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Bayesian shrinkage methods have been widely employed to perform variable selection with high-dimensional data. However, the presence of missing data hinders the implementation of these methods. Since complete case analyses can lead to biased estimates, applicable and efficient methods of variable selection with imputation are needed to obtain valid results. In order to address this issue, we propose an algorithm that employs the horseshoe shrinkage prior for shrinkage and multiple imputation for missing data in high-dimensional settings with a practical suggestion on model selection decision strategy. Simulation studies and real data analyses are presented and compared with those of other possible approaches. The simulation results suggest that the proposed algorithm can be considered as a general strategy for model selection of incomplete continuous data. [ABSTRACT FROM AUTHOR]

Missing values are common, but dealing with them by inappropriate method may lead to large classification errors. Empirical evidences show that the tree‐based classification algorithms such as classification and regression tree (CART) can benefit from imputation, especially multiple imputation. Nevertheless, less attention has been paid to incorporating multiple imputation into cost‐sensitive decision tree induction. This study focuses on the treatment of missing data based on a time‐constrained minimal‐cost tree algorithm. We introduce various approaches to handle incomplete data into the algorithm including complete‐case analysis, missing‐value branch, single imputation, feature acquisition, and multiple imputation. A simulation study under different scenarios examines the predictive performances of the proposed strategies. The simulation results show that the combination of the algorithm with multiple imputation can assure classification accuracy under the budget. A real medical data example provides insights into the problem of missing values in cost‐sensitive learning and the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

The site regression model (SREG) is utilized by plant breeders for analyzing multi-environment trials (MET) to examine the relationships among test environments, genotypes (G), and genotype-by-environment interactions (GE). SREG explores a matrix of G and GE by performing a singular value decomposition on the residuals matrix from a one-way ANOVA, requiring complete data. As missing values are common in MET, we propose two new imputation methods that implement an Expectation Maximization algorithm to fit the SREG model. To evaluate the impact on SREG model parameter estimation of these proposed methods and other competing imputation methods available, we conducted two studies using different approaches. One study involved simulated data while the other used a real dataset. In both studies, different measures to verify whether the joint effect of G plus GE is altered by imputation of data, and the reproducibility of missing data were evaluated. We also incorporated situations not commonly addressed in the literature, such as non-random structures of missing values and a big data situation. The proposed procedures provided estimators with good performance, maintaining superiority in several aspects studied, even when the competing imputation methods did not achieve convergence. Therefore, the new methods enabled incomplete MET data to be effectively analyzed by a SREG model. [ABSTRACT FROM AUTHOR]

Summary: Where the response variable in a big dataset is consistent with the variable of interest for small area estimation, the big data by itself can provide the estimates for small areas. These estimates are often subject to the coverage and measurement error bias inherited from the big data. However, if a probability survey of the same variable of interest is available, the survey data can be used as a training dataset to develop an algorithm to impute for the data missed by the big data and adjust for measurement errors. In this paper, we outline a methodology for such imputations based on an k‐nearest neighbours (kNN) algorithm calibrated to an asymptotically design‐unbiased estimate of the national total, and illustrate the use of a training dataset to estimate the imputation bias and the "fixed‐k asymptotic" bootstrap to estimate the variance of the small area hybrid estimator. We illustrate the methodology of this paper using a public‐use dataset and use it to compare the accuracy and precision of our hybrid estimator with the Fay–Harriot (FH) estimator. Finally, we also examine numerically the accuracy and precision of the FH estimator when the auxiliary variables used in the linking models are subject to undercoverage errors. [ABSTRACT FROM AUTHOR]

The article titled "Estimation of missing ordinal data: A comment on Wissler et al. (2022)" discusses the accuracy of multiple imputation methods for handling missing ordinal or continuous data. The authors comment on the accuracy metric used in the original study and propose an alternative metric that provides a more realistic assessment of imputation accuracy. They also evaluate the performance of one imputation method, predictive mean matching (PMM), on a dataset of fibrocartilaginous entheseal changes. The results show that PMM performs better on this dataset compared to the dataset used in the original study. The authors conclude by highlighting the importance of considering the number of stages in recording a trait and the intercorrelation among variables when imputing missing values. [Extracted from the article]

