Using cross-validation methods to select time series models: Promises and pitfalls.

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross-validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10-fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person-mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice.

Automated component analysis in DOSY NMR using information criteria.

This study introduces a model selection technique based on Bayesian information criteria for estimating the number of components in a mixture during Diffusion-Ordered Spectroscopy (DOSY) Nuclear Magnetic Resonance (NMR) data analysis. As the accuracy of this technique is dependent on the efficiency of parameter estimators, we further investigate the performance of the Weighted Least Squares (WLS) and Maximum a Posteriori (MAP) estimators. The WLS method, enhanced with meticulously tuned L2-regularization, effectively detects components when the difference in self-diffusion coefficients is more than two-fold, especially when the component with the smaller coefficient has a larger weight ratio. The MAP method, strengthened by a substantial database of prior information, exhibits outstanding precision, decreasing this threshold to 1.5 times. Both estimators provide weight ratio estimates with standard deviations of approximately around 1 percentage point, although the MAP method tends to overestimate the component with a larger self-diffusion coefficient. Deviations from the expected values can exceed 10 percentage points, often due to inaccuracies in component detection. The error estimates are determined using data resampling techniques derived from a large-scale 1000-point experiment and an additional five measurements from a single-component mixture. This approach allowed us to thoroughly examine data distribution characteristics, thereby laying a robust groundwork for future refinement efforts.

Second Order Blind Identification of Event Related Potentials Sources.

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Consistent Model Selection Procedure for Random Coefficient INAR Models.

In the realm of time series data analysis, information criteria constructed on the basis of likelihood functions serve as crucial instruments for determining the appropriate lag order. However, the intricate structure of random coefficient integer-valued time series models, which are founded on thinning operators, complicates the establishment of likelihood functions. Consequently, employing information criteria such as AIC and BIC for model selection becomes problematic. This study introduces an innovative methodology that formulates a penalized criterion by utilizing the estimation equation within conditional least squares estimation, effectively addressing the aforementioned challenge. Initially, the asymptotic properties of the penalized criterion are derived, followed by a numerical simulation study and a comparative analysis. The findings from both theoretical examinations and simulation investigations reveal that this novel approach consistently selects variables under relatively relaxed conditions. Lastly, the applications of this method to infectious disease data and seismic frequency data produce satisfactory outcomes.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

A Model-free Variable Screening Method Based on Leverage Score.

With rapid advances in information technology, massive datasets are collected in all fields of science, such as biology, chemistry, and social science. Useful or meaningful information is extracted from these data often through statistical learning or model fitting. In massive datasets, both sample size and number of predictors can be large, in which case conventional methods face computational challenges. Recently, an innovative and effective sampling scheme based on leverage scores via singular value decompositions has been proposed to select rows of a design matrix as a surrogate of the full data in linear regression. Analogously, variable screening can be viewed as selecting rows of the design matrix. However, effective variable selection along this line of thinking remains elusive. In this article, we bridge this gap to propose a weighted leverage variable screening method by utilizing both the left and right singular vectors of the design matrix. We show theoretically and empirically that the predictors selected using our method can consistently include true predictors not only for linear models but also for complicated general index models. Extensive simulation studies show that the weighted leverage screening method is highly computationally efficient and effective. We also demonstrate its success in identifying carcinoma related genes using spatial transcriptome data.

Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.

BACKGROUND: This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification of risk factors for timely diagnosis and improved patient outcomes. The researchers conducted a literature review to address previous studies' limitations in using machine learning to assess CVD risk factors. METHODS: This study analyzed data from 1024 patients to identify the significant CVD risk factors based on sex. The data comprising 13 features, such as demographic, lifestyle, and clinical factors, were obtained from the UCI repository and preprocessed to eliminate missing information. The analysis was performed using principal component analysis (PCA) and latent class analysis (LCA) to determine the major CVD risk factors and to identify any homogeneous subgroups between male and female patients. Data analysis was performed using XLSTAT Software. This software provides a comprehensive suite of tools for Data Analysis, Machine Learning, and Statistical Solutions for MS Excel. RESULTS: This study showed significant sex differences in CVD risk factors. 8 out of 13 risk factors affecting male and female patients found that males and females share 4 of the eight risk factors. Identified latent profiles of CVD patients, suggesting the presence of subgroups among CVD patients. These findings provide valuable insights into the impact of sex differences on CVD risk factors. Moreover, they have important implications for healthcare professionals, who can use this information to develop individualized prevention and treatment plans. The results highlight the need for further research to elucidate these disparities better and develop more effective CVD prevention measures. CONCLUSIONS: The study explored the sex differences in the CVD risk factors and the presence of subgroups among CVD patients using ML techniques. The results revealed sex-specific differences in risk factors and the existence of subgroups among CVD patients, thus providing essential insights for personalized prevention and treatment plans. Hence, further research is necessary to understand these disparities better and improve CVD prevention.

