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AbstractPattern-mixture models have received increasing attention as they are commonly used to assess treatment effects in primary or sensitivity analyses for clinical trials with nonignorable missing data. Pattern-mixture models have traditionally been implemented using multiple imputation, where the variance estimation may be a challenge because the Rubin’s approach of combining between- and within-imputation variance may not provide consistent variance estimation while bootstrap methods may be time-consuming. Direct likelihood-based approaches have been proposed in the literature and implemented for some pattern-mixture models, but the assumptions are sometimes restrictive and the theoretical framework is fragile. In this article, we propose an analytical framework for an efficient direct likelihood estimation method for commonly used pattern-mixture models corresponding to return-to-baseline, jump-to-reference, placebo washout, and retrieved dropout imputations. A parsimonious tipping point analysis is also discussed and implemented. Results from simulation studies demonstrate that the proposed methods provide consistent estimators. We further illustrate the utility of the proposed methods using data from a clinical trial evaluating a treatment for type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example. Methods: We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015–2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications. Results: High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies. Conclusions: We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data. [ABSTRACT FROM AUTHOR]

Mediation models are often conducted in psychology to understand mechanisms and processes of change. However, current best practices for handling missing data in mediation models are not always used by researchers. Missing data methods, such as full information maximum likelihood (FIML) and multiple imputation (MI), are best practice methods of handling missing data. However, FIML or MI are rarely used to handle missing data when testing mediation models, instead analyses used listwise deletion methods, the default in popular software. Compared to listwise deletion, the implementation of FIML or MI to handle missing data reduces parameter estimate bias, while maintaining the sample collected to maximise power and generalizability of results. In this tutorial, we review how to implement full‐information maximum likelihood and MI using best practice methods of testing the indirect effect. We demonstrate how to implement these methods using both R and JASP, which are both free, open‐source software programmes and provide online supplemental materials for these demonstrations. These methods are demonstrated using two example analyses, one using a cross‐sectional mediation model and one using a longitudinal mediation model examining how student‐athletes reported worry about COVID predicts their perceived stress, which in turn predicts satisfaction with life. [ABSTRACT FROM AUTHOR]

ABSTRACT Multiple primary endpoints are commonly used in randomized controlled trials to assess treatment effects. When the endpoints are measured on different scales, the O'Brien rank‐sum test or the Wei–Lachin test for stochastic ordering may be used for hypothesis testing. However, the O'Brien–Wei–Lachin (OWL) approach is unable to handle missing data and adjust for baseline measurements. We present a nonparametric approach for data analysis that encompasses the OWL approach as a special case. Our approach is based on quantifying an endpoint‐specific treatment effect using the probability that a participant in the treatment group has a better score than (or a win over) a participant in the control group. The average of the endpoint‐specific win probabilities (WinPs), termed the global win probability (gWinP), is used to quantify the global treatment effect, with the null hypothesis gWinP = 0.50. Our approach involves converting the data for each endpoint to endpoint‐specific win fractions, and modeling the win fractions using multivariate linear mixed models to obtain estimates of the endpoint‐specific WinPs and the associated variance–covariance matrix. Focusing on confidence interval estimation for the gWinP, we derive sample size formulas for clinical trial design. Simulation results demonstrate that our approach performed well in terms of bias, interval coverage percentage, and assurance of achieving a pre‐specified precision for the gWinP. Illustrative code for implementing the methods using SAS PROC RANK and PROC MIXED is provided. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in many domains that rely on data analysis. The k Nearest Neighbors imputation method has been widely used to address this issue, but it has limitations in accurately imputing missing values, especially for datasets with small pairwise correlations and small values of k. In this study, we proposed a method, Ranked k Nearest Neighbors imputation that uses a similar approach to k Nearest Neighbor, but utilizing the concept of Ranked set sampling to select the most relevant neighbors for imputation. Our results show that the proposed method outperforms the standard k nearest neighbor method in terms of imputation accuracy both in case of Missing Completely at Random and Missing at Random mechanism, as demonstrated by consistently lower MSIE and MAIE values across all datasets. This suggests that the proposed method is a promising alternative for imputing missing values in datasets with small pairwise correlations and small values of k. Thus, the proposed Ranked k Nearest Neighbor method has important implications for data imputation in various domains and can contribute to the development of more efficient and accurate imputation methods without adding any computational complexity to an algorithm. [ABSTRACT FROM AUTHOR]

