Likelihood Ratio Test Method Research Articles

Extended likelihood ratio test-based methods for signal detection in a drug class with application to FDA's adverse event reporting system database.

A likelihood ratio test, recently developed for the detection of signals of adverse events for a drug of interest in the FDA Adverse Events Reporting System database, is extended to detect signals of adverse events simultaneously for all the drugs in a drug class. The extended likelihood ratio test methods, based on Poisson model (Ext-LRT) and zero-inflated Poisson model (Ext-ZIP-LRT), are discussed and are analytically shown, like the likelihood ratio test method, to control the type-I error and false discovery rate. Simulation studies are performed to evaluate the performance characteristics of Ext-LRT and Ext-ZIP-LRT. The proposed methods are applied to the Gadolinium drug class in FAERS database. An in-house likelihood ratio test tool, incorporating the Ext-LRT methodology, is being developed in the Food and Drug Administration.

Likelihood ratio and score tests to test the non-inferiority (or equivalence) of the odds ratio in a crossover study with binary outcomes.

We consider the non-inferiority (or equivalence) test of the odds ratio (OR) in a crossover study with binary outcomes to evaluate the treatment effects of two drugs. To solve this problem, Lui and Chang (2011) proposed both an asymptotic method and a conditional method based on a random effects logit model. Kenward and Jones (1987) proposed a likelihood ratio test (LRTM ) based on a log linear model. These existing methods are all subject to model misspecification. In this paper, we propose a likelihood ratio test (LRT) and a score test that are independent of model specification. Monte Carlo simulation studies show that, in scenarios considered in this paper, both the LRT and the score test have higher power than the asymptotic and conditional methods for the non-inferiority test; the LRT, score, and asymptotic methods have similar power, and they all have higher power than the conditional method for the equivalence test. When data can be well described by a log linear model, the LRTM has the highest power among all the five methods (LRTM , LRT, score, asymptotic, and conditional) for both non-inferiority and equivalence tests. However, in scenarios for which a log linear model does not describe the data well, the LRTM has the lowest power for the non-inferiority test and has inflated type I error rates for the equivalence test. We provide an example from a clinical trial that illustrates our methods. Copyright © 2016 John Wiley & Sons, Ltd.

On the Near-Optimality of Training-Based GLRT Spectrum Sensing

Spectrum sensing is of utmost importance in cognitive radio and dynamic spectrum access systems for achieving spectrum awareness. To provide reliable spectrum awareness, it is critical to develop (near-) optimal sensing techniques and understand achievable performance limits. In this paper, we study likelihood ratio test (LRT)-based methods for detection of a signal of interest (SOI) assuming multiple receive antennas in the presence of spatially correlated additive noise (colored noise). We show that with on/off status information of the SOI available at receiver, a training-based generalized likelihood ratio test (TB-GLRT) method can be designed which approximates the optimal LRT estimator-correlator (EC) detector. Using the inverse Laplace transform and complex Wishart distribution theory, we derive formulas for detection and false alarm probabilities of both LRT-EC and GLRT schemes. The convergence of the TB-GLRT to the LRT-EC is analyzed and proved based on convergence behaviors of the detectors and their decision statistics. Simulation results verify the analytical convergence properties. In addition, the results illustrate the effects of different system design parameters, and demonstrate that the TB-GLRT scheme can provide substantial performance improvement over several existing GLRT methods.

A review on polarization‐based spectrum sensing

AbstractIn cognitive radios, the biggest challenge is spectrum sensing. It usually makes use of the amplitude, frequency or space dimension of the received signal to differentiate signal from background noise. Polarization, also as an inherent characteristic of signals, provides an additional degree of freedom in signal space. Polarization‐based spectrum sensing has been identified as an effective method for improving detection performance. In this paper, various polarization‐based spectrum sensing methods including the optimal polarization likelihood ratio test using Neyman–Pearson theorem, virtual polarization detection utilizing maximum signal to noise ratio method and blind polarization detectors based on generalized likelihood ratio test method are reviewed. Then, a comprehensive comparison of various polarization‐based sensing methods in terms of detection performance and computational complexity is detailed. Finally, challenges and possible future studies on polarization‐based spectrum sensing are described. Copyright © 2015 John Wiley & Sons, Ltd.

Likelihood-based complex trait association testing for arbitrary depth sequencing data.

In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ∼60% when the causal variant is of low frequency (MAF < 0.01). UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/∼yunmli/UNCcombo/ yunli@med.unc.edu Supplementary data are available at Bioinformatics online.

