scholarly journals A New Method for Imputing Censored Values in Crossover Designs with Time-to-Event Outcomes Using Median Residual Life

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Maryam Jalali ◽  
Zahra Bagheri ◽  
Najaf Zare ◽  
Seyyed Mohammad Taghi Ayatollahi
Keyword(s):  
Regression Models ◽  
Residual Life ◽  
Model Averaging ◽  
Real Data ◽  
Analytical Models ◽  
Time To Event ◽  
Crossover Designs ◽  
Event Time ◽  
Censored Observations ◽  
The Difference

Crossover designs are commonly applied in research due to efficiency and subject parsimony compared to parallel studies. Baseline measurements would improve the power of comparison. For time to event outcomes, the sample size is reduced due to censorship, if they are ignored; thus, applying traditional regression models will be limited. A logical solution is to impute the censored observation and apply common analytical models for analyzing the data. Nevertheless, techniques to impute censored data in time-to-event outcomes in crossover designs are not practiced as much. Accordingly, we propose a method to impute the censored observation using median residual life regression and then analyze the data using analyses of covariance (ANCOVA), considering the difference of period-specific baselines as covariate. We used simulation to show the favorable performance of our method relative to a recently proposed method, multiple imputation with model averaging and ANCOVA (MIMI). Specifically, the censored observations were multiply-imputed using prespecified parametric event time models, and then, the methods were applied to a real data example.

scholarly journals Zero-inflated-censored Weibull and gamma regression models to estimate wild boar population dispersal distance

2021 ◽  
Author(s):  
Eduardo de Freitas Costa ◽  
Silvana Schneider ◽  
Giulia Bagatini Carlotto ◽  
Tainá Cabalheiro ◽  
Mauro Ribeiro de Oliveira Júnior
Keyword(s):  
Wild Boar ◽  
Regression Models ◽  
Likelihood Function ◽  
Complex Structure ◽  
Dispersal Distance ◽  
Wild Boar Population ◽  
Gamma Distributions ◽  
Excess Of Zeros ◽  
Censored Observations ◽  
Positive Effect

AbstractThe dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrating that both regression models behave well. The simulation results point to the consistency and asymptotic unbiasedness of the developed methods. Afterwards, we adjusted both models to a simulated dataset of wild boar dispersal, including excess of zeros, right-censored observations, and two covariates: age and sex. We showed that the models were useful to extract inferences about the wild boar dispersal, correctly describing the data mimicking a situation where males disperse more than females, and age has a positive effect on the dispersal of the wild boars. These results are useful to overcome some limitations regarding inferences in zero-inflated-right-censored datasets, especially concerning the wild boar’s population. Users will be provided with an R function to run the proposed models.

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

scholarly journals Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Biometrika ◽  
2020 ◽  
Author(s):  
S Na ◽  
M Kolar ◽  
O Koyejo
Keyword(s):  
Graphical Models ◽  
Latent Variable ◽  
Graphical Model ◽  
Brain Connectivity ◽  
Statistical Error ◽  
Real Data ◽  
Low Rank ◽  
Conditional Dependence ◽  
Gradient Descent Algorithm ◽  
The Difference

Abstract Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific investigation. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components, both of which are symmetric indefinite matrices. We estimate these two components simultaneously using a two-stage procedure: (i) an initialization stage, which computes a simple, consistent estimator, and (ii) a convergence stage, implemented using a projected alternating gradient descent algorithm applied to a nonconvex objective, initialized using the output of the first stage. We prove that given the initialization, the estimator converges linearly with a nontrivial, minimax optimal statistical error. Experiments on synthetic and real data illustrate that the proposed nonconvex procedure outperforms existing methods.

scholarly journals Events, Time and the Simple Form

2009 ◽  
Vol 17 (1) ◽  
pp. 85-105 ◽  
Author(s):  
Walter H. Hirtle
Keyword(s):  
Simple Form ◽  
Point Of View ◽  
Event Time ◽  
The Difference ◽  
Time Required ◽  
English Verb