AbstractAmong the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.g., means). For this, we first provide the theoretical underpinnings of the mechanisms under study and then provide two focused simulations that (i) directly expand on the collider scenario in Thoemmes and Rose (2014, appendix A) by considering outcomes that are relevant to norming and (ii) extend the scenarios under consideration by instrumental variable mechanisms. We illustrate the bias mechanisms for two different norming approaches and exemplify the procedures by means of an empirical example. We end by discussing limitations and implications of our research. [ABSTRACT FROM AUTHOR]

Background: Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables ("auxiliary variables") in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR. Methods: We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, "additional bias" refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study. Results: The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed). Conclusions: When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias). [ABSTRACT FROM AUTHOR]

In various applications, observed variables are missing some information that was intended to be collected. The estimations of both loading and path coefficients could be biased when ignoring the missing data. Inverse probability weighting (IPW) is one of the well-known methods helping to reduce bias in regressions, while belonging to a promising but new category in structural equation models. The paper proposes both parametric and nonparametric IPW estimation methods for dynamic structural equation models, in which both loading and path coefficients are developed into functions of a random variable and of the quantile level. To improve the computational efficiency, modified parametric IPW and modified nonparametric IPW are developed through reducing inverse probability computations but making fuller use of completely observed information. All the above IPW estimation methods are compared to existing complete case analysis through simulation investigations. Finally, the paper illustrates the proposed model and estimation methods by an empirical study on digital new-quality productivity. [ABSTRACT FROM AUTHOR]

Motivation The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc , fill these gaps. Implementation micd and tpc packages are R packages. General features The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. Availability The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc). [ABSTRACT FROM AUTHOR]

Purpose: The Functional Assessment of Cancer Therapy item (FACT-GP5) has the potential to provide an understanding of global treatment tolerability from the patient perspective. Longitudinal evaluations of the FACT-GP5 and challenges posed by data missing-not-at-random (MNAR) have not been explored. Robustness of the FACT-GP5 to missing data assumptions and the responsiveness of the FACT-GP5 to key side-effects are evaluated. Methods: In a randomized, double-blind study (NCT00065325), postmenopausal women (n = 618) with hormone receptor-positive (HR+), advanced breast cancer received either fulvestrant or exemestane and completed FACT measures monthly for seven months. Cumulative link mixed models (CLMM) were fit to evaluate: (1) the trajectory of the FACT-GP5 and (2) the responsiveness of the FACT-GP5 to CTCAE grade, Eastern Cooperative Oncology Group (ECOG) Performance Status scale, and key side-effects from the FACT. Sensitivity analyses of the missing-at-random (MAR) assumption were conducted. Results: Odds of reporting worse side-effect bother increased over time. There were positive within-person relationships between level of side-effect bother (FACT-GP5) and severity of other FACT items, as well as ECOG performance status and Common Terminology Criteria for Adverse Events (CTCAE) grade. The number of missing FACT-GP5 assessments impacted the trajectory of the FACT-GP5 but did not impact the relationships between the FACT-GP5 and other items (except for nausea [FACT-GP2]). Conclusions: Results support the responsiveness of the FACT-GP5. Generally speaking, the responsiveness of the FACT-GP5 is robust to missing assessments. Missingness should be considered, however, when evaluating change over time of the FACT-GP5. Trial Registration: NCT00065325. Trial Registration Year: 2003. [ABSTRACT FROM AUTHOR]