Adaptive Anisotropic Diffusion Method for Polarimetric SAR Speckle Filtering

In this paper, we present an adaptive anisotropic diffusion (AD) method for the speckle filtering of polarimetric synthetic aperture radar (PolSAR) images. One of the main innovations of our work is that we employ a likelihood-ratio test method to measure the equality of two polarimetric covariance matrices to control the diffusivity, and thus consider the full polarimetric information and the statistical traits of PolSAR data in the diffusion process. Meanwhile, to overcome the drawback of the conventional AD methods, we integrate the local homogeneity information into the diffusion model to adaptively control the generosity of the filtering. Experiments were conducted on a simulated image and two airborne PolSAR images to illustrate the filtering performance, and the results show that the proposed method effectively reduces speckle, retains edges, and targets, and preserves the polarimetric scattering mechanisms.

A Comparison of IRT-based Methods for Examining Differential Item Functioning in TIMSS 2011 Mathematics Subtest

The purpose of this study is to determine items which have differential item functioning (DIF) in TIMSS 2011 mathematics subtest with three different item response theory (IRT)-based DIF methods and compare results of these methods. For this purpose, DIF values obtained by Lord's Chi-Square, Raju's Area and Likelihood-Ratio Test methods were compared with respect to gender (males were the reference group while females were the focal group) to test whether these procedures yielded similar results. In addition, item purification was performed for each methods and results were compared in order to determine the effect of item purification. These comparisons can provide evidence for determining the best models for detecting DIF items. Results indicated that 2PL IRT model fitted best to the data for both Lord's Chi-Square method and Raju's Signed Area method. Although number of items detected as DIF differed for each methods, 2 out of 22 dichotomous items in the test observed consistently across all methods, which were more likely to be answered correctly by males after controlling for overall ability.

An Improved GLRT Method for Target Detection in SAR Imagery

Automatic ground vehicle detection based on SAR imagery is one of the important military applications of SAR. A region-based generalized likelihood ratio test (GLRT) method is proposed in this paper, and this method combines the GLRT detection theory and image segmentation technology. First, the SAR imagery is roughly segmented as land clutter region and potential target region through the split and merge procedure often used for processing the original images. Then, based on the segmentation results, the reasonable statistical models for the data in the two regions are built respectively. Finally, with the knowledge of statistical characteristics of clutter and target, GLRT detection method is applied to the each pixel in the potential target region to obtain more accurate detection results. Experimental results based on real SAR data show that the proposed method can effectively detect the ground vehicle targets from the land clutter with excellent accuracy and speed.

Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors.

Traditional parallel analysis (T-PA) estimates the number of factors by sequentially comparing sample eigenvalues with eigenvalues for randomly generated data. Revised parallel analysis (R-PA) sequentially compares the kth eigenvalue for sample data to the kth eigenvalue for generated data sets, conditioned on k- 1 underlying factors. T-PA and R-PA are conceptualized as stepwise hypothesis-testing procedures and, thus, are alternatives to sequential likelihood ratio test (LRT) methods. We assessed the accuracy of T-PA, R-PA, and LRT methods using a Monte Carlo approach. Although no method was uniformly more accurate across all 180 conditions, the PA approaches outperformed LRT methods overall. Relative to T-PA, R-PA tended to perform better within the framework of hypothesis testing and to evidence greater accuracy in conditions with higher factor loadings.

Phase I monitoring of generalized linear model-based regression profiles

In some industrial applications, the quality of a process or product is characterized by a relationship between the response variable and one or more independent variables which is called as profile. There are many approaches for monitoring different types of profiles in the literature. Most researchers assume that the response variable follows a normal distribution. However, this assumption may be violated in many cases. The most likely situation is when the response variable follows a distribution from generalized linear models (GLMs). For example, when the response variable is the number of defects in a certain area of a product, the observations follow Poisson distribution and ignoring this fact will cause misleading results. In this paper, three methods including a T2-based method, likelihood ratio test (LRT) method and F method are developed and modified in order to be applied in monitoring GLM regression profiles in Phase I. The performance of the proposed methods is analysed and compared for the special case that the response variable follows Poisson distribution. A simulation study is done regarding the probability of the signal criterion. Results show that the LRT method performs better than two other methods and the F method performs better than the T2-based method in detecting either small or large step shifts as well as drifts. Moreover, the F method performs better than the other two methods, and the LRT method performs poor in comparison with the F and T2-based methods in detecting outliers. A real case, in which the size and number of agglomerates ejected from a volcano in successive days form the GLM profile, is illustrated and the proposed methods are applied to determine whether the number of agglomerates of each size is under statistical control or not. Results showed that the proposed methods could handle the mentioned situation and distinguish the out-of-control conditions.

