scholarly journals Probabilistic thresholding of functional connectomes: application to schizophrenia

2017 ◽  
Author(s):  
František Váša ◽  
Edward T. Bullmore ◽  
Ameera X. Patel
Keyword(s):  
Type I Error ◽  
Resting State Fmri ◽  
Connected Components ◽  
Edge Density ◽  
P Value ◽  
Type I ◽  
Healthy Controls ◽  
True Positive ◽  
Sparse Graphs ◽  
Cross Correlations

AbstractFunctional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density. The latter (more widely used) method risks inclusion of false positive edges at high edge densities and exclusion of true positive edges at low edge densities. Here we apply new wavelet-based methods, which enable construction of probabilistically-thresholded graphs controlled for type I error, to a dataset of resting-state fMRI scans of 56 patients with schizophrenia and 71 healthy controls. By thresholding connectomes to fixed edge-specific P value, we found that functional connectomes of patients with schizophrenia were more dysconnected than those of healthy controls, exhibiting a lower edge density and a higher number of (dis)connected components. Furthermore, many participants’ connectomes could not be built up to the fixed edge densities commonly studied in the literature (~5-30%), while controlling for type I error. Additionally, we showed that the topological randomisation previously reported in the schizophrenia literature is likely attributable to “non-significant” edges added when thresholding connectomes to fixed density based on correlation. Finally, by explicitly comparing connectomes thresholded by increasing P value and decreasing correlation, we showed that probabilistically thresholded connectomes show decreased randomness and increased consistency across participants. Our results have implications for future analysis of functional connectivity using graph theory, especially within datasets exhibiting heterogenous distributions of edge weights (correlations), between groups or across participants.

scholarly journals A novel gene-set association test based on variance-gamma distribution

2018 ◽  
Vol 28 (9) ◽  
pp. 2868-2875
Author(s):  
Zhongxue Chen ◽  
Qingzhong Liu ◽  
Kai Wang
Keyword(s):  
Gamma Distribution ◽  
Type I Error ◽  
Null Distribution ◽  
Real Data ◽  
Association Test ◽  
P Value ◽  
Type I ◽  
Test Statistic ◽  
Data Set ◽  
Variance Gamma

Several gene- or set-based association tests have been proposed recently in the literature. Powerful statistical approaches are still highly desirable in this area. In this paper we propose a novel statistical association test, which uses information of the burden component and its complement from the genotypes. This new test statistic has a simple null distribution, which is a special and simplified variance-gamma distribution, and its p-value can be easily calculated. Through a comprehensive simulation study, we show that the new test can control type I error rate and has superior detecting power compared with some popular existing methods. We also apply the new approach to a real data set; the results demonstrate that this test is promising.

When Studies are in Error: Basic Statistical Vocabulary Needed to Understand Clinical Studies

1996 ◽  
Vol 1 (1) ◽  
pp. 25-28 ◽  
Author(s):  
Martin A. Weinstock
Keyword(s):  
Null Hypothesis ◽  
Statistical Power ◽  
Critical Appraisal ◽  
Type I Error ◽  
Statistical Significance ◽  
P Value ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Error Type

Background: Accurate understanding of certain basic statistical terms and principles is key to critical appraisal of published literature. Objective: This review describes type I error, type II error, null hypothesis, p value, statistical significance, a, two-tailed and one-tailed tests, effect size, alternate hypothesis, statistical power, β, publication bias, confidence interval, standard error, and standard deviation, while including examples from reports of dermatologic studies. Conclusion: The application of the results of published studies to individual patients should be informed by an understanding of certain basic statistical concepts.

The Conundrum of P-Values: Statistical Significance is Unavoidable but Need Medical Significance Too

2020 ◽  
Author(s):  
Abhaya Indrayan
Keyword(s):  
Type I Error ◽  
Dominant Role ◽  
Statistical Significance ◽  
Empirical Studies ◽  
P Value ◽  
Selective Reporting ◽  
Type I ◽  
Practical Application ◽  
P Values ◽  
Zero Effect

Background: Small P-values have been conventionally considered as evidence to reject a null hypothesis in empirical studies. However, there is widespread criticism of P-values now and the threshold we use for statistical significance is questioned.Methods: This communication is on contrarian view and explains why P-value and its threshold are still useful for ruling out sampling fluctuation as a source of the findings.Results: The problem is not with P-values themselves but it is with their misuse, abuse, and over-use, including the dominant role they have assumed in empirical results. False results may be mostly because of errors in design, invalid data, inadequate analysis, inappropriate interpretation, accumulation of Type-I error, and selective reporting, and not because of P-values per se.Conclusion: A threshold of P-values such as 0.05 for statistical significance is helpful in making a binary inference for practical application of the result. However, a lower threshold can be suggested to reduce the chance of false results. Also, the emphasis should be on detecting a medically significant effect and not zero effect.