Noncompletion of ecological momentary assessment (EMA) surveys is a common issue and may yield bias in results if not properly handled. Using data observed at scheduled times as well as data retrieved later to fill missing responses, this study aims to investigate predictors of EMA completion, including demographic characteristics, time-related factors, and momentary experiences/behaviors. Data were from a 30-day EMA study including 145 women currently experiencing intimate partner violence (IPV) and using substances. The average rate of EMA completion was initially 51.4% at the scheduled times and increased to 72.6% after incorporating data from later-retrieved surveys. Participants who were younger, had more children, or had lower mean levels of negative affect dysregulation showed lower completion rates. At the momentary survey level, more days into the study and afternoon/evening reports (vs. morning reports) were associated with lower completion; lower levels of negative affect dysregulation, less smoking or alcohol use, and experiencing IPV were linked to lower momentary completion. Implications of the results for handling missing data in EMA are discussed and have important ramifications for future research, practice, and theory. [ABSTRACT FROM AUTHOR]

Intensive longitudinal research—including experience sampling and smartphone sensor monitoring—has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old (N = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. General Scientific Summary: Missing data poses substantial challenges to longitudinal research on mental health. This study worked to characterize patterns of missing data among adolescents with psychiatric disorders and at risk for suicide, in particular missingness of smartphone-based surveys and measurements passively collected from participants' smartphones (e.g., global positioning system, accelerometer, and keyboard). Findings highlight key technical and study design factors that should be considered to minimize missing data and maximize the validity of results in future longitudinal studies using smartphones. [ABSTRACT FROM AUTHOR]

Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random. [ABSTRACT FROM AUTHOR]

Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under‐determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions. This paper presents a novel MFA methodology under the Bayesian framework. By relaxing the mass balance constraints, we improve the computational scalability and reliability of the posterior samples compared to existing Bayesian MFA methods. We propose a mass‐based, child and parent process framework to model systems with disaggregated processes and flows. We show posterior predictive checks can be used to identify inconsistencies in the data and aid noise and hyperparameter selection. The proposed approach is demonstrated in case studies, including a global aluminum cycle with significant disaggregation, under weakly informative priors and significant data gaps to investigate the feasibility of Bayesian MFA. We illustrate that just a weakly informative prior can greatly improve the performance of Bayesian methods, for both estimation accuracy and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Background: In computer-aided diagnosis (CAD) studies utilizing multireader multicase (MRMC) designs, missing data might occur when there are instances of misinterpretation or oversight by the reader or problems with measurement techniques. Improper handling of these missing data can lead to bias. However, little research has been conducted on addressing the missing data issue within the MRMC framework. Methods: We introduced a novel approach that integrates multiple imputation with MRMC analysis (MI-MRMC). An elaborate simulation study was conducted to compare the efficacy of our proposed approach with that of the traditional complete case analysis strategy within the MRMC design. Furthermore, we applied these approaches to a real MRMC design CAD study on aneurysm detection via head and neck CT angiograms to further validate their practicality. Results: Compared with traditional complete case analysis, the simulation study demonstrated the MI-MRMC approach provides an almost unbiased estimate of diagnostic capability, alongside satisfactory performance in terms of statistical power and the type I error rate within the MRMC framework, even in small sample scenarios. In the real CAD study, the proposed MI-MRMC method further demonstrated strong performance in terms of both point estimates and confidence intervals compared with traditional complete case analysis. Conclusion: Within MRMC design settings, the adoption of an MI-MRMC approach in the face of missing data can facilitate the attainment of unbiased and robust estimates of diagnostic capability. [ABSTRACT FROM AUTHOR]

Large observational databases are often subject to missing data. As such, methods for causal inference must simultaneously handle confounding and missingness; surprisingly little work has been done at this intersection. Motivated by this, we propose an efficient and robust estimator of the causal average treatment effect from cohort studies when confounders are missing at random. The approach is based on a novel factorization of the likelihood that, unlike alternative methods, facilitates flexible modelling of nuisance functions (e.g., with state‐of‐the‐art machine learning methods) while maintaining nominal convergence rates of the final estimators. Simulated data, derived from an electronic health record‐based study of the long‐term effects of bariatric surgery on weight outcomes, verify the robustness properties of the proposed estimators in finite samples. Our approach may serve as a theoretical benchmark against which ad hoc methods may be assessed. [ABSTRACT FROM AUTHOR]