Abstract This is an attempt to discern more clearly the underlying or POTENTIAL meaning of the simple form of the English verb, described in Hirtle 1967 as 'perfective'. Vendler's widely accepted classification of events into ACCOMPLISHMENTS, ACHIEVEMENTS, ACTIVITIES, and STATES is examined from the point of view of the time necessarily contained between the beginning and end of any event, i.e. EVENT TIME as represented by the simple form. This examination justifies the well known dynamic/stative dichotomy by showing that event time is evoked in two different ways, that, in fact, the simple form has two ACTUAL significates. Further reflection on the difference between the two types thus expressed—developmental or action-like events and non-developmental or state-like events—leads to the conclusion that the simple form provides a representation of the time required to situate all the impressions involved in the notional or lexical import of the verb.

scholarly journals A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jia-Rou Liu ◽  
Po-Hsiu Kuo ◽  
Hung Hung
Keyword(s):  
Characteristic Curve ◽  
Real Data ◽  
Tuning Parameter ◽  
Practical Implementation ◽  
Tuning Parameters ◽  
Genome Wide ◽  
Feature Based ◽  
The Difference ◽  
Gwa Studies ◽  
Selection Of

Large-p-small-ndatasets are commonly encountered in modern biomedical studies. To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances int-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates. The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward. In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties. In particular, we obtain a rank-based statistic for each feature based on the concept of “rank-over-variable.” Techniques of “random subset” and “rerank” are then iteratively applied to rank features, and the leading features will be selected for further studies. The proposed re-rank approach is especially applicable for large-p-small-ndatasets. Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation. Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.

scholarly journals The Relationship Between Alcohol Beverage Types and Violence

2018 ◽  
Vol 49 (1) ◽  
pp. 183-200
Author(s):  
Aleksandra J. Snowden
Keyword(s):  
Regression Models ◽  
Neighborhood Characteristics ◽  
Substantial Evidence ◽  
Alcohol Outlets ◽  
Observation Instrument ◽  
Beverage Type ◽  
Social Observation ◽  
Ecological Association ◽  
The Difference ◽  
The Relationship

There is substantial evidence of an ecological association between off-premise alcohol outlets and violence. We know less, however, about how specific beverage types that are sold in the outlets might explain the difference in violence rates across different alcohol outlets. Data on alcohol beverage types were collected for all off-premise alcohol outlets in Milwaukee, Wisconsin, using a systematic social observation instrument. Spatially lagged regression models were estimated to determine whether the variation in alcohol beverage types is related to robbery density net of important neighborhood predictors of crime rates. Availability of all alcohol beverage types (beer, wine, spirits, premixed, single beer, single spirits, single premixed) was positively associated with the density of robberies, net of neighborhood characteristics. Reducing alcohol beverages, regardless of the beverage type, sold at off-premise alcohol outlets may reduce violence in communities.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Flexible Bayesian Nonlinear Model Configuration

2021 ◽  
Vol 72 ◽  
pp. 901-942
Author(s):  
Aliaksandr Hubin ◽  
Geir Storvik ◽  
Florian Frommlet
Keyword(s):  
Regression Models ◽  
Nonlinear Models ◽  
Model Averaging ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Monte Carlo Algorithm ◽  
Wide Range ◽  
Nonlinear Features ◽  
Interpretable Models

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through  flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a  flexible approach for the construction and selection of highly  flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional  flexibility on the possible types of features to be considered. This  flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modi ed mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.  

scholarly journals Causal Interaction and Effect Modification: Same Model, Different Concepts

2017 ◽  
Author(s):  
Luke Keele ◽  
Randolph T. Stevenson
Keyword(s):  
Political Science ◽  
Regression Models ◽  
Interaction Analysis ◽  
Effect Modification ◽  
The Political ◽  
Science Literature ◽  
Causal Interaction ◽  
Single Treatment ◽  
Social Scientists ◽  
The Difference

Social scientists use the concept of interactions to study effect dependency. Such analyses can be conducted using standard regression models. However, an interaction analysis may represent either a causal interaction or effect modification. Under causal interaction, the analyst is interested in whether two treatments have differing effects when both are administered. Under effect modification, the analysts investigates whether the effect of a single treatment varies across levels of a baseline covariate. Importantly, the identification assumptions for these two types of analysis are very different. In this paper, we clarify the difference between these two types of interaction analysis. We demonstrate that this distinction is mostly ignored in the political science literature. We conclude with a review of several applications.

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