Testing equality of means in partially paired data with incompleteness in single response

2018 ◽  
Vol 28 (5) ◽  
pp. 1508-1522 ◽  
Author(s):  
Qianya Qi ◽  
Li Yan ◽  
Lili Tian
Keyword(s):  
Type I Error ◽  
Real Data ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Type I ◽  
Paired Data ◽  
Data Set ◽  
Equality Of Means ◽  
Breast Cancer Study ◽  
Single Response

In testing differentially expressed genes between tumor and healthy tissues, data are usually collected in paired form. However, incomplete paired data often occur. While extensive statistical researches exist for paired data with incompleteness in both arms, hardly any recent work can be found on paired data with incompleteness in single arm. This paper aims to fill this gap by proposing some new methods, namely, P-value pooling methods and a nonparametric combination test. Simulation studies are conducted to investigate the performance of the proposed methods in terms of type I error and power at small to moderate sample sizes. A real data set from The Cancer Genome Atlas (TCGA) breast cancer study is analyzed using the proposed methods.

Inconsistencies in Reported p-Values in Spanish Journals of Psychology

Methodology ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 44-51 ◽  
Author(s):  
José Manuel Caperos ◽  
Ricardo Olmos ◽  
Antonio Pardo
Keyword(s):  
Type I Error ◽  
Meta Analysis ◽  
P Value ◽  
Type I ◽  
Simultaneous Inference ◽  
Test Statistics ◽  
Type I Errors ◽  
P Values ◽  
Editorial Boards ◽  
Correlation Tests

Abstract. Correlation analysis is one of the most widely used methods to test hypotheses in social and health sciences; however, its use is not completely error free. We have explored the frequency of inconsistencies between reported p-values and the associated test statistics in 186 papers published in four Spanish journals of psychology (1,950 correlation tests); we have also collected information about the use of one- versus two-tailed tests in the presence of directional hypotheses, and about the use of some kind of adjustment to control Type I errors due to simultaneous inference. Reported correlation tests (83.8%) are incomplete and 92.5% include an inexact p-value. Gross inconsistencies, which are liable to alter the statistical conclusions, appear in 4% of the reviewed tests, and 26.9% of the inconsistencies found were large enough to bias the results of a meta-analysis. The election of one-tailed tests and the use of adjustments to control the Type I error rate are negligible. We therefore urge authors, reviewers, and editorial boards to pay particular attention to this in order to prevent inconsistencies in statistical reports.

scholarly journals A New Framework for Online Testing of Heterogeneous Treatment Effect

2020 ◽  
Vol 34 (06) ◽  
pp. 10310-10317
Author(s):  
Miao Yu ◽  
Wenbin Lu ◽  
Rui Song
Keyword(s):  
Multiple Testing ◽  
Continuous Monitoring ◽  
Treatment Effects ◽  
Type I Error ◽  
Score Test ◽  
P Value ◽  
Type I ◽  
Online Testing ◽  
Heterogeneous Treatment Effects ◽  
New Framework

We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under continuous monitoring and detect multi-dimensional heterogeneous treatment effects. We provide an online p-value calculation for SST, making it convenient for continuous monitoring, and extend our tests to online multiple testing settings by controlling the false discovery rate. We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named mSPRT using simulations and a real data. The results show that our proposed test controls type I error at any time, has higher detection power and allows quick inference on online A/B testing.

scholarly journals Modifying and Evaluating the Alexander-Govern Test Using Real Data

2015 ◽  
Vol 9 (12) ◽  
pp. 1
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Binti Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Parametric Method ◽  
Real Life ◽  
Good Control ◽  
Error Rates ◽  
P Value ◽  
Type I ◽  
Central Tendency ◽  
Central Tendency Measure ◽  
Type I Error Rates