One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA). [ABSTRACT FROM AUTHOR]

This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation. [ABSTRACT FROM AUTHOR]

Background: Evaluating outcome reliability is critical in real-world evidence studies. Overall survival is a common outcome in these studies; however, its capture in real-world data (RWD) sources is often incomplete and supplemented with linked mortality information from external sources. Conflicting recommendations exist for censoring overall survival in real-world evidence studies. This simulation study aimed to understand the impact of different censoring methods on estimating median survival and log hazard ratios when external mortality information is partially captured. Methods: We used Monte Carlo simulation to emulate a non-randomized comparative effectiveness study of two treatments with RWD from electronic health records and linked external mortality data. We simulated the time to death, the time to last database activity, and the time to data cutoff. Death events after the last database activity were attributed to linked external mortality data and randomly set to missing to reflect the sensitivity of contemporary real-world data sources. Two censoring schemes were evaluated: (1) censoring at the last activity date and (2) censoring at the end of data availability (data cutoff) without an observed death. We assessed the performance of each method in estimating median survival and log hazard ratios using bias, coverage, variance, and rejection rate under varying amounts of incomplete mortality information and varying treatment effects, length of follow-up, and sample size. Results: When mortality information was fully captured, median survival estimates were unbiased when censoring at data cutoff and underestimated when censoring at the last activity. When linked mortality information was missing, censoring at the last activity date underestimated the median survival, while censoring at the data cutoff overestimated it. As missing linked mortality information increased, bias decreased when censoring at the last activity date and increased when censoring at data cutoff. Conclusions: Researchers should consider the completeness of linked external mortality information when choosing how to censor the analysis of overall survival using RWD. Substantial bias in median survival estimates can occur if an inappropriate censoring scheme is selected. We advocate for RWD providers to perform validation studies of their mortality data and publish their findings to inform methodological decisions better. [ABSTRACT FROM AUTHOR]

Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats data as functions, providing a more comprehensive understanding of their behavior and variability. This approach is particularly beneficial for exploring observations collected over time or space. The Spatial Functional Data Analysis (SFDA) incorporates spatial locations as an additional dimension. This integration enhances the ability to model complex associations between curves in the functional domain. Despite progress in analyzing multivariate spatial data, limited attention has been given to SFDA. This study addresses this gap by proposing a Bayesian Gaussian functional approach based on B-splines. The proposal also addresses challenges posed by irregularly spaced observations in the functional domain, a feature rarely considered in the literature. A simulation study is developed to evaluate performance. Finally, results are explored using a relatively novel real dataset from monitoring stations of temperature in Mexico City. [ABSTRACT FROM AUTHOR]

AbstractMultiple imputation (MI) is effectively used to deal with missing data when the missing mechanism is missing at random. However, MI may not be effective when the missing mechanism is not missing at random (NMAR). In such cases, additional information is required to obtain an appropriate imputation. Pham et al. (2019) proposed the calibrated-δ adjustment method, which is a multiple imputation method using population information. It provides appropriate imputation in two NMAR settings. However, the calibrated-δ adjustment method has two problems. First, it can be used only when one variable has missing values. Second, the theoretical properties of the variance estimator have not been provided. This article proposes a multiple imputation method using population information that can be applied when several variables have missing values. The proposed method is proven to include the calibrated-δ adjustment method. It is shown that the proposed method provides a consistent estimator for the parameter of the imputation model in an NMAR situation. The asymptotic variance of the estimator obtained by the proposed method and its estimator are also given. [ABSTRACT FROM AUTHOR]

Summary Unemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed. [ABSTRACT FROM AUTHOR]

Background: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. Methods: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. Conclusion: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction. [ABSTRACT FROM AUTHOR]

Background: Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. Methods: We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. Results: Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. Conclusion: Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data. [ABSTRACT FROM AUTHOR]

