scholarly journals Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Jean-Eudes Dazard ◽  
Hemant Ishwaran ◽  
Rajeev Mehlotra ◽  
Aaron Weinberg ◽  
Peter Zimmerman
Keyword(s):  
Pairwise Interaction ◽  
Survival Models ◽  
Test Statistics ◽  
Time To Event ◽  
Bootstrap Confidence Intervals ◽  
Pairwise Interactions ◽  
Aids Progression ◽  
Tree Models ◽  
Low Dimensional ◽  
Main Effects

Abstract Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

scholarly journals Existence of Gibbs point processes with stable infinite range interaction

2020 ◽  
Vol 57 (3) ◽  
pp. 775-791
Author(s):  
David Dereudre ◽  
Thibaut Vasseur
Keyword(s):  
Point Processes ◽  
Existence Results ◽  
Pairwise Interaction ◽  
Range Interaction ◽  
Pairwise Interactions ◽  
Gibbs Point Processes ◽  
Infinite Range Interactions ◽  
Finite Configuration ◽  
Multi Body ◽  
Infinite Range

AbstractWe provide a new proof of the existence of Gibbs point processes with infinite range interactions, based on the compactness of entropy levels. Our main existence theorem holds under two assumptions. The first one is the standard stability assumption, which means that the energy of any finite configuration is superlinear with respect to the number of points. The second assumption is the so-called intensity regularity, which controls the long range of the interaction via the intensity of the process. This assumption is new and introduced here since it is well adapted to the entropy approach. As a corollary of our main result we improve the existence results by Ruelle (1970) for pairwise interactions by relaxing the superstabilty assumption. Note that our setting is not reduced to pairwise interaction and can contain infinite-range multi-body counterparts.

scholarly journals Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

2021 ◽  
Author(s):  
Anna L Tyler ◽  
Baha El Kassaby ◽  
Georgi Kolishovski ◽  
Jake Emerson ◽  
Ann E Wells ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Interaction ◽  
Recombinant Inbred Lines ◽  
Test Statistics ◽  
Test Statistic ◽  
Kinship Matrix ◽  
Main Effect ◽  
Main Effects ◽  
Interaction Test

Abstract It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

scholarly journals Branching processes with interactions: subcritical cooperative regime

2021 ◽  
Vol 53 (1) ◽  
pp. 251-278
Author(s):  
Adrián González Casanova ◽  
Juan Carlos Pardo ◽  
José Luis Pérez
Keyword(s):  
Population Genetics ◽  
Branching Processes ◽  
Jump Diffusion ◽  
Pairwise Interaction ◽  
Pairwise Interactions ◽  
Different Types ◽  
Interesting Object ◽  
The Moment ◽  
Dynamics Of Populations

AbstractIn this paper, we introduce a family of processes with values on the nonnegative integers that describes the dynamics of populations where individuals are allowed to have different types of interactions. The types of interactions that we consider include pairwise interactions, such as competition, annihilation, and cooperation; and interactions among several individuals that can be viewed as catastrophes. We call such families of processes branching processes with interactions. Our aim is to study their long-term behaviour under a specific regime of the pairwise interaction parameters that we introduce as the subcritical cooperative regime. Under such a regime, we prove that a process in this class comes down from infinity and has a moment dual which turns out to be a jump-diffusion that can be thought as the evolution of the frequency of a trait or phenotype, and whose parameters have a classical interpretation in terms of population genetics. The moment dual is an important tool for characterizing the stationary distribution of branching processes with interactions whenever such a distribution exists; it is also an interesting object in its own right.

scholarly journals Data irregularities across five implicit learning articles: Comments on Lola and Tzetzis (2021), Lola and Tzetzis (2020), Tzetzis and Lola (2015), Lola, Tzetzis, and Zetou (2012), and Tzetzis and Lola (2010)

2021 ◽  
Author(s):  
Brad McKay ◽  
Michael J Carter
Keyword(s):  
Motor Learning ◽  
Implicit Learning ◽  
Pairwise Comparison ◽  
Effect Sizes ◽  
Test Statistics ◽  
Deductive Logic ◽  
Eta Squared ◽  
Calculated Effect ◽  
D Values ◽  
Main Effects

We present a critical re-analysis of five implicit learning papers published by the same authors between 2010 and 2021. We calculated effect sizes for each pairwise comparison reported in the papers using the data published in each article. We further identified mathematically impossible data reported in multiple papers, either with deductive logic or by conducting a GRIMMER analysis of reported means and standard deviations. We found the pairwise effect sizes were implausible in all five articles in question, with Cohen’s d values often exceeding 100 and sometimes exceeding 1000. Impossible statistics were reported in four out of the five articles. Reported test statistics and eta-squared values were also implausible, with several eta-squared = .99 and even eta-squared = 1.0 for between-subjects main effects. The results reported in the five articles in question are unreliable. Many of the problems we identified could be spotted without further analysis, highlighting the need for adequate statistical training in the field of motor learning.

scholarly journals Knockoff boosted tree for model-free variable selection

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yuanyuan Li ◽  
Alison A Motsinger-Reif
Keyword(s):  
Variable Selection ◽  
Principal Component ◽  
Free Variable ◽  
Supplementary Information ◽  
Type I ◽  
Test Statistics ◽  
Linear Regression Models ◽  
Model Free ◽  
Tree Models ◽  
Boosted Tree

Abstract Motivation The recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a ‘knockoff’ of each variable tested for exact FDR control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with FDR control. Results We propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extend the current knockoff method to model-free variable selection through the use of tree-based models. Additionally, we propose and evaluate two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test and compare these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compare the properties and performance of importance test statistics of tree models. The results include different combinations of knockoffs and importance test statistics. We consider scenarios that include main-effect, interaction, exponential and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using Cancer Genome Atlas (TCGA) gene expression data. Our results show improved discrimination between difficult-to-discriminate cancer types. Availability and implementation The proposed algorithm is included in the KOBT package, which is available at https://cran.r-project.org/web/packages/KOBT/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

