scholarly journals New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Émeline Courtois ◽  
Pascale Tubert-Bitter ◽  
Ismaïl Ahmed
Keyword(s):  
Variable Selection ◽  
Signal Detection ◽  
Propensity Scores ◽  
Regularization Parameter ◽  
Real Data ◽  
Information Criterion ◽  
Test Theory ◽  
Adaptive Lasso ◽  
Lasso Regression ◽  
Adaptive Weights

Abstract Background Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. Methods We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. Results In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. Conclusion Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

scholarly journals New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

2021 ◽  
Author(s):  
Émeline Courtois ◽  
Pascale Tubert-Bitter ◽  
Ismaïl Ahmed
Keyword(s):  
Variable Selection ◽  
Signal Detection ◽  
High Dimension ◽  
Regularization Parameter ◽  
Real Data ◽  
Information Criterion ◽  
Test Theory ◽  
Adaptive Lasso ◽  
Lasso Regression ◽  
Adaptive Weights

Abstract Background: Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may be inconsistent. Methods: We propose a new signal detection methodology based on the adaptive lasso in the context of high dimension. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. Results: In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. Conclusion: Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

scholarly journals Variable Selection for Nonparametric Learning with Power Series Kernels

2019 ◽  
Vol 31 (8) ◽  
pp. 1718-1750
Author(s):  
Kota Matsui ◽  
Wataru Kumagai ◽  
Kenta Kanamori ◽  
Mitsuaki Nishikimi ◽  
Takafumi Kanamori
Keyword(s):  
Power Series ◽  
Variable Selection ◽  
Density Ratio ◽  
Target Function ◽  
Real Data ◽  
Adaptive Lasso ◽  
Text Type ◽  
Nonparametric Learning ◽  
Stage Estimation ◽  
Selection Consistency

In this letter, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, and (2) approximate the estimator using a few variables by [Formula: see text]-type penalized estimation. We see that the proposed method can be applied to various kernel nonparametric estimation such as kernel ridge regression, kernel-based density, and density-ratio estimation. We prove that the proposed method has the property of variable selection consistency when the power series kernel is used. Here, the power series kernel is a certain class of kernels containing polynomial and exponential kernels. This result is regarded as an extension of the variable selection consistency for the nonnegative garrote (NNG), a special case of the adaptive Lasso, to the kernel-based estimators. Several experiments, including simulation studies and real data applications, show the effectiveness of the proposed method.

Propensity score analysis for correlated subgroup effects

2019 ◽  
Vol 29 (4) ◽  
pp. 1067-1080
Author(s):  
Shan-Yu Liu ◽  
Chunyan Liu ◽  
Eddie Nehus ◽  
Maurizio Macaluso ◽  
Bo Lu ◽  
...  
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Real Data ◽  
Treatment Selection ◽  
Propensity Score Model ◽  
Interaction Terms ◽  
Selection Processes ◽  
Subgroup Effects

As individuals may respond differently to treatment, estimating subgroup effects is important to understand the characteristics of individuals who may benefit. Factors that define subgroups may be correlated, complicating evaluation of subgroup effects, especially in observational studies requiring control of confounding variables. We address this problem when propensity score methods are used for confounding control. A common practice is to evaluate candidate subgroup identifiers one at a time without adjusting for other candidate identifiers. We show that this practice can be misleading if the treatment effect modification attributed to a candidate identifier is in truth due to the effect of other correlated true effect modifiers. Whereas jointly analyzing multiple identifiers provides estimates of the desired subgroup effects adjusted for the effects of the other identifiers, it requires the propensity scores to adequately reflect the underlying treatment selection processes and balance the covariates within each subgroup of interest. Satisfying the requirement in practice is hard since the number of strata may increase quickly, while the per stratum sample size may decrease dramatically. A practically helpful approach is utilizing the whole cohort for the propensity score estimation with modeling of interaction terms to reflect the potentially different treatment selection processes across strata. We empirically examine the performance of the whole cohort approach by itself and with subjecting the interaction terms to variable selection. Our results using both simulations and real data analysis suggest that the whole cohort approach should explore inclusion of high-order interactions in the propensity score model to ensure adequate covariate balance across strata, and that variable selection is of limited utility.

scholarly journals Regularization and variable selection in Heckman selection model