Background: Urinary incontinence (UI) might be linked to suicidal ideation, but we do not yet have all the relevant details. This study aimed to dig deeper into the connection between UI and suicidal ideation using data from the National Health and Nutrition Examination Survey (NHANES). Methods: We examined 31,891 participants aged ≥ 20 years from NHANES 2005–2018 who provided complete information. We used standardized surveys to check for UI and signs of suicidal ideation. To better understand this relationship, we used statistical tools such as multivariable logistic regression, subgroup analysis, and sensitivity analyses. Results: Among the 31,891 participants, 28.9% reported UI and 10.7% reported suicidal ideation. Those with UI exhibited a significantly greater incidence of suicidal ideation (15.5%) than did those without UI (8.8%, P < 0.001). After adjusting for various factors, including age, sex, marital status, socioeconomic status, educational level, lifestyle factors, and chronic comorbidities, UI remained significantly associated with suicidal ideation (OR:1.54, 95% CI = 1.39–1.7, P < 0.001). Among all types of UI, MUI participants were more likely to experience suicidal ideation. Compared with no UI, higher odds of suicidal ideation suffered from MUI (OR:2.11, 95%CI:1.83–2.44, P < 0.001), SUI (OR:1.4, 95%CI:1.19–1.65, P < 0.001), UUI(OR:1.37,95%CI:1.16–1.62, P < 0.001) after full adjustment. With the exception of individuals living with a partner, the remaining subgroups exhibited a positive correlation between urinary incontinence and suicidal ideation, considering that factors such as age, sex, and prevalent comorbidities such as hypertension, depression, and diabetes did not reveal any statistically significant interactions (all P > 0.05). Sensitivity analyses, incorporating imputed missing covariates, did not substantially alter the results (OR: 1.53, 95% CI: 1.4–1.68, P < 0.001). Conclusion: Urinary incontinence may correlate with increased suicidal ideation risk, priority screening for suicidal ideation and timely intervention are essential for individuals with urinary incontinence, but prospective studies are needed to verify the results. [ABSTRACT FROM AUTHOR]

As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators “p-hack” results within their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting “right-truncated meta-analysis” (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS. [ABSTRACT FROM AUTHOR]

This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maximum likelihood function induced by measurement errors. Additionally, a combination of inverse probability weighting (IPW) and mean imputation is utilized to mitigate the bias caused by missing data. Under several mild conditions, it is demonstrated that the proposed estimators are consistent and possess oracle properties. The efficacy of the proposed parameter estimation process is assessed through Monte Carlo simulation studies. Finally, the applicability of the proposed method is further substantiated using the Boston Housing Dataset. [ABSTRACT FROM AUTHOR]

An assessment of the causal treatment effect in the development and progression of certain diseases is important in clinical trials and biomedical studies. However, it is not possible to infer a causal relationship when the treatment assignment is imbalanced and confounded by other mechanisms. Specifically, when the treatment assignment is not randomized and the primary outcome is binary, a conventional logistic regression may not be valid to elucidate any causal inference. Moreover, exclusively capturing all confounders is extremely difficult and even impossible in large-scale observational studies. We propose a multiple-model-based robust (MultiMR) estimator for estimating the causal effect with a binary outcome, where multiple propensity score models and conditional mean imputation models are used to ensure estimation robustness. Furthermore, we propose an enhanced MultiMR (eMultiMR) estimator that reduces the estimation variability of MultiMR estimates by incorporating secondary outcomes that are highly correlated with the primary binary outcome. The resulting estimates are less sensitive to model mis-specification compared to those based on state-of-the-art doubly-robust methods. These estimates are verified through both theoretical and numerical assessments. The utility of (e)MultiMR estimation is illustrated using the Uniform Data Set (UDS) from the National Alzheimer's Coordinating Center with the objective of detecting the causal effect of the short-term use of antihypertensive medications on the development of dementia or mild cognitive impairment. The proposed method has been implemented in an R package and is available at. [ABSTRACT FROM AUTHOR]

