scholarly journals Statistical tests for intra-tumour clonal co-occurrence and exclusivity

2021 ◽  
Vol 17 (12) ◽  
pp. e1009036
Author(s):  
Jack Kuipers ◽  
Ariane L. Moore ◽  
Katharina Jahn ◽  
Peter Schraml ◽  
Feng Wang ◽  
...  
Keyword(s):  
Tumour Progression ◽  
Statistical Tests ◽  
Evolutionary Process ◽  
Statistical Testing ◽  
Type I ◽  
Type I Errors ◽  
Tumour Heterogeneity ◽  
Testing Framework ◽  
Single Cell Sequencing ◽  
Gene Pairs

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

scholarly journals Statistical Tests for the Reciprocal of a Normal Mean with a Known Coefficient of Variation

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Wararit Panichkitkosolkul
Keyword(s):  
Monte Carlo Simulation ◽  
Coefficient Of Variation ◽  
Statistical Tests ◽  
Taylor Series Expansion ◽  
Type I ◽  
Type I Errors ◽  
Monte Carlo Simulation Study ◽  
Asymptotic Test ◽  
Normal Mean ◽  
Simulation Results

An asymptotic test and an approximate test for the reciprocal of a normal mean with a known coefficient of variation were proposed in this paper. The asymptotic test was based on the expectation and variance of the estimator of the reciprocal of a normal mean. The approximate test used the approximate expectation and variance of the estimator by Taylor series expansion. A Monte Carlo simulation study was conducted to compare the performance of the two statistical tests. Simulation results showed that the two proposed tests performed well in terms of empirical type I errors and power. Nevertheless, the approximate test was easier to compute than the asymptotic test.

A Comparison of Simultaneous Confidence Intervals to Identify Handwritten Digits

2014 ◽  
Vol 5 (3) ◽  
pp. 29-40
Author(s):  
Nicolle Clements
Keyword(s):  
Confidence Interval ◽  
Statistical Testing ◽  
Interval Methods ◽  
Type I ◽  
Familywise Error Rate ◽  
Type I Errors ◽  
Data Set ◽  
Simultaneous Confidence Intervals ◽  
Automated Recognition ◽  
Testing Procedures

This paper evaluates the use of several known simultaneous confidence interval methods for the automated recognition of handwritten digits from data in a well-known handwriting database. Contained in this database are handwritten digits, 0 through 9, that were obtained from 42,000 participants' writing samples. The objective of the analyses is to utilize statistical testing procedures that can be easily automated by a computer to recognize which digit was written by a subject. The methodologies discussed in this paper are designed to be sensitive to Type I errors and will control an overall measure of these errors, called the Familywise Error Rate. The procedures were constructed based off of a training portion of the data set, then applied and validated on the remaining testing portion of the data.

A more efficient three-arm non-inferiority test based on pooled estimators of the homogeneous variance

2016 ◽  
Vol 27 (8) ◽  
pp. 2437-2446 ◽  
Author(s):  
Hezhi Lu ◽  
Hua Jin ◽  
Weixiong Zeng
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Statistical Power ◽  
Type I Error ◽  
Statistical Testing ◽  
New Method ◽  
Type I ◽  
Simulation Studies ◽  
Testing Framework ◽  
Better Than

Hida and Tango established a statistical testing framework for the three-arm non-inferiority trial including a placebo with a pre-specified non-inferiority margin to overcome the shortcomings of traditional two-arm non-inferiority trials (such as having to choose the non-inferiority margin). In this paper, we propose a new method that improves their approach with respect to two aspects. We construct our testing statistics based on the best unbiased pooled estimators of the homogeneous variance; and we use the principle of intersection-union tests to determine the rejection rule. We theoretically prove that our test is better than that of Hida and Tango for large sample sizes. Furthermore, when that sample size was small or moderate, our simulation studies showed that our approach performed better than Hida and Tango’s. Although both controlled the type I error rate, their test was more conservative and the statistical power of our test was higher.