Iceberg Detection Using Simulated Dual-Polarized Radarsat Constellation Data

Iceberg monitoring is an important application of synthetic aperture radar (SAR), and is one of the stated objectives of the Radarsat Constellation, the next generation of Canada’s Radarsat satellites. In this paper, we simulate Radarsat Constellation data in a number of different imaging modes, using Radarsat-2 single-look complex data covering a study area in the Labrador Sea. We test the iceberg detection performance of both linear dual-pol data as well as compact polarimetry, a novel SAR architecture that transmits circular polarization rather than the traditional horizontal or vertical polarizations. We use the likelihood ratio test method to calculate a decision variable image for each of a number of different dual-pol and compact configurations, then analyze the detection performance using 25 validated iceberg locations spread across 12 different scenes. We found that compared to the linear data, the compact data missed fewer targets, and detected a greater number of pixels of detected targets, for most of the incidence angles and imaging modes tested. Compact polarimetry seems to be a promising choice for iceberg detection applications.

Likelihood ratio based tests for longitudinal drug safety data.

This article presents longitudinal likelihood ratio test (LongLRT) methods for large databases with exposure information. These methods are applied to a pooled large longitudinal clinical trial dataset for drugs treating osteoporosis with concomitant use of proton pump inhibitors (PPIs). When the interest is in the evaluation of a signal of an adverse event for a particular drug compared with placebo or a comparator, the special case of the LongLRT, referred to as sequential LRT (SeqLRT), is also presented. The results show that there is some possible evidence of concomitant use of PPIs leading to more adverse events associated with osteoporosis. The performance of the proposed LongLRT and SeqLRT methods is evaluated using simulated datasets and shown to be good in terms of (conditional) power and control of type I error over time. The proposed methods can also be applied to large observational databases with exposure information under the US Food and Drug Administration Sentinel Initiative for active surveillance. Published 2014. This article is a US Government work and is in the public domain in the USA.

An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI.

An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.

A comparative study on fault detection methods of rail vehicle suspension systems based on acceleration measurements

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. On-line health condition monitoring for the suspension system of rail vehicles offers a number of benefits such as preventing further deterioration of vehicle performance, enhancing vehicle safety, increasing operational reliability and availability, and reducing maintenance costs. It is desirable to timely detect the fault and monitor the performance degradation of vehicle suspension systems. In this paper, a comparative study on fault detection methods of urban rail vehicle suspension systems is considered. A novel sensor configuration is proposed where the underlying vehicle system is equipped with only acceleration sensors in the four corners of the carbody, the leading and trailing bogie, respectively. A mathematical model is developed for the considered vehicle suspension system. Both model-based and data-driven approaches are studied for the suspension fault detection problem. The robust observer, the Kalman filter combined with the generalised likelihood ratio test method, the dynamical principle components analysis and the canonical variate analysis approaches are applied to the fault detection problem. The simulation is carried out by means of the professional multi-body simulation tool, SIMPACK. In addition, the advantages and disadvantages of these methods are compared. The simulation results show that the data-driven methods outperform the model-based methods.

Spectrum Sensing for OFDM Signals Using Pilot Induced Auto-Correlations

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

Likelihood Ratio Test-Based Method for Signal Detection in Drug Classes Using FDA's AERS Database

In 1968 the Food and Drug Administration (FDA) established the Adverse Event Reporting System (AERS) database containing data on adverse events (AEs) reported by patients, health care providers, and other sources through a spontaneous reporting system. FDA uses AERS for monitoring the safety of the drugs on the market after approval. Most statistical methods that are available in the literature to analyze large postmarket drug safety data for identifying drug–event combinations with disproportionately high frequencies are designed to explore signals of a single drug–AE combination, but not signals including a drug class or a group of AEs simultaneously. Those methods are also not designed to control type I error and are subject to high false discovery rates. In this paper, we first briefly review a recently developed method, known as the likelihood ratio test (LRT)-based method, which has been demonstrated to control the family-wise type I error and false discovery rates. By introducing a concept of weight matrix for the drugs (or for AEs), we then extend the LRT method for detecting signals including a class of drugs (or AEs) in addition to detecting signals of single drug (or AE). A simplified Bayesian method is also proposed and compared with LRT method. The proposed methods are applied to study the signal patterns of drug classes, namely, the gadolinium drug class for magnetic resonance imaging (MRI) and statins for hypercholesterolemia, over different time periods, using the datasets with only suspect drugs and with both suspect and concomitant drugs from the AERS database. The signals detected by the statistical methods can be confirmed by signals detected across different databases, existing medical evidence from research or regulatory resources, prospective biological studies, and also through simulation as illustrated in the application.