Imputation of missing data in high-dimensional datasets with more variables P than samples N, P ≫ N , is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables. Thus, the high dimension requires special imputation approaches. In this article, we provide an overview and realistic comparisons of imputation approaches for high-dimensional data when applied to a linear mixed modeling (LMM) framework. We examine approaches from three different classes using simulation studies: multiple imputation with penalized regression, multiple imputation with recursive partitioning and predictive mean matching; and multiple imputation with Principal Component Analysis (PCA). We illustrate the methods on a real case study where a multivariate outcome (i.e., an extracted set of correlated biomarkers from human urine samples) was collected and monitored over time and we discuss the proposed methods with more standard imputation techniques that could be applied by ignoring either the multivariate or the longitudinal dimension. Our simulations demonstrate the superiority of the recursive partitioning and predictive mean matching algorithm over the other methods in terms of bias, mean squared error and coverage of the LMM parameter estimates when compared to those obtained from a data analysis without missingness, although it comes at the expense of high computational costs. It is worthwhile reconsidering much faster methodologies like the one relying on PCA. [ABSTRACT FROM AUTHOR]

This article compares different missing data methods in randomized controlled trials, specifically addressing cases involving joint missingness in the outcome and covariates. In the existing literature, it is still unclear how advanced methods like linear mixed model (LMM) and multiple imputation (MI) perform in comparison to simpler methods regarding the estimation of treatment effects and their standard errors. We therefore evaluates the performance of LMM and MI against simple alternatives across a wide range of simulation scenarios for various realistic missingness mechanisms. The results show that no single method universally outperforms the others. However, LMM followed by MI demonstrates superior performance across most missingness scenarios. Interestingly, a simple method that combines complete case analysis for the missing outcome and mean imputation for the missing covariate (CCAME) performs similarly to LMM and MI. All methods are furthermore compared in the context of a randomized controlled trial on chronic obstructive pulmonary disease. [ABSTRACT FROM AUTHOR]

Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics. [ABSTRACT FROM AUTHOR]

Groundwater-level monitoring networks provide vital information for hydrogeological studies. Including exploited domestic wells in these monitoring networks can provide a low-cost means of obtaining a broader set of data; however, the use of these sites is limited because the frequent pumping of these wells generates outliers in the recorded time series. Here a slope criterion is applied to identify and remove outliers from groundwater-level time series from exploited domestic wells. Nonetheless, eliminating outliers creates a problem of missing values, which biases the subsequent time series analysis. Thus, 14 imputation methods were used to replace the missing values. The proposed approach is applied to groundwater-level time series from a monitoring network of 20 wells in the Lanaudière region, Québec, Canada. The slope criterion proves very effective in identifying outliers in exploited domestic wells. Missing values generated by outlier removal can reach up to 99% of the recorded data. Among the characteristics of the missing value pattern, the gap size and the position of the gaps along the time series are the most important parameters that affect the performance of the 14 imputation methods. Of the imputation methods tested, linear interpolation and Stineman interpolation, and then Kalman filtering, were the most effective. The present study demonstrates that exploited domestic wells can be used for groundwater monitoring by removing the outliers and imputing the missing values. [ABSTRACT FROM AUTHOR]

Modern spatial temporal data are often collected from sensor networks. Missing data problems are common to this kind of data. Making accurate imputation is important for many applications. In the unsupervised setting, one technique is to minimize the rank of a tensor or matrix. If we add related covariates, can we get more accurate imputation results? To address this, we transform the original sensor×time measurements to high order tensors by adding additional temporal dimensions and then integrate tensor regression with tensor completion using nuclear norm penalty. One advantage is we can simultaneously estimate parameters and impute missing values due to clear spatial consistency for near-sited spatial-temporal data. The proposed method doesn't assume missing mechanism of the response. Theoretical properties of the proposed estimator are investigated. Simulation studies and real data analysis are conducted to verify the efficiency of the estimation procedure. [ABSTRACT FROM AUTHOR]