Clarifying Agreement Calculations and Analysis for End-User Elicitation Studies

2022 ◽  
Vol 29 (1) ◽  
pp. 1-70
Author(s):  
Radu-Daniel Vatavu ◽  
Jacob O. Wobbrock
Keyword(s):  
Formal Model ◽  
Statistical Tests ◽  
Type I ◽  
End User ◽  
Type I Errors ◽  
Scientific Rigor ◽  
Tolerance Relation ◽  
Measures Of Agreement ◽  
Elicitation Studies ◽  
Review Current

We clarify fundamental aspects of end-user elicitation, enabling such studies to be run and analyzed with confidence, correctness, and scientific rigor. To this end, our contributions are multifold. We introduce a formal model of end-user elicitation in HCI and identify three types of agreement analysis: expert , codebook , and computer . We show that agreement is a mathematical tolerance relation generating a tolerance space over the set of elicited proposals. We review current measures of agreement and show that all can be computed from an agreement graph . In response to recent criticisms, we show that chance agreement represents an issue solely for inter-rater reliability studies and not for end-user elicitation, where it is opposed by chance disagreement . We conduct extensive simulations of 16 statistical tests for agreement rates, and report Type I errors and power. Based on our findings, we provide recommendations for practitioners and introduce a five-level hierarchy for elicitation studies.

Underreporting in Political Science Survey Experiments: Comparing Questionnaires to Published Results

2015 ◽  
Vol 23 (2) ◽  
pp. 306-312 ◽  
Author(s):  
Annie Franco ◽  
Neil Malhotra ◽  
Gabor Simonovits
Keyword(s):  
Multiple Testing ◽  
Statistical Tests ◽  
Inductive Learning ◽  
Type I ◽  
Time Sharing ◽  
Institutional Reforms ◽  
Type I Errors ◽  
Experimental Conditions ◽  
The Social ◽  
Survey Experiments

The accuracy of published findings is compromised when researchers fail to report and adjust for multiple testing. Preregistration of studies and the requirement of preanalysis plans for publication are two proposed solutions to combat this problem. Some have raised concerns that such changes in research practice may hinder inductive learning. However, without knowing the extent of underreporting, it is difficult to assess the costs and benefits of institutional reforms. This paper examines published survey experiments conducted as part of the Time-sharing Experiments in the Social Sciences program, where the questionnaires are made publicly available, allowing us to compare planned design features against what is reported in published research. We find that: (1) 30% of papers report fewer experimental conditions in the published paper than in the questionnaire; (2) roughly 60% of papers report fewer outcome variables than what are listed in the questionnaire; and (3) about 80% of papers fail to report all experimental conditions and outcomes. These findings suggest that published statistical tests understate the probability of type I errors.

A Poisson Method of Controlling the Maximum Tolerable Number of Type I Errors

1978 ◽  
Vol 46 (1) ◽  
pp. 211-218
Author(s):  
Louis M. Hsu
Keyword(s):  
Type I Error ◽  
Statistical Tests ◽  
Poisson Approximation ◽  
Type I ◽  
Type I Errors ◽  
Significance Level ◽  
Orthogonal Contrasts ◽  
Multiple Tests ◽  
Significance Levels ◽  
Tests Of Hypotheses

The problem of controlling the risk of occurrence of at least one Type I Error in a family of n statistical tests has been discussed extensively in psychological literature. However, the more general problem of controlling the probability of occurrence of more than some maximum (not necessarily zero) tolerable number ( xm) of Type I Errors in such a family appears to have received little attention. The present paper presents a simple Poisson approximation to the significance level P( EI) which should be used per test, to achieve this goal, in a family of n independent tests. The cases of equal and unequal significance levels for the n tests are discussed. Relative merits and limitations of the Poisson and Bonferroni methods of controlling the number of Type I Errors are examined, and application of the Poisson method to tests of orthogonal contrasts in analysis of variance, multiple tests of hypotheses in single studies, and multiple tests of hypotheses in literature reviews, are discussed.