Iceberg Detection Using Compact Polarimetric Synthetic Aperture Radar

In recent years, the use of synthetic aperture radar (SAR) for iceberg detection has been increasing thanks to the greater availability and coverage of SAR data, particularly polarimetric data. Greater amounts of polarimetric information can increase detection performance, preventing false alarms and missed detections. However, quad-polarization (quad-pol) SAR systems have increased data rate and power usage requirements, causing quad-pol modes to have generally half the swath width of dual-polarization (dual-pol) modes. Compact polarimetry is a compromise that allows the approximation of quad-pol information (referred to as “pseudo quad-pol”) using a dual-pol SAR. In this paper, using compact polarimetric data simulated from RADARSAT-2 quad-pol imagery, we show how pseudo-quad-pol data can be used for iceberg detection using the likelihood ratio test method. We show that use of the pseudo-quad-pol HV intensity can augment the detection performance of a compact polarimetric SAR system. We also compare the performance of the various compact polarimetric configurations to linearly polarized dual-pol and quad-pol data. [Traduit par la rédaction] Ces dernières années, le radar à synthèse d'ouverture (RSO) a été de plus en plus utilisé pour la détection des icebergs grâce à une meilleure disponibilité et une meilleure couverture des données RSO, en particulier des données polarimétriques. De plus grandes quantités d'informations polarimétiques peuvent améliorer la performance de détection, ce qui évite les fausses alarmes et les détections manquées. Cependant, les systèmes RSO à quadruple polarisation (quad-pol) exigent des débits binaires et une consommation d’énergie plus élevés, de telle sorte que les modes quad-pol utilisent généralement une largeur de couloir la moitié moindre que celle des modes à double polarisation (dual-pol). La polarimétrie compacte est un compromis qui permet d'approximer l'information quad-pol (alors appelée « pseudo-quad-pol ») au moyen d'un RSO dual-pol. Dans cet article, en utilisant des données de polarimétrie compacte simulées à partir de l'imagerie RADARSAT-2 quad-pol, nous montrons comment les données pseudo-quad-pol peuvent servir à la détection des icebergs au moyen de la méthode du test du rapport des vraisemblances. Nous montrons que l'utilisation de l'intensité HV pseudo-quad-pol peut augmenter la performance de détection d'un système RSO polarimétrique compact. Nous comparons aussi la performance des diverses configurations polarimétriques compactes aux données dual-pol et quad-pol linéairement polarisées.

Asymptotic Performance of Sparse Signal Detection Using Convex Programming Method

The detection of sparse signals against background noise is considered. Detecting signals of such kind is difficult since only a small portion of the signal carries information. Prior knowledge is usually assumed to ease detection. In this paper, we consider the general unknown and arbitrary sparse signal detection problem when no prior knowledge is available. Under a Neyman-Pearson hypothesis-testing framework, a new detection scheme is proposed by combining a generalized likelihood ratio test (GLRT)-like test statistic and convex programming methods which directly exploit sparsity in an underdetermined system of linear equations. We characterize large sample behavior of the proposed method by analyzing its asymptotic performance. Specifically, we give the condition for the Chernoff-consistent detection which shows that the proposed method is very sensitive to the ℓ2 norm energy of the sparse signals. Both the false alarm rate and the miss rate tend to zero at vanishing signal-to-noise ratio (SNR), as long as the signal energy grows at least logarithmically with the problem dimension. Next we give a large deviation analysis to characterize the error exponent for the Neyman-Pearson detection. We derive the oracle error exponent assuming signal knowledge. Then we explicitly derive the error exponent of the proposed scheme and compare it with the oracle exponent. We complement our study with numerical experiments, showing that the proposed method performs in the vicinity of the likelihood ratio test (LRT) method in the finite sample scenario and the error probability degrades exponentially with the number of observations.

GPR-Based Landmine Detection and Identification Using Multiple Features

This paper presents a method to identify landmines in various burial conditions. A ground penetration radar is used to generate data set, which is then processed to reduce the ground effect and noise to obtain landmine signals. Principal components and Fourier coefficients of the landmine signals are computed, which are used as features of each landmine for detection and identification. A database is constructed based on the features of various types of landmines and the ground conditions, including the different levels of moisture and types of ground and the burial depths of the landmines. Detection and identification is performed by searching for features in the database. For a robust decision, the counting method and the Mahalanobis distance-based likelihood ratio test method are employed. Four landmines, different in size and material, are considered as examples that demonstrate the efficiency of the proposed method for detecting and identifying landmines.

A Likelihood Ratio Test Based Method for Signal Detection With Application to FDA’s Drug Safety Data

Several statistical methods that are available in the literature to analyze postmarket safety databases, such as the U.S. Federal Drug Administration’s (FDA) adverse event reporting system (AERS), for identifying drug-event combinations with disproportionately high frequencies, are subject to high false discovery rates. Here, we propose a likelihood ratio test (LRT) based method and show, via an extensive simulation study, that the proposed method while retaining good power and sensitivity for identifying signals, controls both the Type I error and false discovery rates. The application of the LRT method to the AERS database is illustrated using two datasets; a small dataset consisting of suicidal behavior and mood change-related AE cases for the drug Montelukast, and a large dataset consisting of all possible AE cases reported to FDA during 2004–2008 for the drug Heparin. This article has supplementary material online.