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy. [ABSTRACT FROM AUTHOR]

To address the challenges of limited line coverage, protracted planning durations, and elevated construction expenses inherent in traditional urban transportation network planning techniques, an urban transportation network planning method considering traffic accessibility is proposed. This method leverages multiple sensors to establish an urban traffic data collection framework, enabling the acquisition of pertinent traffic information. Data preprocessing steps, including clustering and missing data imputation, are executed, followed by the application of the Kalman filtering method to forecast urban traffic flow. Traffic accessibility is computed based on the spatial interaction model's relative accessibility, and this metric is integrated with traffic flow, fairness indicators for benefit allocation, the Gini coefficient for urban road resources, social cost sharing indicators, and urban transportation construction costs to formulate an urban transportation network planning model. The experimental outcomes reveal that the proposed approach achieves a maximum coverage of 80.1%, with a maximum planning time of 156.47 seconds and a total construction cost of 5.7076 million yuan. By comparing the literature results, its superiority is demonstrated. [ABSTRACT FROM AUTHOR]

Motivation As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. Results To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)—a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32 953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. Availability and implementation The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/ , along with a comprehensive user guide detailed in Supplementary Text S1. [ABSTRACT FROM AUTHOR]

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R. [ABSTRACT FROM AUTHOR]

Model-based health economic evaluations of ischemic stroke are in need of cost- and utility estimates related to relevant outcome measures. This study aims to describe societal cost- and utility estimates per modified Rankin Scale (mRS)-score at different time points within 2 years post stroke. Included patients had a stroke between 3 months and 2.5 years ago. mRS and EQ-5D-5L were scored during a telephone interview. Based on the interview date, records were categorized into a time point: 3 months (3M; 3-6 months), 1 year (Y1; 6-18 months), or 2 years (Y2; 18-30 months). Patients completed a questionnaire on healthcare utilization and productivity losses in the previous 3 months. Initial stroke hospitalization costs were assessed. Mean costs and utilities per mRS and time point were derived with multiple imputation nested in bootstrapping. Cost at 3 months post stroke were estimated separately for endovascular treatment (EVT)-/non-EVT-patients. 1106 patients were included from 18 Dutch centers. At each time point, higher mRS-scores were associated with increasing average costs and decreasing average utility. Mean societal costs at 3M ranged from €11 943 (mRS 1, no EVT) to €55 957 (mRS 5, no EVT). For Y1, mean costs in the previous 3 months ranged from €885 (mRS 0) to €23 215 (mRS 5), and from €1655 (mRS 0) to €22 904 (mRS 5) for Y2. Mean utilities ranged from 0.07 to 0.96, depending on mRS and time point. The mRS-score is a major determinant of costs and utilities at different post-stroke time points. Our estimates may be used to inform future model-based health economic evaluations. • Model-based health economic evaluations of acute ischemic stroke are in need of cost- and utility estimates related to relevant outcome measures, such as the modified Rankin Scale. • Using prospectively collected data, this cross-sectional study provides cost and utility estimates per modified Rankin Scale score at 3 months, 1 year, and 2 years post stroke. [ABSTRACT FROM AUTHOR]

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer's Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer's Disease, which is crucial for early disease detection and personalized treatment. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18–25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue. [ABSTRACT FROM AUTHOR]

The identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound can affect multiple proteins. To overcome this challenge, Batzilla et al. (PLoS Comput Biol 18(8): e1010438, 2022) proposed DepInfeR, a regularized multi-response regression model designed to identify and estimate specific molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles. Inspired by their work, we propose a Bayesian extension to DepInfeR. Our proposed approach offers several advantages over DepInfeR, including e.g. the ability to handle missing values in both protein-drug affinity and drug sensitivity profiles without the need for data pre-processing steps such as imputation. Moreover, our approach uses Gaussian Processes to capture more complex molecular dependency structures, and provides probabilistic statements about whether a protein in the protein-drug affinity profiles is informative to the drug sensitivity profiles. Simulation studies demonstrate that our proposed approach achieves better prediction accuracy, and is able to discover unreported dependency structures. [ABSTRACT FROM AUTHOR]