Parametric Statistical Tests

2018 ◽  
pp. 181-205
Author(s):  
Ben M. W. Illigens ◽  
Fernanda Lopes ◽  
Felipe Fregni ◽  
Andre Brunoni
Keyword(s):  
Statistical Tests ◽  
Statistical Significance ◽  
Data Distribution ◽  
Theoretical Explanation ◽  
Statistical Testing ◽  
P Value ◽  
Type I ◽  
Type Ii ◽  
Evaluation Of Data ◽  
Type Ii Errors

Chapter 9 provides an introduction to statistical testing,alongside the most common parametric tests. It reviews the basis of hypothesis testing while highlighting important concepts such as chance, bias, and confounding. Additionally, this chapter discusses fundamental topics in basic statistics, including p-value, type I and type II errors, alpha (α‎), beta (β‎), and statistical significance. The reader is taken throughout the basics of the theoretical explanation of t-test, ANOVA and linear regression, together with the indications of appropriate use for each of them. Additionally, the reader will learn how to perform and interpret the output for these tests in statistical softwares (e.g., STATA and SPSS). This chapter also examines the concept of normality and central limit theorem (CLT), and how it applies to the evaluation of data distribution.

scholarly journals Outlier Exclusion Procedures Must be Blind to the Researcher’s Hypothesis

2021 ◽  
Author(s):  
Quentin André
Keyword(s):  
False Positive ◽  
Statistical Tests ◽  
Exclusion Criterion ◽  
Type I ◽  
Data Types ◽  
Type I Errors ◽  
Experimental Conditions ◽  
Inflated Type ◽  
Visualization Techniques ◽  
Do So

When researchers choose to identify and exclude outliers from their data, should they do so across all the data, or within experimental conditions? A survey of recent papers published in the Journal of Experimental Psychology: General shows that both methods are widely used, and common data visualization techniques suggest that outliers should be excluded at the condition-level. However, I highlight in the present paper that removing outliers by condition runs against the logic of hypothesis testing, and that this practice leads to unacceptable increases in false-positive rates. I demonstrate that this conclusion holds true across a variety of statistical tests, exclusion criterion and cutoffs, sample sizes, and data types, and show in simulated experiments and in a re-analysis of existing data that by-condition exclusions can result in false-positive rates as high as 43%. I finally demonstrate that by-condition exclusions are a specific case of a more general issue: Any outlier exclusion procedure that is not blind to the hypothesis that researchers want to test may result in inflated Type I errors. I conclude by offering best practices and recommendations for excluding outliers.

scholarly journals Dataset Decay: the problem of sequential analyses on open datasets

2019 ◽  
Author(s):  
William Hedley Thompson ◽  
Jessey Wright ◽  
Patrick G Bissett ◽  
Russell A Poldrack
Keyword(s):  
Open Data ◽  
Error Rates ◽  
False Positives ◽  
Statistical Testing ◽  
Type I ◽  
Type I Errors ◽  
Sequential Hypothesis ◽  
Sequential Hypothesis Testing ◽  
Open Datasets ◽  
Sequential Analyses

AbstractOpen data has two principal uses: (i) to reproduce original findings and (ii) to allow researchers to ask new questions with existing data. The latter enables discoveries by allowing a more diverse set of viewpoints and hypotheses to approach the data, which is self-evidently advantageous for the progress of science. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives in the literature. Current practice suggests that the number of tests to be corrected is the number of simultaneous tests performed by a researcher. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. This finding is troubling because, as more researchers embrace an open dataset, the likelihood of false positives (i.e. type I errors) will increase. Thus, we should expect a dataset’s utility for discovering new true relations between variables to decay. We consider several sequential correction procedures. These solutions can reduce the number of false positives but, at the same time, can prompt undesired challenges to open data (e.g. incentivising restricted access).

Heteroscedastic Methods for Performing All Pairwise Comparisons of Regression Lines Associated With J Independent Groups

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 110-115 ◽  
Author(s):  
Rand R. Wilcox ◽  
Jinxia Ma
Keyword(s):  
Least Squares ◽  
Pairwise Comparisons ◽  
Simple Extension ◽  
Type I ◽  
Type I Errors ◽  
Well Elderly ◽  
Using Data

Abstract. The paper compares methods that allow both within group and between group heteroscedasticity when performing all pairwise comparisons of the least squares lines associated with J independent groups. The methods are based on simple extension of results derived by Johansen (1980) and Welch (1938) in conjunction with the HC3 and HC4 estimators. The probability of one or more Type I errors is controlled using the improvement on the Bonferroni method derived by Hochberg (1988) . Results are illustrated using data from the Well Elderly 2 study, which motivated this paper.

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