RESUMEN

Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the missing data from their perspective predictive distributions, which leads to significantly improved computation time in the class of variable-by-variable imputation algorithms. We assess the computational performance in a comprehensive simulation study. We then compare and contrast the performance of our algorithm with commonly used alternatives. The results show that our method has a significant advantage over the commonly used alternatives with respect to computational efficiency and inferential quality. Finally, we demonstrate our methods in a substantive problem aimed at investigating the effects of area-level behavioral, socioeconomic, and demographic characteristics on poor birth outcomes in New York State among singleton births.

RESUMEN

Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald χ2 test-based procedure for detecting both uniform and non-uniform DIF with polytomous items in the presence of the ubiquitous multilevel data structure. The proposed approach is a multilevel extension of a two-stage procedure, which identifies anchor items in its first stage and formally evaluates candidate items in the second stage. We applied the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to estimate multilevel polytomous item response theory (IRT) models and to obtain accurate covariance matrices. To evaluate the performance of the proposed approach, we conducted a preliminary simulation study that considered various conditions to mimic real-world scenarios. The simulation results indicated that the proposed approach has great power for identifying DIF items and well controls the Type I error rate. Limitations and future research directions were also discussed.

RESUMEN

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

Asunto(s)

RESUMEN

In 2003, Bloom, Hill, and Riccio (BHR) published an influential paper introducing novel methods for explaining the variation in local impacts observed in multi-site randomized control trials of socio-economic interventions in terms of site-level mediators. This paper seeks to improve upon this previous work by using student-level data to measure site-level mediators and confounders. Development of asymptotic behavior backed up with simulations and an empirical example. Students and training providers. Two simulations and an empirical application to data from an evaluation of the Health Professions Opportunity Grants (HPOG) Program. This empirical analysis involved roughly 6600 participants across 37 local sites. We examine bias and mean square error of estimates of mediation coefficients as well as the true coverage of nominal 95-percent confidence intervals on the mediation coefficients. Simulations suggest that the new methods generally improve the quality of inferences even when there is no confounding. Applying this methodology to the HPOG study shows that program-average FTE months of study by month six was a significant mediator of both career progress and long-term degree/credential receipt. Evaluators can robustify their BHR-style analyses by the use of the methods proposed here.

Asunto(s)

RESUMEN

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

RESUMEN

Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.

Asunto(s)

RESUMEN

Li et al developed a multilevel covariance regression (MCR) model as an extension of the covariance regression model of Hoff and Niu. This model assumes a hierarchical structure for the mean and the covariance matrix. Here, we propose the combined multilevel factor analysis and covariance regression model in a Bayesian framework, simultaneously modeling the MCR model and a multilevel factor analysis (MFA) model. The proposed model replaces the responses in the MCR part with the factor scores coming from an MFA model. Via a simulation study and the analysis of real data, we show that the proposed model is quite efficient when the responses of the MCR model are not measured directly but are latent variables such as the patient experience measurements in our motivating dataset.

Asunto(s)

RESUMEN

BACKGROUND: The 2002 World Health Report documented that low fruit and vegetable intake are among the top ten risk factors contributing to attributable mortality and up to three million lives could be saved each year by adequate consumption of F&V across the globe, leading an examination of behavioral preferences of the individual and family social, environmental, and behavioral factors that constitute perceived barriers to fruit and vegetable consumption. OBJECTIVE: The study examines factors affecting the choice of eating fruits and vegetables by household members and calculates eating frequency probabilities of different population-origin associated with personal attributes and behavior. METHOD: Turkish Health Survey (THS) 2019 data from the Turkish Statistical Institute (TSI) national representative household panel is applied. Estimating a random-effect bivariate probit model of fruit and vegetable choice, we calculated marginal probabilities of choosing fruits and vegetables, the joint probability of choosing both, and conditional probabilities between choosing to eat either, detecting consumption synergy. RESULTS: The role of uncontrolled variables in choosing to eat fruits and vegetable (F&V) differs between the decision of an average family and the decision of individual family members. The attitude is positive for an average family and contrasts with the negative attitude among some family members. Most individual and family attributes inversely affect fruit and vegetable choice across different groups, while a positive relationship exists between the likelihood of fruit and vegetable choice and attributes such as age, marital status, education, weight, having health insurance, income, and time and forms of physical activity. CONCLUSION AND RECOMMENDATION: Instead of a general policy for the implementation of a healthy and balanced nutrition program to improve fruit and vegetable eating frequency, it appears more effective to adopt programs with distinct characteristics that segregate society into different cohorts. We suggest appropriate policies and offer suitable approaches to reach targeted groups.