We propose a nested semiparametric model to analyze a case‐control study where genuine case status is missing for some individuals. The concept of a noncase is introduced to allow for the imputation of the missing genuine cases. The odds ratio parameter of the genuine cases compared to controls is of interest. The imputation procedure predicts the probability of being a genuine case compared to a noncase semiparametrically in a dimension reduction fashion. This procedure is flexible, and vastly generalizes the existing methods. We establish the root‐n$$ n $$ asymptotic normality of the odds ratio parameter estimator. Our method yields stable odds ratio parameter estimation owing to the application of an efficient semiparametric sufficient dimension reduction estimator. We conduct finite sample numerical simulations to illustrate the performance of our approach, and apply it to a dilated cardiomyopathy study. [ABSTRACT FROM AUTHOR]

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types. [ABSTRACT FROM AUTHOR]

Background: Obstetrical complications impact the health of mothers and offspring along the life course, resulting in an increased burden of chronic diseases. One specific complication is abruption, a life‐threatening condition with consequences for cardiovascular health that remains poorly studied. Objectives: To describe the design and data linkage algorithms for the Placental Abruption and Cardiovascular Event Risk (PACER) cohort. Population: All subjects who delivered in New Jersey, USA, between 1993 and 2020. Design: Retrospective, population‐based, birth cohort study. Methods: We linked the vital records data of foetal deaths and live births to delivery and all subsequent hospitalisations along the life course for birthing persons and newborns. The linkage was based on a probabilistic record‐matching algorithm. Preliminary Results: Over the 28 years of follow‐up, we identified 1,877,824 birthing persons with 3,093,241 deliveries (1.1%, n = 33,058 abruption prevalence). The linkage rates for live births‐hospitalisations and foetal deaths‐hospitalisations were 92.4% (n = 2,842,012) and 70.7% (n = 13,796), respectively, for the maternal cohort. The corresponding linkage rate for the live births‐hospitalisations for the offspring cohort was 70.3% (n = 2,160,736). The median (interquartile range) follow‐up for the maternal and offspring cohorts was 15.4 (8.1, 22.4) and 14.4 (7.4, 21.0) years, respectively. We will undertake multiple imputations for missing data and develop inverse probability weights to account for selection bias owing to unlinked records. Conclusions: Pregnancy offers a unique window to study chronic diseases along the life course and efforts to identify the aetiology of abruption may provide important insights into the causes of future CVD. This project presents an unprecedented opportunity to understand how abruption may predispose women and their offspring to develop CVD complications and chronic conditions later in life. [ABSTRACT FROM AUTHOR]

The sustainable operation of ambient air quality monitoring stations in developing countries is not always possible. Intermittent failures and breakdowns at air quality monitoring stations often affect the continuous measurement of data as required. These failures and breakdowns result in missing data. This study aimed to impute NO2, SO2, O3, and PM 10 to produce complete data sets of daily average exposures from 2010 to 2017. Models were built for (a) an individual pollutant at a monitoring station, (b) a combined model for the same pollutant from different stations, and (c) a data set with all the pollutants from all the monitoring stations. This study sought to evaluate the efficacy of the Multiple Imputation by Chain Equations (MICE) algorithm in successfully imputing air quality data that are missing at random. The application of classification and regression trees (CART) analysis using the MICE package in the R statistical programming language was compared with the predictive mean matching (PMM) method. The CART method performed better, with the pooled R-squared statistics of the imputed data ranging from 0.3 to 0.7, compared to a range of 0.02 to 0.25 for PMM. The MICE algorithm successfully resolved the incompleteness of the data. It was concluded that the CART method produced better reliable data than the PMM method. However, in this study, the pooled R2 values were accurate for NO2, but not so much for other pollutants. [ABSTRACT FROM AUTHOR]

