelihood estimator (MLE) of CL based on the type II right‐censored sample from the Weibull distribution. The MLE of CL is then utilised to develop a novel hypothesis testing procedure provided that L is known. Finally, we give one practical example to illustrate the use of the testing procedure under given significance level α.

Multimodality evaluation of excision repair cross-complementation 1 (ERCC1) expression as a predictive biomarker for platinum resistance in epithelial ovarian cancer (EOC).

5055 Background: Pre-chemotherapy identification of platinum-resistant EOC would facilitate use of alternative treatment strategies. Small studies have proposed ERCC1 as a platinum resistance marker. Here we evaluate the utility of quantitative ERCC1 mRNA and protein expression as biomarkers in a large cohort. Methods: Tissue microarrays (TMAs) were constructed in triplicate from 470 advanced EOC patients treated with first-line platinum-based chemotherapy and with prospectively collected clinical and survival data. Following ERCC1 antibody validation (FL297 & SC17809 clones), immunohistochemistry (IHC) was performed. The intensity and frequency of positive staining were evaluated by both automated scoring and a human observer blinded to the case details. Quantitative analysis of protein and gene expression were also assessed using automated quantitative analysis (AQUA) of the TMAs and qRT-PCR from mRNA extracted from the same tissue specimens. Expression by each assay was correlated with survival using a minimum p-value method with subgroup analyses taking into account histology, grade, stage and chemotherapy. Results: 457 samples were suitable for IHC/AQUA and 354 for mRNA. There was correlation between IHC results and AQUA (Spearman R value=0.33; p<0.0001), between IHC and mRNA (R=0.16; p=0.002) and between AQUA and mRNA (R=0.12, p=0.03). The median ERCC1 histoscore for patients with platinum-resistant disease (n=171) was 172 (maximum = 300; IQR 154-190), for platinum-sensitive EOC (n=286) was 177 (ns; IQR 160-194) and platinum-refractory disease subset (n=97) 169. No statistically significant difference was identified, either for PFS or OS, for any subgroup analyzed. No trend towards shorter survival in those with higher ERCC1 expression was seen using any assay. Conclusions: Several small studies have suggested that ERCC1 expression, quantified by either IHC or qRT-PCR, can predict platinum resistance. In this, by far the largest cohort, and using quantitative methodologies, we were unable to demonstrate any association with clinical outcome. Using these assays, ERCC1 as a single gene biomarker is not useful in EOC.

Off-Line Detection of Multiple Change Points by the Filtered Derivative with p-Value Method

This article deals with off-line detection of change points, for time series of independent observations, when the number of change points is unknown. We propose a sequential analysis method with linear time and memory complexity. Our method is based, on a filtered derivative method that detects the right change points as well as false ones. We improve the filtered derivative method by adding a second step in which we compute the p-values associated to every single potential change point. Then, we eliminate false alarms; that is, the change points that have p-values smaller than a given critical level. Next, we apply our method and penalized least squares criterion procedure to detect change points on simulated data sets and then we compare them. Eventually, we apply the filtered derivative with p-value method to the segmentation of heartbeat time series, and the detection of change points in the average daily volume of financial time series.

Identification of Differentially Expressed Genes for Time-Course Microarray Data Based on Modified RM ANOVA

The regulation of gene expression is a dynamic process, hence it is of vital interest to identify and characterize changes in gene expression over time. We present here a general statistical method for detecting changes in microarray expression over time within a single biological group and is based on repeated measures (RM) ANOVA. In this method, unlike the classical F-statistic, statistical significance is determined taking into account the time dependency of the microarray data. A correction factor for this RM F-statistic is introduced leading to a higher sensitivity as well as high specificity. We investigate the two approaches that exist in the literature for calculating the p-values using resampling techniques of gene-wise p-values and pooled p-values. It is shown that the pooled p-values method compared to the method of the gene-wise p-values is more powerful, and computationally less expensive, and hence is applied along with the introduced correction factor to various synthetic data sets and a real data set. These results show that the proposed technique outperforms the current methods. The real data set results are consistent with the existing knowledge concerning the presence of the genes. The algorithms presented are implemented in R and are freely available upon request.

Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments

In time-course experiments, it is often desirable to identify genes that exhibit a specific pattern of differential expression over time and thus gain insights into the mechanisms of the underlying biological processes. Two challenging issues in the pattern identification problem are: (i) how to combine the simultaneous inferences across multiple time points and (ii) how to control the multiplicity while accounting for the strong dependence. We formulate a compound decision-theoretic framework for set-wise multiple testing and propose a data-driven procedure that aims to minimize the missed set rate subject to a constraint on the false set rate. The hidden Markov model proposed in Yuan and Kendziorski (2006) is generalized to capture the temporal correlation in the gene expression data. Both theoretical and numerical results are presented to show that our data-driven procedure controls the multiplicity, provides an optimal way of combining simultaneous inferences across multiple time points, and greatly improves the conventional combined p-value methods. In particular, we demonstrate our method in an application to a study of systemic inflammation in humans for detecting early and late response genes.

Detecting association with rare variants for common diseases using haplotype-based methods

Current Genome-Wide Association Studies (GWAS) have successfully detected many genetic variants contributing to common diseases but not rare ones. Here two haplotypebased methods are proposed for detecting rare variants contributing a common disease. One method is a haplotypebased truncated product method (HTPM), for which we borrow a p-value combination method from testing for the multiple hypotheses, but use it for the purpose of clustering the information on rare risk haplotypes. The other method is the combined method, for which a set of risk haplotypes are chosen based on haplotype frequency comparison between cases and controls, and then testing for association using the same sample. Our simulation study demonstrates that both methods have improved power for detecting the association between rare variants and diseases, compared with other available methods. Both methods are applied to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease and hypertension data and replicated the previous findings of genes associated with hypertension and coronary artery disease respectively at a genome-wide significance level of 5%. These results suggest that haplotypebased methods are powerful methods in searching for rare genetic variants and can be applicable to the data from current GWAS.

Simultaneous set-wise testing under dependence, with applications to genome-wide association studies

We consider the problem of identifying diseaseassociated genomic regions in genome-wide association studies (GWAS). It is shown that conventional single SNP analysis can be greatly improved by (i) exploiting the spatial dependency and (ii) conducing set-wise analysis. The SNP set association problem can be conceptualized as the problem of simultaneously testing a large number of sets of hypotheses. We use hidden Markov models to exploit the linkage disequilibrium information in GWAS data, based on which a data-driven screening procedure (GLIS) is proposed. GLIS is shown to be optimal in the sense that it has the smallest missed set rate (MSR) among all valid false set rate (FSR) procedures. The numerical results demonstrate that the proposed procedure controls the FSR at the desired level, enjoys certain optimality properties and outperforms conventional combined p-value methods. We apply the GLIS procedure to analyze a Type 1 diabetes (T1D) GWAS dataset for detecting T1D associated genomic regions. The results show that our proposed SNP set analysis not only provides better biological insights, but also increases the statistical power by pooling information from different samples.