Introduction Definitive management of non-compressible intra-abdominal hemorrhage (NCIAH) currently requires a surgeon and operating room capable of performing damage control surgery. In a wartime scenario or a geographically remote environment, these may not be readily available. In this study, we sought to test the safety of 2 emerging injectable hemostatic agents (CounterFlow and Fast Onset Abdominal Management, or FOAM, poloxamer component) versus normal saline control over a prolonged monitoring duration following administration by a non-surgical provider. Materials and Methods The Institutional Animal Care and Use Committee approved all research conducted in this study. We randomized male New Zealand white rabbits into 2 monitoring cohorts of 24 hours and 2 weeks. Each cohort contained 3 treatment groups (n  = 4 rabbits/group): CounterFlow, the testable poloxamer component of FOAM, and normal saline control. We injected each treatment intraperitoneally in the left lower abdominal quadrant. Doses were 15 mL/kg for CounterFlow, 6.3 mL/kg for the poloxamer component of FOAM, and 15 mL/kg for normal saline. We conducted all injections under isoflurane anesthesia monitored by trained veterinary staff. Animals were euthanized at each cohort end point, and a veterinary pathologist blinded to treatment type performed necropsy. The primary outcome was incidence of intra-abdominal adhesions at necropsy. Quantitatively, adhesions when present were graded by the veterinary pathologist on a 1 to 4 scale, where "1" represented adhesions involving from 1 to 25% of the examined abdomen, "2" represented from 26 to 50%, "3" represented from 51 to 75%, and "4" represented from 76 to 100%. Qualitatively, adhesions present were graded by degree ("1" = minimal, "2" = mild, "3" = moderate, and "4" = severe) and chronicity ("1" = acute, "2" = subacute, and "3" = chronic). We also drew d-dimer blood values and measured body weights for each animal. Statistical analysis included either repeated measures 2-way ANOVA or a mixed-effects model (in the case of missing data) with Geisser–Greenhouse correction. We adjusted multiple comparisons using Tukey statistical hypothesis tests. Results In the 2-week cohort, 3 CounterFlow animals showed adhesions judged to be "1" quantitatively. Qualitatively, 2 of these were assessed as "1" for degree of adhesions and the other demonstrated a "2." On the chronicity of adhesions scale, 1 animal demonstrated a "2" and 2 demonstrated a "3." No animals in other groups (FOAM and control) demonstrated adhesions. CounterFlow-treated animals showed a statistically significant rise in d-dimer values in the 24-hour cohort only. In the 2-week cohort, CounterFlow-treated animals showed a decrease in body weight at 24 hours after injection but returned to their baseline (normal) body weights at 7 days. Conclusions Findings from this study demonstrate that the tested ingredients of FOAM poloxamer component are safe for intraperitoneal injection and hold potential for further study directed toward prehospital non-compressible intra-abdominal hemorrhage management by non-surgical providers. Although CounterFlow produced abdominal adhesions in 3 of 4 rabbits in the 2-week cohort, these were determined to be "minimal" or "mild" in degree. [ABSTRACT FROM AUTHOR]

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures. [ABSTRACT FROM AUTHOR]

In education testing, the items that examinees receive may be selected for a variety of reasons, resulting in missing data for items that were not selected. Item selection is internal when based on prior performance on the test, such as in adaptive testing designs or for branching items. Item selection is external when based on an auxiliary variable collected independently to performance on the test, such as education level in a targeting testing design or geographical location in a nonequivalent anchor test equating design. This paper describes the implications of this distinction for Item Response Theory (IRT) estimation, drawing upon missing-data theory (e.g., Mislevy & Sheehan, 1989, https://doi.org/10.1007/BF02296402; Rubin, 1976, https://doi.org/10.1093/biomet/63.3.581), and selection theory (Meredith, 1993, https://doi.org/10.1007/BF02294825). Through mathematical analyses and simulations, we demonstrate that this internal versus external item selection framework provides a general guide in applying missing-data and selection theory to choose a valid analysis model for datasets with missing data. [ABSTRACT FROM AUTHOR]