Purpose: Random item effects item response theory (IRT) models have received much attention for more than a decade. However, more research is needed on random item effects IRT models for polytomous data. Additionally, to improve the utility of this new class of IRT models, the scoring issue must be addressed. Methods: We proposed a new random item effects generalized partial credit model (GPCM), which considers both random person and random item and category-specific effects. In addition, we introduced a multiple imputation (MI)-based scoring procedure that applies to various random item effects IRT models. To evaluate the proposed model and scoring procedure, we analyzed data from a Quality of Life (QoL) scale for the Chronically Mentally III and conducted a preliminary simulation study. Results: In the empirical data analysis, we found that patient scores generated based on the proposed model and scoring procedure were almost identical to those obtained through the conventional GPCM and scoring method. However, the standard errors (SEs) associated with the scores were slightly larger when the proposed approach was utilized. In the simulation study, we observed adequate recovery of the model parameters and patient scores. Conclusion: The proposed model and MI-based scoring procedure contribute to the literature. The proposed model substantially reduces the number of free parameters in comparison to a conventional GPCM, which can be desired when sample sizes are small, e.g., special populations. In addition, the MI-based scoring procedure addresses the scoring issue and can be easily extended for scoring with other random item effects IRT models. [ABSTRACT FROM AUTHOR]

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study. [ABSTRACT FROM AUTHOR]

According to recent regulatory guidance, clinical trial objectives should be translated into estimands, i.e., precise descriptions of that what is to be estimated. Hence, estimands are to be formulated for influenza vaccine immunogenicity (IVI) trials, notably for one of the most popular immunogenicity trials, the non-inferiority trial. In this paper an estimand for this trial design is proposed. An estimand should state how intercurrent events are handled. Intercurrent events are events that occur after the start of the trial and that affect the endpoint's measurement or interpretation or prevent its observation. In IVI trials the intercurrent events of interest are immunological intercurrent events (IIEs). Major IIEs are identified, i.e., protocol deviations occurring during the trial that affect immunogenicity endpoints, and the consequences for the trial data collection are discussed. In the statistical analysis endpoint values that are missing or excluded from the analysis due to IIEs are to be substituted by plausible values, by means of multiple imputing. Replacing values are based on predictors of the endpoint. A distinction is made between mandatory and non-mandatory predictors. Mandatory predictors are predictors necessary to prevent biased prediction. Non-mandatory predictors are predictors that limit the additional variance due to the imputing. The four steps of the multiple imputing are explained, and available software is listed. [ABSTRACT FROM AUTHOR]

Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an "oracle," can be constructed in such cases to decide the best procedure from a set C$$ \mathcal{C} $$ of available preparatory procedures. The oracle learns the decision regions associated with C$$ \mathcal{C} $$ based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit. [ABSTRACT FROM AUTHOR]

The use of whole‐genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome‐wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait‐associated single‐nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait‐associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%–2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP. [ABSTRACT FROM AUTHOR]

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated. [ABSTRACT FROM AUTHOR]

Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM's performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall's W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. [ABSTRACT FROM AUTHOR]

Non-response situations are inevitable in sample survey work, and this is also true in dual sampling frame. In view of this, this study proposes alternative imputation techniques to solve the problem of missing data and further suggests an estimate of the population total in dual sampling frame. The properties of the suggested estimator are analyzed, and its empirical results are compared to the entire response situation. Based on the results obtained, appropriate suggestions have been proposed to survey practitioners and researchers for their practical applications. [ABSTRACT FROM AUTHOR]

High-dimensional complex survey data of general structures (e.g. containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense's Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data. Imputation by fully conditional specification (FCS) is often considered the state of the art for such datasets due to its generality and flexibility. However, FCS procedures contain a theoretical flaw that is exposed by HRBS data—HRBS imputations created with FCS are shown to diverge across iterations of Markov Chain Monte Carlo. Imputation by joint modeling lacks this flaw; however, current joint modeling procedures are neither general nor flexible enough to handle HRBS data. As such, we introduce an algorithm that efficiently and flexibly applies multiple imputation by joint modeling in data of general structures. This procedure draws imputations from a latent joint multivariate normal model that underpins the generally structured data and models the latent data via a sequence of conditional linear models, the predictors of which can be specified by the user. We perform rigorous evaluations of HRBS imputations created with the new algorithm and show that they are convergent and of high quality. Lastly, simulations verify that the proposed method performs well compared to existing algorithms including FCS. [ABSTRACT FROM AUTHOR]