Asunto(s)

RESUMEN

BACKGROUND: Multiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel data. However, MI methods for multilevel ordinal outcome variables have not been well studied, especially when cluster size is informative on the outcome. The purpose of this study is to describe and compare different MI strategies for dealing with multilevel ordinal outcomes when informative cluster size (ICS) exists. METHODS: We conducted comprehensive Monte Carlo simulation studies to compare the performance of five strategies: complete case analysis (CCA), FCS, FCS+CS (including cluster size (CS) in the imputation model), JM, and JM+CS under various scenarios. We evaluated their performance using a proportional odds logistic regression model estimated with cluster weighted generalized estimating equations (CWGEE). RESULTS: The simulation results showed that including CS in the imputation model can significantly improve estimation accuracy when ICS exists. FCS provided more accurate and robust estimation than JM, followed by CCA for multilevel ordinal outcomes. We further applied these strategies to a real dental study to assess the association between metabolic syndrome and clinical attachment loss scores. The results based on FCS + CS indicated that the power of the analysis would increase after carrying out the appropriate MI strategy. CONCLUSIONS: MI is an effective tool to increase the accuracy and power of the downstream statistical analysis for missing ordinal outcomes. FCS slightly outperforms JM when imputing multilevel ordinal outcomes. When there is plausible ICS, we recommend including CS in the imputation phase.

Asunto(s)

RESUMEN

Background: Child mortality is a major public health issue. The studies on under-five mortality that ignore the hierarchical facts mislead the interpretation of the results due to observations in the same cluster sharing common cluster-level random effects. Objectives: The present study uses a multilevel model to analyze under-five mortality and identify the significant factors for under-five mortality in Manipur. Methods: National Family Health Survey-5 (2019-21) data are used in the present study. A multilevel mixed-effect Weibull parameter survival model was fitted to determine the factors affecting under-five mortality. We construct three-level data, individual levels are nested within primary sampling units (PSUs), and PSUs are nested within districts. Results: Out of the 3225 under-five children, 85 (2.64%) died. The three-level mixed-effects Weibull parametric survival model with PSUs nested within the districts, the likelihood-ratio test with Chi-square value = 10.98 and P = 0.004 < 0.05 indicated that the model with random-intercept effects model with PSUs nested within the districts fits the data better than the fixed effect model. The four covariates, namely the number of birth in the last 5 years, age of mother at first birth, use of contraceptive, and size of child at birth, were found as the risk factor for under-five mortality at a 5% level of significance. Conclusions: In the random-intercept effect model, the two estimated variances of the random-intercept effects for district and PSU levels are 0.27 and 0.31, respectively. The values indicate variations (unobserved heterogeneities) in the risk of death of the under-five children between districts and PSUs levels.

Asunto(s)

RESUMEN

Vertically integrated NAND (V-NAND) flash memory is the main data storage in modern handheld electronic devices, widening its share even in the data centers where installation and operation costs are critical. While the conventional scaling rule has been applied down to the design rule of ≈15 nm (year 2013), the current method of increasing device density is stacking up layers. Currently, 176-layer-stacked V-NAND flash memory is available on the market. Nonetheless, increasing the layers invokes several challenges, such as film stress management and deep contact hole etching. Also, there should be an upper bound for the attainable stacking layers (400-500) due to the total allowable chip thickness, which will be reached within 6-7 years. This review summarizes the current status and critical challenges of charge-trap-based flash memory devices, with a focus on the material (floating-gate vs charge-trap-layer), array-level circuit architecture (NOR vs NAND), physical integration structure (2D vs 3D), and cell-level programming technique (single vs multiple levels). Current efforts to improve fabrication processes and device performances using new materials are also introduced. The review suggests directions for future storage devices based on the ionic mechanism, which may overcome the inherent problems of flash memory devices.

RESUMEN

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and heterogeneous treatment effects can be hampered when data are structured within groups if we fail to correctly model the dependence between observations. Most machine learning methods do not readily accommodate such structure. This paper introduces a new algorithm, stan4bart, that combines the flexibility of Bayesian Additive Regression Trees (BART) for fitting nonlinear response surfaces with the computational and statistical efficiencies of using Stan for the parametric components of the model. We demonstrate how stan4bart can be used to estimate average, subgroup, and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data as well as multilevel approaches that have strict parametric forms.

RESUMEN

Herein, the lead-free halide perovskite films with different Cu-to-Ag ratios (Cu3-xAgxSbI6, x = 0, 1, 2, or 3) have been prepared by a spin-coating method at low temperature. The enhanced resistive switching (RS) performance of more uniform SET/RESET voltages and the endurance up to at least 1600 cycles are found in the RS memory with a device structure of Ag/PMMA/Cu2AgSbI6/ITO. The device performance is not degraded under different bending angles and after 103 bending cycles, which is beneficial for flexible memory applications. The appropriately increased activation energy of the perovskites with the partial substitution of Ag atoms, which would lead to a more robust filament formed, is proposed to explain the enhanced RS mechanism. Importantly, the effective size and number of filaments measured by conductive AFM are introduced to confirm the multilevel storage effect of Cu2AgSbI6. The multilevel storage characteristics with four resistance levels are demonstrated by various compliance currents. Moreover, the Cu2AgSbI6 memory devices still exhibit enhanced RS properties and multilevel storage after 75 days of exposure to ambient conditions. Our study provides a strategy for improving the stability and high-density storage applications of halide perovskite RS memory devices.