This study examines the use of independent group test of comparing two or more means by using parametric method, such as the Alexander-Govern (<em>AG</em>) test. The Alexander-Govern test is used for comparing two or more groups and is a better alternative compared to the James test, the Welch test and the <em>ANOVA</em>. This test has a good control of Type I error rates and gives a high power under variance heterogeneity for a normal data, but it is not robust for non-normal data. As a result, trimmed mean was applied on the test under non-normal data for two group condition. But this test could not control the Type I error rates, when the number of groups exceed two groups. As a result, the <em>MOM</em> estimator was introduced on the test, as its central tendency measure and is not influenced by the number of groups. But this estimator fails to give a good control of Type I error rates, under skewed heavy tailed distribution. In this study, the <em>AGWMOM </em>test was applied in Alexander-Govern test as its central tendency measure. To evaluate the capacity of the test, a real life data was used. Descriptive statistics, Tests of Normality and boxplots were used to determine the normality and non-normality of the independent groups. The results show that only the group middle is not normally distributed due extreme value in the data distribution. The results from the test statistic show that the <em>AGWMOM</em> test has a smaller p-value of 0.0000002869 that is less than 0.05, compared to the <em>AG</em> test that produced a p-value of 0.06982, that is greater than 0.05. Therefore, the <em>AGWMOM</em> test is considered to be significant, compared to the <em>AG</em> test.

scholarly journals Big data, observational research and P-value: a recipe for false-positive findings? A study of simulated and real prospective cohorts

2019 ◽  
Vol 49 (3) ◽  
pp. 876-884
Author(s):  
Giovanni Veronesi ◽  
Guido Grassi ◽  
Giordano Savelli ◽  
Piero Quatto ◽  
Antonella Zambon
Keyword(s):  
Selection Bias ◽  
Cancer Mortality ◽  
Observational Studies ◽  
Type I Error ◽  
Cox Model ◽  
Observational Research ◽  
P Value ◽  
Type I ◽  
Nominal Level ◽  
Negative Controls

Abstract Background An increasing number of observational studies combine large sample sizes with low participation rates, which could lead to standard inference failing to control the false-discovery rate. We investigated if the ‘empirical calibration of P-value’ method (EPCV), reliant on negative controls, can preserve type I error in the context of survival analysis. Methods We used simulated cohort studies with 50% participation rate and two different selection bias mechanisms, and a real-life application on predictors of cancer mortality using data from four population-based cohorts in Northern Italy (n = 6976 men and women aged 25–74 years at baseline and 17 years of median follow-up). Results Type I error for the standard Cox model was above the 5% nominal level in 15 out of 16 simulated settings; for n = 10 000, the chances of a null association with hazard ratio = 1.05 having a P-value &lt; 0.05 were 42.5%. Conversely, EPCV with 10 negative controls preserved the 5% nominal level in all the simulation settings, reducing bias in the point estimate by 80–90% when its main assumption was verified. In the real case, 15 out of 21 (71%) blood markers with no association with cancer mortality according to literature had a P-value &lt; 0.05 in age- and gender-adjusted Cox models. After calibration, only 1 (4.8%) remained statistically significant. Conclusions In the analyses of large observational studies prone to selection bias, the use of empirical distribution to calibrate P-values can substantially reduce the number of trivial results needing further screening for relevance and external validity.

scholarly journals Multi-SKAT: General framework to test multiple phenotype associations of rare variants

2017 ◽  
Author(s):  
Diptavo Dutta ◽  
Laura Scott ◽  
Michael Boehnke ◽  
Seunggeun Lee
Keyword(s):  
General Framework ◽  
Type I Error ◽  
Rare Variants ◽  
Simulated Data ◽  
P Value ◽  
Type I ◽  
Component Test ◽  
Multiple Phenotype ◽  
Causal Variants ◽  
Multivariate Kernel Regression

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association of underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here we develop a general framework for testing pleiotropic effects of rare variants based on multivariate kernel regression (Multi-SKAT). Multi-SKAT models effect sizes of variants on the phenotypes through a kernel matrix and performs a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power to detect association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses a random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METSIM study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.

scholarly journals Differences between alfa significance level and P value

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Hector Mueses
Keyword(s):  
Quantitative Research ◽  
Type I Error ◽  
P Value ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Information Analysis ◽  
Error Type ◽  
Significance Level ◽  
The Difference

Summary: Usually health professionals and people with little knowledge of statistics wheninvolved with quantitative research they are faced to make statistical techniques to fulfill thedata analysis resulting from a previous data collection. Generally they state hypothesis and laterthe information analysis can support the evidence in favor or against such hypothesis. In that pointcommonly they are faced to confusion when they try to interpret p value and type I error. Theconcept of p value and significance level will be approached in this paper and the difference amongthem will be cleared. Key words: Type I error. Type II error. P value. Null hypothesis. Statisticalof test.

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