In many survey settings, population counts or percentages are available for some of the variables in the survey, for example, from censuses, administrative databases, or other high-quality surveys. We present a model-based approach to utilize such auxiliary marginal distributions in multiple imputation for unit and item nonresponse in complex surveys. In doing so, we ensure that the imputations produce design-based estimates that are plausible given the known margins. We introduce and utilize a hybrid missingness model comprising a pattern mixture model for unit nonresponse and selection models for item nonresponse. We also develop a computational strategy for estimating the parameters of and generating imputations with hybrid missingness models. We apply a hybrid missingness model to examine voter turnout by subgroups using the 2018 Current Population Survey for North Carolina. The hybrid missingness model also facilitates modeling measurement errors simultaneously with handling missing values. We illustrate this feature with the voter turnout application by examining how results change when we allow for overreporting, that is, individuals self-reporting that they voted when in fact they did not. [ABSTRACT FROM AUTHOR]

This article discusses the modeling of intraindividual variability (IIV) as predictors in longitudinal research. IIV refers to the short-term fluctuations of a variable, and it is commonly studied in psychology. The article explores different approaches to modeling IIV, including standard regression models and dynamic structural equation modeling (DSEM). The authors conducted a simulation study to compare the performance of these approaches and found that the one-step full Bayesian modeling approach generally performed well. The study aims to provide practical recommendations for accurately modeling IIV as predictors. [Extracted from the article]

The solution of the distribution system state estimation (DSSE) relies on the presence of physical measurements in real time. Sometimes, these measurements may not reach the control center due to the defects in meter functionality, the large communication time delays, and denial-of-service (DoS) attacks on communication channels. Addressing this issue, a novel deep learning (DL) approach is proposed by using a transformer model with the integration of bi-directional long short-term memory (Bi-LSTM) layer. The proposed model can be leveraged to forecast the unavailable measurement data, which maintains the observability of the system to obtain an accurate solution from DSSE. The superiority of the proposed model is probed by comparing it with other forecasting-based DL models at various percentages of missing measurement data. Further, the effectiveness of the proposed model is evaluated for forecasting the power injections of residential, commercial, and industrial loads as well as renewable energy sources. Finally, the solution of the DSSE is tested with forecasted data and compared with the results of the DSSE for the original measurement data. The simulations are performed on modified IEEE 13-node and IEEE 37-node test systems, and their corresponding results highlighted the superiority of the proposed model in the forecasting of the incomplete data. [ABSTRACT FROM AUTHOR]

Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study's findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention. [ABSTRACT FROM AUTHOR]

Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. Methods: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. Results: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. Conclusions: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data. [ABSTRACT FROM AUTHOR]

Motivation Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. Results To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. Availability and implementation Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan. [ABSTRACT FROM AUTHOR]

We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R obs and C obs . However, if the model for R given C includes random coefficients, interactions, or polynomial terms, their joint distribution will be nonstandard. We propose a family of unique factorizations involving selected "provisionally known random effects" u such that h (R obs , C obs | u) is normally distributed and u is a low-dimensional normal random vector; we approximate h (R obs , C obs) = ∫ h (R obs , C obs | u) g (u) du via adaptive Gauss-Hermite quadrature. For polynomial models, the approximation is exact but, in any case, can be made as accurate as required given sufficient computation time. The model incorporates random effects as explanatory variables, reducing bias due to measurement error. By construction, our factorizations solve problems of compatibility among fully conditional distributions that have arisen in Bayesian imputation based on the Gibbs Sampler. We spell out general rules for selecting u, and show that our factorizations can support fully compatible Bayesian methods of imputation using the Gibbs Sampler. for this article are available online. [ABSTRACT FROM AUTHOR]