RESUMEN

Group similarities and differences may manifest themselves in a variety of ways in multiple-group latent class analysis (LCA). Sometimes, measurement models are identical across groups in LCA. In other situations, the measurement models may differ, suggesting that the latent structure itself is different between groups. Tests of measurement invariance shed light on this distinction. We created an R package glca that implements procedures for exploring differences in latent class structure between populations, taking multilevel data structure into account. The glca package deals with the fixed-effect LCA and the nonparametric random-effect LCA; the former can be applied in the situation where populations are segmented by the observed group variable itself, whereas the latter can be used when there are too many levels in the group variable to make a meaningful group comparisons by identifying a group-level latent variable. The glca package consists of functions for statistical test procedures for exploring group differences in various LCA models considering multilevel data structure.

RESUMEN

Propensity score methods are a widely recommended approach to adjust for confounding and to recover treatment effects with non-experimental, single-level data. This article reviews propensity score weighting estimators for multilevel data in which individuals (level 1) are nested in clusters (level 2) and nonrandomly assigned to either a treatment or control condition at level 1. We address the choice of a weighting strategy (inverse probability weights, trimming, overlap weights, calibration weights) and discuss key issues related to the specification of the propensity score model (fixed-effects model, multilevel random-effects model) in the context of multilevel data. In three simulation studies, we show that estimates based on calibration weights, which prioritize balancing the sample distribution of level-1 and (unmeasured) level-2 covariates, should be preferred under many scenarios (i.e., treatment effect heterogeneity, presence of strong level-2 confounding) and can accommodate covariate-by-cluster interactions. However, when level-1 covariate effects vary strongly across clusters (i.e., under random slopes), and this variation is present in both the treatment and outcome data-generating mechanisms, large cluster sizes are needed to obtain accurate estimates of the treatment effect. We also discuss the implementation of survey weights and present a real-data example that illustrates the different methods.

Asunto(s)

RESUMEN

As our understanding of the microbiome has expanded, so has the recognition of its critical role in human health and disease, thereby emphasizing the importance of testing whether microbes are associated with environmental factors or clinical outcomes. However, many of the fundamental challenges that concern microbiome surveys arise from statistical and experimental design issues, such as the sparse and overdispersed nature of microbiome count data and the complex correlation structure among samples. For example, in the human microbiome project (HMP) dataset, the repeated observations across time points (level 1) are nested within body sites (level 2), which are further nested within subjects (level 3). Therefore, there is a great need for the development of specialized and sophisticated statistical tests. In this paper, we propose multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. We develop a variational approximation method for maximum likelihood estimation and inference. It uses optimization, rather than sampling, to approximate the log-likelihood and compute parameter estimates, provides a robust estimate of the covariance of parameter estimates and constructs a Wald-type test statistic for association testing. We evaluate and demonstrate the performance of our method using extensive simulation studies and an application to the HMP dataset. We have developed an R package MZINBVA to implement the proposed method, which is available from the GitHub repository https://github.com/liudoubletian/MZINBVA.

Asunto(s)

RESUMEN

In this paper, a tuneable multilevel data storage bioresistive memory device is prepared from a composite of multiwalled carbon nanotubes (MWCNTs) and egg albumen (EA). By changing the concentration of MWCNTs incorporated into the egg albumen film, the switching current ratio of aluminium/egg albumen:multiwalled carbon nanotubes/indium tin oxide (Al/EA:MWCNT/ITO) for resistive random access memory increases as the concentration of MWCNTs decreases. The device can achieve continuous bipolar switching that is repeated 100 times per cell with stable resistance for 104 s and a clear storage window under 2.5 × 104 continuous pulses. Changing the current limit of the device to obtain low-state resistance values of different states achieves multivalue storage. The mechanism of conduction can be explained by the oxygen vacancies and the smaller number of iron atoms that are working together to form and fracture conductive filaments. The device is nonvolatile and stable for use in rewritable memory due to the adjustable switch ratio, adjustable voltage, and nanometre size, and it can be integrated into circuits with different power consumption requirements. Therefore, it has broad application prospects in the fields of data storage and neural networks.

RESUMEN

The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model.

RESUMEN

Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients, for example. This paper introduces GLMM trees, a decision-tree method for multilevel and longitudinal data. Method: To illustrate, we apply GLMM trees to a dataset of 3,256 young people (mean age 11.33, 48% girls) receiving treatment at one of several mental-health service providers in the UK. Two treatment outcomes (mental-health difficulties scores corrected for baseline) were regressed on 18 demographic, case and severity characteristics at baseline. We compared the performance of GLMM trees with that of traditional GLMMs and random forests. Results: GLMM trees yielded modest predictive accuracy, with cross-validated multiple R values of .18 and .25. Predictive accuracy did not differ significantly from that of traditional GLMMs and random forests, while GLMM trees required evaluation of a lower number of variables. Conclusion: GLMM trees provide a useful data-analytic tool for clinical prediction problems. The supplemental material provides a tutorial for replicating the GLMM tree analyses in R.

Asunto(s)

RESUMEN

The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between 'physiologically normal', 'physiologically extreme' and 'implausible' observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.