SURVIVAL MODELS FOR WITHIN-TREE POPULATIONS OF DENDROCTONUS FRONTALIS (COLEOPTERA: SCOLYTIDAE)

1977 ◽  
Vol 109 (8) ◽  
pp. 1071-1077 ◽  
Author(s):  
Robert N. Coulson ◽  
P. E. Pulley ◽  
J. L. Foltz ◽  
W. C. Martin ◽  
C. L. Kelley
Keyword(s):  
Emerging Adults ◽  
Population Change ◽  
Adult Population ◽  
Developmental Time ◽  
Survival Model ◽  
Dendroctonus Frontalis ◽  
Survival Models ◽  
Infested Tree ◽  
Initial Cohort ◽  
Tree Models

AbstractWithin-tree models of Dendroctonus frontalis generation survival from attacking adults to emerging adults and survivorship from eggs to emergence were developed for five regions of the infested tree bole of Pinus taeda L. The generation survival model (GS) describes the number of D. frontalis/attacking adult as a function of time at a specific height. The form of the model isYGS = 1.0 + C(1–e–20.0X)eA(1.0–X)B + ɛ.The survival model (S) describes the number of D. frontalis/100 eggs as a function of time at a specific height. The form of this model isYS = CeA(1.0–X)B + ɛ.The generation survival model indicated that the rate of survival was primarily a function of generation development time, rather than position on the infested tree bole. The rates also varied in different sections of the tree depending on the initial egg/attacking adult population of D. frontalis. The emergence/attack ratios for the tree sections were slightly greater at the top and bottom than in the middle of the infested bole.The survivorship curves, based on an initial cohort of 100 eggs, were similar for the various sections of the tree bole. Again, the rate of population change was primarily a function of developmental time, rather than position on the tree. The curves for the various tree sections were essentially the same.The combined action of the various biotic and abiotic mortality agents acting in the different sections of the tree resulted in essentially uniform survivorship throughout the infested portion of the tree bole.

TESTING CHAOS OF TIME SERIES: PORTMANTEAU STATISTICS UNDER AN ORDER TRANSFORMATION

2001 ◽  
Vol 11 (06) ◽  
pp. 1761-1769 ◽  
Author(s):  
DEJIAN LAI
Keyword(s):  
Time Series ◽  
Null Hypothesis ◽  
Random Variable ◽  
Asymptotic Distributions ◽  
Test Statistics ◽  
Chi Square ◽  
Gaussian Series ◽  
Low Dimensional ◽  
Portmanteau Statistics ◽  
Direct Use

This paper studies several portmanteau test statistics with a nonparametric order transformation for distinguishing independent and identically distributed (i.i.d.) random processes from noisy chaotic time series. These portmanteau test statistics are asymptotically distributed as a chi-square random variable under the null hypothesis of i.i.d. Gaussian series. In this Letter, we show that the asymptotic distributions of these portmanteau test statistics on the transformed series are still chi-square under the null hypothesis. The simulations indicate that direct use of these portmanteau test statistics yields low power in identifying chaos. However, with the proposed order transformation, the simulations show that these test statistics are still effective for identifying noisy low dimensional chaos in some cases.

scholarly journals Uncovering emergent interactions in three-way combinations of stressors

2016 ◽  
Vol 13 (125) ◽  
pp. 20160800 ◽  
Author(s):  
Casey Beppler ◽  
Elif Tekin ◽  
Zhiyuan Mao ◽  
Cynthia White ◽  
Cassandra McDiarmid ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Bacterial Growth ◽  
Multiple Stressors ◽  
Drug Combinations ◽  
Pairwise Interaction ◽  
Environmental Toxicants ◽  
Drug Resistant ◽  
Combination Drug ◽  
Pairwise Interactions ◽  
Two Component

Understanding how multiple stressors interact is needed to predict the dynamical outcomes of diverse biological systems, ranging from drug-resistant pathogens that are combated and treated with combination drug therapies to ecosystems impacted by environmental toxicants or disturbances. Nevertheless, extensive studies of higher-order (more than two component) interactions have been lacking. Here, we conduct experiments using 20 three-drug combinations and their effects on the bacterial growth of Escherichia coli . We report our measurements of growth rates in single, pairwise and triple-drug combinations. To uncover emergent interactions, we derive a simple framework to calculate expectations for three-way interactions based on the measured impact of each individual stressor and of each pairwise interaction. Using our framework, we find that (i) emergent antagonisms are more common than emergent synergies and (ii) emergent antagonisms are more common and emergent synergies are more rare than would be inferred from measures of net effects that do not disentangle pairwise interactions from three-way interactions.

scholarly journals Dynamic cooperative network games with pairwise interactions

2020 ◽  
Vol 13 ◽  
pp. 95-120
Author(s):  
Mariia A. Bulgakova ◽  
Keyword(s):  
Dynamic Network ◽  
Pairwise Interaction ◽  
Cooperative Network ◽  
Two Stage ◽  
Network Games ◽  
Pairwise Interactions

This article is an overview of results obtained in the field of dynamic network games with pairwise interaction. The paper provides a summary and analysis of works related to two-stage and multistage nonzero-sum games based on pairwise interaction. The meaning of pairwise interaction is to consider the game as a family of games occurring on a network between pairs of players (vertices of a graph) connected to each other by an edge. The network can be set or formed in the first stage. In the paper, solutions of cooperative pairwise interaction games are also considered.

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