Variable selection using a smooth information criterion for distributional regression models.

Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the least absolute shrinkage and selection operator, the use of which requires selecting the value of a tuning parameter. This parameter is typically tuned by minimizing the cross-validation error or Bayesian information criterion, but this can be computationally intensive as it involves fitting an array of different models and selecting the best one. In contrast with this standard approach, we have developed a procedure based on the so-called "smooth IC" (SIC) in which the tuning parameter is automatically selected in one step. We also extend this model selection procedure to the distributional regression framework, which is more flexible than classical regression modelling. Distributional regression, also known as multiparameter regression, introduces flexibility by taking account of the effect of covariates through multiple distributional parameters simultaneously, e.g., mean and variance. These models are useful in the context of normal linear regression when the process under study exhibits heteroscedastic behaviour. Reformulating the distributional regression estimation problem in terms of penalized likelihood enables us to take advantage of the close relationship between model selection criteria and penalization. Utilizing the SIC is computationally advantageous, as it obviates the issue of having to choose multiple tuning parameters. Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-023-10204-8.

Prediction of the COVID-19 transmission: a case study of Pakistan.

The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We've used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.

Information criteria for latent factor models: a study on factor pervasiveness and adaptivity.

We study the information criteria extensively under general conditions for high-dimensional latent factor models. Upon carefully analyzing the estimation errors of the principal component analysis method, we establish theoretical results on the estimation accuracy of the latent factor scores, incorporating the impact from possibly weak factor pervasiveness; our analysis does not require the same factor strength of all the leading factors. To estimate the number of the latent factors, we propose a new penalty specification with a two-fold consideration: i) being adaptive to the strength of the factor pervasiveness, and ii) favoring more parsimonious models. Our theory establishes the validity of the proposed approach under general conditions. Additionally, we construct examples to demonstrate that when the factor strength is too weak, scenarios exist such that no information criterion can consistently identify the latent factors. We illustrate the performance of the proposed adaptive information criteria with extensive numerical examples, including simulations and a real data analysis.

Evidence of an Absence of Inbreeding Depression in a Wild Population of Weddell Seals (Leptonychotes weddellii).

Inbreeding depression can reduce the viability of wild populations. Detecting inbreeding depression in the wild is difficult; developing accurate estimates of inbreeding can be time and labor intensive. In this study, we used a two-step modeling procedure to incorporate uncertainty inherent in estimating individual inbreeding coefficients from multilocus genotypes into estimates of inbreeding depression in a population of Weddell seals (Leptonychotes weddellii). The two-step modeling procedure presented in this paper provides a method for estimating the magnitude of a known source of error, which is assumed absent in classic regression models, and incorporating this error into inferences about inbreeding depression. The method is essentially an errors-in-variables regression with non-normal errors in both the dependent and independent variables. These models, therefore, allow for a better evaluation of the uncertainty surrounding the biological importance of inbreeding depression in non-pedigreed wild populations. For this study we genotyped 154 adult female seals from the population in Erebus Bay, Antarctica, at 29 microsatellite loci, 12 of which are novel. We used a statistical evidence approach to inference rather than hypothesis testing because the discovery of both low and high levels of inbreeding are of scientific interest. We found evidence for an absence of inbreeding depression in lifetime reproductive success, adult survival, age at maturity, and the reproductive interval of female seals in this population.

Improving the Performance and Stability of TIC and ICE.