Imputation of missing values is a strategy for handling non-responses in surveys or data loss in measurement processes, which may be more effective than ignoring the losses and omitting them. The characteristics of variables presenting missing values must be considered when choosing the imputation method to be used; in particular when the variable is a count the literature dealing with this issue is scarce. If the variable has an excess of zeros it is necessary to consider models including parameters for handling zero-inflation. Likewise, if problems of over- or under-dispersion are observed, generalizations of the Poisson, such as the Hermite or Conway–Maxwell Poisson distributions are recommended for carrying out imputation. The aim of this study was to assess the performance of various regression models in the imputation of a discrete variable based on Poisson generalizations, in comparison with classical counting models, through a comprehensive simulation study considering a variety of scenarios and a real data example. To do so we compared the results of estimations using only complete data, and using imputations based on the most common count models. The COMPoisson distribution provides in general better results in any dispersion scenario, especially when the amount of missing information is large. [ABSTRACT FROM AUTHOR]

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine. [ABSTRACT FROM AUTHOR]

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM. [ABSTRACT FROM AUTHOR]

In this article, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides little insights for erosely measured data, and no existing inference method is applicable. We aim to fill in this gap by proposing the first inference method that characterizes the different uncertainty levels over the graph caused by the erose measurements, named GI-JOE (Graph Inference when Joint Observations are Erose). Specifically, we develop an edge-wise inference method and an affiliated FDR control procedure, where the variance of each edge depends on the sample sizes associated with corresponding neighbors. We prove statistical validity under erose measurements, thanks to careful localized edge-wise analysis and disentangling the dependencies across the graph. Finally, through simulation studies and a real neuroscience data example, we demonstrate the advantages of our inference methods for graph selection from erosely measured data. for this article are available online. [ABSTRACT FROM AUTHOR]

In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife estimator of the standard error was proposed as a way to obtain deterministic treatment effect estimates and correct frequentist inference. For both multiple and CMI, a mixed model for repeated measures (MMRM) is often used for the imputation model, but this can be computationally intensive to fit to multiple data sets (e.g. the jackknife samples) and lead to convergence issues with complex MMRM models with many parameters. Therefore, a step-wise approach based on sequential linear regression (SLR) of the outcomes at each visit was developed for the imputation model in the multiple imputation framework, but similar developments in the CMI framework are lacking. In this article, we fill this gap in the literature by proposing a SLR approach to implement RBI in the CMI framework, and justify its validity using theoretical results and simulations. We also illustrate our proposal on a real data application. [ABSTRACT FROM AUTHOR]

Purpose: Non-response (NR) to patient-reported outcome (PRO) questionnaires may cause bias if not handled appropriately. Collecting reasons for NR is recommended, but how reasons for NR are related to missing data mechanisms remains unexplored. We aimed to explore this relationship for intermittent NRs. Methods: Patients with multiple myeloma completed validated PRO questionnaires at enrolment and 12 follow-up time-points. NR was defined as non-completion of a follow-up assessment within seven days, which triggered contact with the patient, recording the reason for missingness and an invitation to complete the questionnaire (denoted "salvage response"). Mean differences between salvage and previous on-time scores were estimated for groups defined by reasons for NR using linear regression with clustered standard errors. Statistically significant mean differences larger than minimal important difference thresholds were interpreted as "missing not at random" (MNAR) mechanism (i.e. assumed to be related to declining health), and the remainder interpreted as aligned with "missing completely at random" (MCAR) mechanism (i.e. assumed unrelated to changes in health). Results: Most (7228/7534 (96%)) follow-up questionnaires were completed; 11% (802/7534) were salvage responses. Mean salvage scores were compared to previous on-time scores by reason: those due to hospital admission, mental or physical reasons were worse in 10/22 PRO domains; those due to technical difficulties/procedural errors were no different in 21/22 PRO domains; and those due to overlooked/forgotten or other/unspecified reasons were no different in any domains. Conclusion: Intermittent NRs due to hospital admission, mental or physical reasons were aligned with MNAR mechanism for nearly half of PRO domains, while intermittent NRs due to technical difficulties/procedural errors or other/unspecified reasons generally were aligned with MCAR mechanism. [ABSTRACT FROM AUTHOR]

Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive summary and critical evaluation of intention-to-treat analysis, with a particular focus on its application to randomized controlled trials within the field of rehabilitation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a methodological review that encompassed electronic and manual search strategies to identify relevant studies. Our selection process involved two independent reviewers who initially screened titles and abstracts and subsequently performed full-text screening based on established eligibility criteria. In addition, we included studies from manual searches that were already cataloged within the first author's personal database. The findings are synthesized through a narrative approach, covering fundamental aspects of intention to treat, including its definition, common misconceptions, advantages, disadvantages, and key recommendations. Notably, the health literature offers a variety of definitions for intention to treat, which can lead to misinterpretations and inappropriate application when analyzing randomized controlled trial results, potentially resulting in misleading findings with significant implications for healthcare decision making. Authors should clearly report the specific intention-to-treat definition used in their analysis, provide details on participant dropouts, and explain upon their approach to managing missing data. Adherence to reporting guidelines, such as the Consolidated Standards of Reporting Trials for randomized controlled trials, is essential to standardize intention-to-treat information, ensuring the delivery of accurate and informative results for healthcare decision making. [ABSTRACT FROM AUTHOR]

We propose a new method for variable selection and prediction under a nonparametric regression setting, where a covariate may be missing either because its value is hidden from the observer or because it is inapplicable to the particular subject being observed. Despite its practical relevance, the problem has received little attention in the literature and its solutions are largely non-existent. Our proposal hinges on the construction of a modified Nadaraya–Watson estimator of the conditional mean regression function, with its bandwidths regularised to select variables and its weights adapted to accommodate different types of missingness. The method allows for information sharing across different missing data patterns without affecting consistency of the estimator. Unlike other conventional methods such as those based on imputations or likelihoods, our method requires only mild assumptions on the model and the missingness mechanism. For prediction we focus on finding relevant variables for predicting mean responses, conditional on covariate vectors subject to a given type of missingness. Our theoretical and numerical results show that the new method is consistent in variable selection and yields better prediction accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Data collected over time are common in applications and may contain censored or missing observations, making it difficult to use standard statistical procedures. This article proposes an algorithm to estimate the parameters of a censored linear regression model with errors serially correlated and innovations following a Student‐t distribution. This distribution is widely used in the statistical modelling of data containing outliers because its longer‐than‐normal tails provide a robust approach to handling such data. The maximum likelihood estimates of the proposed model are obtained through a stochastic approximation of the EM algorithm. The methods are applied to an environmental dataset regarding ammonia‐nitrogen concentration, which is subject to a limit of detection (left censoring) and contains missing observations. Additionally, two simulation studies are conducted to examine the asymptotic properties of the estimates and the robustness of the model. The proposed algorithm and methods are implemented in the R package ARCensReg. [ABSTRACT FROM AUTHOR]

AbstractOne compelling use of citation networks is to characterize papers by their relationships to the surrounding literature. We propose a method to characterize papers by embedding them into two distinct “co-factor” spaces: one describing how papers send citations, and the other describing how papers receive citations. This approach presents several challenges. First, older documents cannot cite newer documents, and thus it is not clear that co-factors are even identifiable. We resolve this challenge by developing a co-factor model for asymmetric adjacency matrices with missing lower triangles and showing that identification is possible. We then frame estimation as a matrix completion problem and develop a specialized implementation of matrix completion because prior implementations are memory bound in our setting. Simulations show that our estimator has promising finite sample properties, and that naive approaches fail to recover latent co-factor structure. We leverage our estimator to investigate 255,780 papers published in statistics journals from 1898 to 2024, resulting in the most comprehensive topic model of the statistics literature to date. We find interpretable co-factors corresponding to many statistical subfields, including time series, variable selection, spatial methods, graphical models, GLM(M)s, causal inference, multiple testing, quantile regression, semiparametrics, dimension reduction, and several more. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations. [ABSTRACT FROM AUTHOR]