Linear regression models are commonly used to determine the quantitative relationships between variables and utilize the resulting regression equations to make predictions. This paper proposes a fully informative multiple imputation method based on a linear regression model with a missing response variable, utilizing all observable data to obtain estimates of the regression coefficients and thereby the predicted values of the missing response variable. This not only provides a good explanation of the relationship between the response variable and their respective variables, but also effectively enhances the imputation accuracy of the response variable. The stability and sensitivity of the fiMI method are evaluated through a simulation study. Subsequently, the proposed method is applied to two real data sets, the admission prediction data set and the goalkeeper data set, and is discussed and analyzed. [ABSTRACT FROM AUTHOR]

The SNP‐HLA Reference Consortium (SHLARC), a component of the 18th International HLA and Immunogenetics Workshop, is aimed at collecting diverse and extensive human leukocyte antigen (HLA) data to create custom reference panels and enhance HLA imputation techniques. Genome‐wide association studies (GWAS) have significantly contributed to identifying genetic associations with various diseases. The HLA genomic region has emerged as the top locus in GWAS, particularly in immune‐related disorders. However, the limited information provided by single nucleotide polymorphisms (SNPs), the hallmark of GWAS, poses challenges, especially in the HLA region, where strong linkage disequilibrium (LD) spans several megabases. HLA imputation techniques have been developed using statistical inference in response to these challenges. These techniques enable the prediction of HLA alleles from genotyped GWAS SNPs. Here we present the SHLARC activities, a collaborative effort to create extensive, and multi‐ethnic reference panels to enhance HLA imputation accuracy. [ABSTRACT FROM AUTHOR]

Assessing donor/recipient HLA compatibility at the eplet level requires second field DNA typings but these are not always available. These can be estimated from lower‐resolution data either manually or with computational tools currently relying, at best, on data containing typing ambiguities. We gathered NGS typing data from 61,393 individuals in 17 French laboratories, for loci A, B, and C (100% of typings), DRB1 and DQB1 (95.5%), DQA1 (39.6%), DRB3/4/5, DPB1, and DPA1 (10.5%). We developed HaploSFHI, a modified iterative maximum likelihood algorithm, to impute second field HLA typings from low‐ or intermediate‐resolution ones. Compared with the reference tools HaploStats, HLA‐EMMA, and HLA‐Upgrade, HaploSFHI provided more accurate predictions across all loci on two French test sets and four European‐independent test sets. Only HaploSFHI could impute DQA1, and solely HaploSFHI and HaploStats provided DRB3/4/5 imputations. The improved performance of HaploSFHI was due to our local and nonambiguous data. We provided explanations for the most common imputation errors and pinpointed the variability of a low number of low‐resolution haplotypes. We thus provided guidance to select individuals for whom sequencing would optimize incompatibility assessment and cost‐effectiveness of HLA typing, considering not only well‐imputed second field typing(s) but also well‐imputed eplets. [ABSTRACT FROM AUTHOR]

A quick count seeks to estimate the voting trends of an election and communicate them to the population on the evening of the same day of the election. In quick counts, the sampling is based on a stratified design of polling stations. Voting information is gathered gradually, often with no guarantee of obtaining the complete sample or even information in all the strata. However, accurate interval estimates with partial information must be obtained. Furthermore, this becomes more challenging if the strata are additionally study domains. To produce partial estimates, two strategies are proposed: (1) a Bayesian model using a dynamic post-stratification strategy and a single imputation process defined after a thorough analysis of historic voting information; additionally, a credibility level correction is included to solve the underestimation of the variance and (2) a frequentist alternative that combines standard multiple imputation ideas with classic sampling techniques to obtain estimates under a missing information framework. Both solutions are illustrated and compared using information from the 2021 quick count. The aim was to estimate the composition of the Chamber of Deputies in Mexico. [ABSTRACT FROM AUTHOR]