Takeuchi's Information Criterion (TIC) was introduced as a generalization of Akaike's Information Criterion (AIC) in 1976. Though TIC avoids many of AIC's strict requirements and assumptions, it is only rarely used. One of the reasons for this is that the trace term introduced in TIC is numerically unstable and computationally expensive to compute. An extension of TIC called ICE was published in 2021, which allows this trace term to be used for model fitting (where it was primarily compared to L2 regularization) instead of just model selection. That paper also examined numerically stable and computationally efficient approximations that could be applied to TIC or ICE, but these approximations were only examined on small synthetic models. This paper applies and extends these approximations to larger models on real datasets for both TIC and ICE. This work shows the practical models may use TIC and ICE in a numerically stable way to achieve superior results at a reasonable computational cost.

Predictors of time to first symptomatic recovery of major depressive disordered patients: a case study at Jimma University Medical Center.

BACKGROUND: Major Depressive Disorder is one of the most common mental disorders, and it is the main cause of disability worldwide with a prevalence ranging from 7 to 21%. OBJECTIVE: The goal of this study was to predict the time it took for patients with severe depressive disorders at Jimma University Medical Center to experience their initial symptomatic recovery. STUDY DESIGN: The researchers utilized a prospective study design. METHODS: Patients with major depressive disorder were followed up on at Jimma University Medical Center from September 2018 to August 2020 for this study. The Gamma and Inverse Gaussian frailty distributions were employed with Weibull, Log-logistic, and Log-normal as baseline hazard functions. Akaike Information Criteria were used to choose the best model for describing the data. RESULTS: This study comprised 366 patients, with 54.1% of them experiencing their first symptomatic recovery from a severe depressive disorder. The median time from the onset of symptoms to symptomatic recovery was 7 months. In the study area, there was a clustering effect in terms of time to first symptomatic recovery from major depressive disorder. According to the Log-normal Inverse-Gaussian frailty model, marital status, chewing khat, educational status, work status, substance addiction, and other co-variables were significant predictors of major depressive disorder (p-value < 0.05). CONCLUSION: The best model for describing the time to the first symptomatic recovery of major depressive disorder is the log-normal Inverse-Gaussian frailty model. Being educated and working considerably were the variables that reduces the time to first symptomatic recovery from major depressive disorder; whereas being divorced, chewing khat, substance abused and other co-factors were the variables that significantly extends the time to first symptomatic recovery.

Effects of socioeconomic status on excess mortality in patients with multiple sclerosis in France: A retrospective observational cohort study.

Background: The effects of socio-economic status on mortality in patients with multiple sclerosis is not well known. The objective was to examine mortality due to multiple sclerosis according to socio-economic status. Methods: A retrospective observational cohort design was used with recruitment from 18 French multiple sclerosis expert centers participating in the Observatoire Français de la Sclérose en Plaques. All patients lived in metropolitan France and had a definite or probable diagnosis of multiple sclerosis according to either Poser or McDonald criteria with an onset of disease between 1960 and 2015. Initial phenotype was either relapsing-onset or primary progressive onset. Vital status was updated on January 1st 2016. Socio-economic status was measured by an ecological index, the European Deprivation Index and was attributed to each patient according to their home address. Excess death rates were studied according to socio-economic status using additive excess hazard models with multidimensional penalised splines. The initial hypothesis was a potential socio-economic gradient in excess mortality. Findings: A total of 34,169 multiple sclerosis patients were included (88% relapsing onset (n = 30,083), 12% progressive onset (n = 4086)), female/male sex ratio 2.7 for relapsing-onset and 1.3 for progressive-onset). Mean age at disease onset was 31.6 (SD = 9.8) for relapsing-onset and 42.7 (SD = 10.8) for progressive-onset. At the end of follow-up, 1849 patients had died (4.4% for relapsing-onset (n = 1311) and 13.2% for progressive-onset (n = 538)). A socio-economic gradient was found for relapsing-onset patients; more deprived patients had a greater excess death rate. At thirty years of disease duration and a year of onset of symptoms of 1980, survival probability difference (or deprivation gap) between less deprived relapsing-onset patients (EDI = -6) and more deprived relapsing-onset patients (EDI = 12) was 16.6% (95% confidence interval (CI) [10.3%-22.9%]) for men and 12.3% (95%CI [7.6%-17.0%]) for women. No clear socio-economic mortality gradient was found in progressive-onset patients. Interpretation: Socio-economic status was associated with mortality due to multiple sclerosis in relapsing-onset patients. Improvements in overall care of more socio-economically deprived patients with multiple sclerosis could help reduce these socio-economic inequalities in multiple sclerosis-related mortality. Funding: This study was funded by the ARSEP foundation "Fondation pour l'aide à la recherche sur la Sclérose en Plaques" (Grant Reference Number 1122). Data collection has been supported by a grant provided by the French State and handled by the "Agence Nationale de la Recherche," within the framework of the "Investments for the Future" programme, under the reference ANR-10-COHO-002, Observatoire Français de la Sclérose en Plaques (OFSEP).