2021 ◽  
Author(s):  
Emmanuel O. Ogundimu
Keyword(s):  
Variable Selection ◽  
Expert Knowledge ◽  
Regularization Parameter ◽  
Sample Selection ◽  
Selection Model ◽  
Adaptive Lasso ◽  
Least Squares Regression ◽  
Heckman Selection Model ◽  
Regression Problem ◽  
Exclusion Restrictions

AbstractSample selection arises when the outcome of interest is partially observed in a study. A common challenge is the requirement for exclusion restrictions. That is, some of the covariates affecting missingness mechanism do not affect the outcome. The drive to establish this requirement often leads to the inclusion of irrelevant variables in the model. A suboptimal solution is the use of classical variable selection criteria such as AIC and BIC, and traditional variable selection procedures such as stepwise selection. These methods are unstable when there is limited expert knowledge about the variables to include in the model. To address this, we propose the use of adaptive Lasso for variable selection and parameter estimation in both the selection and outcome submodels simultaneously in the absence of exclusion restrictions. By using the maximum likelihood estimator of the sample selection model, we constructed a loss function similar to the least squares regression problem up to a constant, and minimized its penalized version using an efficient algorithm. We show that the estimator, with proper choice of regularization parameter, is consistent and possesses the oracle properties. The method is compared to Lasso and adaptively weighted $$L_{1}$$ L 1 penalized Two-step method. We applied the methods to the well-known Ambulatory Expenditure Data.

scholarly journals A Robust Supervised Variable Selection for Noisy High-Dimensional Data

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jan Kalina ◽  
Anna Schlenker
Keyword(s):  
Variable Selection ◽  
Dimensionality Reduction ◽  
Robust Statistics ◽  
High Dimensional Data ◽  
Real Data ◽  
High Dimensional ◽  
Adaptive Weights ◽  
Novel Approach ◽  
Reduction Methods ◽  
Data Adaptive

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.

scholarly journals A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Prithish Banerjee ◽  
Broti Garai ◽  
Himel Mallick ◽  
Shrabanti Chowdhury ◽  
Saptarshi Chatterjee
Keyword(s):  
Variable Selection ◽  
Count Data ◽  
Adaptive Lasso ◽  
Health Care Demand ◽  
Adaptive Weights ◽  
Excess Zeros ◽  
New Methods ◽  
German Health ◽  
True Model ◽  
Data Adaptive

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.

scholarly journals Parametric and Semiparametric Estimations of Bivariate Truncated Type I Generalized Logistic Models driven from Copulas

2017 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Lamya A Baharith
Keyword(s):  
Logistic Models ◽  
Real Data ◽  
Information Criterion ◽  
Logistic Distribution ◽  
Type I ◽  
Copula Functions ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Generalized Logistic Distribution ◽  
Weibull Models

Truncated type I generalized logistic distribution has been used in a variety of applications. In this article, a new bivariate truncated type I generalized logistic (BTTGL) distributional models driven from three different copula functions are introduced. A study of some properties is illustrated. Parametric and semiparametric methods are used to estimate the parameters of the BTTGL models. Maximum likelihood and inference function for margin estimates of the BTTGL parameters are compared with semiparametric estimates using real data set. Further, a comparison between BTTGL, bivariate generalized exponential and bivariate exponentiated Weibull models is conducted using Akaike information criterion and the maximized log-likelihood. Extensive Monte Carlo simulation study is carried out for different values of the parameters and different sample sizes to compare the performance of parametric and semiparametric estimators based on relative mean square error.

scholarly journals Hidden Semi-Markov Models for Predictive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Francesco Cartella ◽  
Jan Lemeire ◽  
Luca Dimiccoli ◽  
Hichem Sahli
Keyword(s):  
Markov Models ◽  
Real Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Predictive Maintenance ◽  
Average Absolute Error ◽  
Current State ◽  
Automatic Model Selection ◽  
State Duration ◽  
Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

scholarly journals Jewel: A Novel Method for Joint Estimation of Gaussian Graphical Models

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2105
Author(s):  
Claudia Angelini ◽  
Daniela De De Canditiis ◽  
Anna Plaksienko
Keyword(s):  
Graphical Models ◽  
Regulatory Networks ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Real Data ◽  
Penalized Regression ◽  
Joint Estimation ◽  
Gaussian Graphical Models ◽  
Lasso Penalty ◽  
Independence Structure

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix’s symmetry and the graphs’ joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator’s consistency property. Finally, we illustrate our estimator’s performance through simulated and real data examples on gene regulatory networks.

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