Distributed machine learning has received much attention for more than two decades. Yet, it is still a challenge to achieve acceptable performance in practical scenarios when some features of data samples are missing. Although some imputation methods have been proposed for handling missing data, their performance deteriorates significantly when data are heterogeneously distributed over different nodes in the network. Considering this, in this article, we first propose a general Gaussian mixture model (GMM) consisting of both public and personalized components for modeling the homogeneous and the heterogeneous parts of data distribution, respectively. Then, we develop a distributed personalized expectation–maximization method based on knowledge transfer (KT-dpEM) to estimate the parameters of the proposed general GMM. After that, based on the estimated general GMM, missing data are imputed using the posterior conditional mean. Experimental results show that the proposed KT-dpEM algorithm has better imputation accuracy, higher robustness against different missing probabilities and better classification performance in the downstream classification tasks, compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study. [ABSTRACT FROM AUTHOR]

In this paper, a novel test for testing whether data are missing completely at random is proposed. Asymptotic properties of the test are derived utilizing the theory of non-degenerate U-statistics. It is shown that the novel test statistic coincides with the well-known Little's $ d^2 $ d 2 statistic in the case of a multivariate data that has only one variable susceptible to missingness. Then, the extensive simulation study is conducted to examine the performance of the test in terms of the preservation of type I error and in terms of power. Various underlying distributions, dimensions of the data, sample sizes and alternatives are used. Performance of the Little's MCAR test is used as a benchmark for the comparison. The novel test shows better performance in all of the studied scenarios, better preserving the type I error and having higher empirical powers for every studied alternative. [ABSTRACT FROM AUTHOR]

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework's effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting. [ABSTRACT FROM AUTHOR]

Missing data reduce the representativeness of the sample and can lead to inference problems. In this article, we apply the Bayesian jackknife empirical likelihood (BJEL) method for inference on data that are missing at random, as well as for causal inference. The semiparametric fractional imputation estimator, propensity score‐weighted estimator, and doubly robust estimator are used for constructing the jackknife pseudo values, which are needed for conducting BJEL‐based inference with missing data. Existing methods, such as normal approximation and JEL, are compared with the BJEL approach in a simulation study. The proposed approach shows better performance in many scenarios in terms of credible intervals. Furthermore, we demonstrate the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function. [ABSTRACT FROM AUTHOR]

Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c) multiple imputation (MI), where missing values of the variables in the analysis are imputed as draws from their predictive distribution under an implicit or explicit statistical model, the imputation process is repeated to create multiple filled-in data sets, and analysis is carried out using simple MI combining rules. This article provides a non-technical discussion of the strengths and weakness of these approaches, and when each of the methods might be adopted over the others. The methods are illustrated on data from the Youth Cohort (Time) Series (YCS) for England, Wales and Scotland, 1984–2002. [ABSTRACT FROM AUTHOR]

ABSTRACT Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR‐ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR‐ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR‐ALS, offers the flexibility to be applied component‐wise or data block‐wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher‐order dataset with whatever pattern of missing values. [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed. [ABSTRACT FROM AUTHOR]

Missing data are pervasive in risk assessment but their impact on predictive accuracy has largely been unexplored. Common techniques for handling missing risk data include summing available items or proration; however, multiple imputation is a more defensible approach that has not been methodically tested against these simpler techniques. We compared the validity of these three missing data techniques across six conditions using STABLE-2007 (N = 4,286) and SARA-V2 (N = 455) assessments from men on community supervision in Canada. Condition 1 was the observed data (low missingness), and Conditions 2 to 6 were generated missing data conditions, whereby 1% to 50% of items per case were randomly deleted in 10% increments. Relative predictive accuracy was unaffected by missing data, and simpler techniques performed just as well as multiple imputation, but summed totals underestimated absolute risk. The current study therefore provides empirical justification for using proration when data are missing within a sample. [ABSTRACT FROM AUTHOR]

Analyzing longitudinal data in health studies is challenging due to sparse and error‐prone measurements, strong within‐individual correlation, missing data and various trajectory shapes. While mixed‐effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non‐parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error‐prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well‐suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case‐control study nested in a population‐based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis. [ABSTRACT FROM AUTHOR]