Utility of the Comprehensive Health and Stringency Indexes in Evaluating Government Responses for Containing the Spread of COVID-19 in India: Ecological Time-Series Study.

BACKGROUND: Many nations swiftly designed and executed government policies to contain the rapid rise in COVID-19 cases. Government actions can be broadly segmented as movement and mass gathering restrictions (such as travel restrictions and lockdown), public awareness (such as face covering and hand washing), emergency health care investment, and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government, University of Oxford, tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using the Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the COVID-19 pandemic. OBJECTIVE: This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of COVID-19. We expect a significant inverse relationship between policy indices (CHI and SI) and COVID-19 severity indices (morbidity and mortality). METHODS: In this ecological study, we analyzed data from 2 publicly available data sources released between March 2020 and October 2021: the Oxford Covid-19 Government Response Tracker and the World Health Organization. We used autoregressive integrated moving average (ARIMA) and seasonal ARIMA to model the data. The performance of different models was assessed using a combination of evaluation criteria: adjusted R2, root mean square error, and Bayesian information criteria. RESULTS: implementation of policies by the government to contain the COVID-19 crises resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell, they were consistently higher at values of >80% points. During the initial investigation, we found that cases per million (CPM) and deaths per million (DPM) followed the same trend. However, the final CPM and DPM models were seasonal ARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. This study does not support the hypothesis that COVID-19 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI). CONCLUSIONS: Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. Twenty-first-century problems thus demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage, and data communication. Thus, digital epidemiology can and should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation.

Bayesian spatio-temporal modelling and mapping of malaria and anaemia among children between 0 and 59 months in Nigeria.

BACKGROUND/M&M: A vital aspect of disease management and policy making lies in the understanding of the universal distribution of diseases. Nevertheless, due to differences all-over host groups and space-time outbreak activities, data are subject to intricacies. Herein, Bayesian spatio-temporal models were proposed to model and map malaria and anaemia risk ratio in space and time as well as to ascertain risk factors related to these diseases and the most endemic states in Nigeria. Parameter estimation was performed by employing the R-integrated nested Laplace approximation (INLA) package and Deviance Information Criteria were applied to select the best model. RESULTS: In malaria, model 7 which basically suggests that previous trend of an event cannot account for future trend i.e., Interaction with one random time effect (random walk) has the least deviance. On the other hand, model 6 assumes that previous event can be used to predict future event i.e., (Interaction with one random time effect (ar1)) gave the least deviance in anaemia. DISCUSSION: For malaria and anaemia, models 7 and 6 were selected to model and map these diseases in Nigeria, because these models have the capacity to receive strength from adjacent states, in a manner that neighbouring states have the same risk. Changes in risk and clustering with a high record of these diseases among states in Nigeria was observed. However, despite these changes, the total risk of malaria and anaemia for 2010 and 2015 was unaffected. CONCLUSION: Notwithstanding the methods applied, this study will be valuable to the advancement of a spatio-temporal approach for analyzing malaria and anaemia risk in Nigeria.

Less than one-fifth of Ethiopian children were vaccinated for measles second dose; evidence from the Ethiopian mini demographic and health survey 2019.

Ethiopia introduced the measles second dose vaccine from the routine expanded immunization program in 2018. Shreds of evidence are scarce on the measles second dose vaccination coverage and its associated factors in Ethiopia. We aimed to assess the measles second dose vaccination coverage and associated factors in Ethiopia using the recent Ethiopian Mini Demographic and Health Survey (EMDHS) 2019 data. An in-depth secondary data analysis was conducted based on the Ethiopian mini demographic and health survey 2019 data; which was a cross-sectional survey targeted on key indicators of maternal and child health. A weighted sample of 965 children was included in the analysis. A multi-level mixed effect logistics regression model was fitted. Adjusted Odds Ratio (AOR) with 95 %CI was reported for statistically significant variables. The measles second dose coverage was 12.36 % (95 %CI = 10.89, 15.44). Not vaccinated for the third dose of pentavalent vaccine (Penta 3) (AOR = 0.60, 95 %CI: 0.37, 0.95), age of the child [13 to 23 months (AOR = 2.14, 95 %CI: 1.05, 4.36), 24 to 36 months (AOR = 2.58, 95 %CI: 1.32, 5.05)], household head educational status [no education (AOR = 0.51,95 %CI: 0.26, 0.99), primary (AOR = 0.44, 95 %CI: 0.23, 0.85)], and living in south nation, nationalities and peoples region (SNNPR) (AOR = 2.83,95 %CI: 1.12, 7.11) were significantly associated with measles second dose vaccination coverage. Measles second dose vaccination coverage was low in Ethiopia. Age of the child, being vaccinated for the Penta 3, educational status of the household head, and region of residence were significant determinants of measles second dose vaccination coverage.

Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.

Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs. The algorithm, called SPA, applies a "GRN information criterion" (GRNIC) that is inspired by two commonly used model selection criteria, Akaike and Bayesian Information Criterion (AIC and BIC) but adapted to GRN inference. The results show that the approach can, in most cases, find the GRN whose sparsity is close to the true sparsity and close to as accurate as possible with the given GRN inference method and data. The datasets and source code can be found at https://bitbucket.org/sonnhammergrni/spa/.

Prevalence, Predictors, and Outcomes of Esophageal Candidiasis in Cirrhosis: An Observational Study With Systematic Review and Meta-Analysis (CANDID-VIEW).

BACKGROUND: Gastrointestinal candidiasis is often neglected and potentially serious infection in cirrhosis patients. Therefore, we evaluated the prevalence, risk factors, and outcomes of esophageal candidiasis (EC) in cirrhotics and did a systematic review to summarize EC's available evidence in cirrhosis. METHODS: Consecutive patients with cirrhosis posted for esophagogastroduodenoscopy (EGD) at a tertiary care institute were screened for EC (cases) between January 2019 and March 2020. EC was diagnosed on EGD findings and/or brush cytology. Controls (without EC) were recruited randomly, and EC's risk factors and outcomes were compared between cases and controls.Four electronic databases were searched for studies describing EC in cirrhosis. Prevalence estimates of EC were pooled on random-effects meta-analysis, and heterogeneity was assessed by I2. A checklist for prevalence studies was used to evaluate the risk of bias in studies. RESULTS: EC was diagnosed in 100 of 2762 patients with cirrhosis (3.6%). Patients with EC had a higher model for end-stage liver disease (MELD) (12.4 vs. 11.2; P = 0.007), acute-on-chronic liver failure (ACLF) (26% vs. 10%; P = 0.003) and concomitant bacterial infections (24% vs. 7%; P = 0.001), as compared with controls. A multivariable model, including recent alcohol binge, hepatocellular carcinoma (HCC), upper gastrointestinal (UGI) bleed, ACLF, diabetes, and MELD, predicted EC's development in cirrhosis with excellent discrimination (C-index: 0.918). Six percent of cases developed the invasive disease and worsened with multiorgan failures, and four patients with EC died on follow-up.Of 236 articles identified, EC's pooled prevalence from 8 studies (all with low-risk of bias) was 2.1% (95% CI: 0.8-5.8). Risk factors and outcomes of EC in cirrhosis were not reported in the literature. CONCLUSIONS: EC is not a rare infection in cirrhosis patients, and it may predispose to invasive candidiasis and untimely deaths. Alcohol binge, HCC, UGI bleed, ACLF, diabetes, and higher MELD are the independent predictors of EC in cirrhosis. At-risk patients with cirrhosis or those with deglutition symptoms should be rapidly screened and treated for EC.

Feature Selection in High-Dimensional Models via EBIC with Energy Distance Correlation.

In this paper, the LASSO method with extended Bayesian information criteria (EBIC) for feature selection in high-dimensional models is studied. We propose the use of the energy distance correlation in place of the ordinary correlation coefficient to measure the dependence of two variables. The energy distance correlation detects linear and non-linear association between two variables, unlike the ordinary correlation coefficient, which detects only linear association. EBIC is adopted as the stopping criterion. It is shown that the new method is more powerful than Luo and Chen's method for feature selection. This is demonstrated by simulation studies and illustrated by a real-life example. It is also proved that the new algorithm is selection-consistent.