Models For Longitudinal Data Research Articles

PEMODELAN HARGA SAHAM PERUSAHAAN PROPERTI DAN REAL ESTATE MENGGUNAKAN REGRESI LONGITUDINAL SPLINE TRUNCATED DILENGKAPI GUI R

Stocks are one of the most popular financial instruments traded in the capital market. One of stock prices fluctuate up and down due to the influence of several factors, one of which is inflation. Stocks in the property and real estate sectors are important indicators to determine the level of a country economy. Data on several stock prices is one case of longitudinal data in economic field. The data is divided into 2 parts, namely in sample data from January 2016 to October 2020 and out sample data from November 2020 to December 2021. In this study, longitudinal stock price data is modeling using nonparametric spline truncated. The best spline truncated model is determined by the order and the optimal number of knot points based on the minimum Generalized Cross Validation value. Spline truncated nonparametric regression modeling for longitudinal data in this study is equipped with Graphical User Interface (GUI) that can facilitate the data processing. The results of the analysis show that the best longitudinal spline truncated regression model obtained on 2nd order with 5 knot points. 95.04% value of indicates the model is a strong model. In the evaluation of the best model, the MAPE data out sample value is 16.45%. It indicates the model has good forecasting ability.

Self-efficacy in predicting smoking cessation: A prospective study in Italy.

Predicting the success of smoking cessation might be crucial to guide towards the treatment of smoking dependence in a clinical setting. We analyzed the potential determinants of successful smoking cessation with a specific focus on self-efficacy in predicting quitting smoking. All consecutive smokers (n=478; 224 men and 254 women) attending the Careggi University Hospital Smoking Cessation Service in Florence (Italy) in 2018-2019 provided information on self-efficacy in predicting smoking cessation, using a 1-10 rating scale during their first visit. Patients were followed up for success in quitting smoking at 3, 6 and 12 months, validated through CO exhaled measurement. To evaluate the association between self-efficacy and the probability of success, we estimated multivariable relative risks (RRs) and corresponding 95% confidence intervals (CIs) through log-binomial models for longitudinal data. Overall, 47.9% of smokers succeeded in their attempt to quit at 3 months, 40.2% at 6 months, and 33.9% at 12 months. Compared to low self-efficacy (rating scale 1-5), the RR of success in quitting smoking was 1.40 (95% CI: 1.06-1.85) for intermediate self-efficacy (scale 6-7) and 1.64 (95% CI: 1.28-2.12) for high self-efficacy (scale 8-10). Self-efficacy is an independent determinant of smoking cessation. We recommend to systematically collect self-efficacy, together with other relevant variables, to predict successful smoking cessation. Moreover, strategies to develop and maintain high levels of self-efficacy are essential to increase quit success and improve treatment.

Flexible Bayesian quantile regression for nonlinear mixed effects models based on the generalized asymmetric Laplace distribution

We propose flexible Bayesian quantile regression for a class of parametric nonlinear mixed effects models for longitudinal data based on the generalized asymmetric Laplace distribution, which exhibits more flexibility in skewness, mode and tail behaviour than the frequently used asymmetric Laplace distribution in quantile regression. An efficient Markov chain Monte Carlo procedure based on the adaptive random walk Metropolis-within-Gibbs sampling algorithm is derived for posterior inference. We demonstrate through simulation studies and empirical analysis that the proposed method could provide more accurate parameter estimation and better model fit than the existing methods.

Non-Invasive Mechanical Ventilation slows motor decline in Amyotrophic Lateral Sclerosis: a population-based study (P10-8.003)

<h3>Objective:</h3> To test whether Non-Invasive Mechanical Ventilation (NIMV) use improved the rate of motor decline among ALS patients. <h3>Background:</h3> The efficacy of NIMV in improving Amyotrophic Lateral Sclerosis (ALS) survival and quality of life is well established. However, data regarding its effect on disease progression are still lacking. <h3>Design/Methods:</h3> This population-based cohort study included 630 patients with ALS from the Piemonte and Valle d’Aosta Register for ALS (PARALS) who were followed up after NIV initiation. Motor function was evaluated with the ALS Functional Rating Score – Revised (ALSFRS-R). Mixed-effect models for longitudinal data were performed to assess change in the rate of functional decline after NIMV initiation. <h3>Results:</h3> NIMV was associated with a ~30% reduction in the ΔALSFRS-R (post-NIMV difference in ΔALSFRS-R −0.46, 97.5% CI −0.43 to −0.49, p&lt;0.0001). The NIMV-related slowing was present on bulbar symptoms (post-NIMV difference in ΔALSFRS-R −0.08, 97.5% CI −0.065 to −0.010, p &lt; 0.0001) and even greater on spinal function (post-NIMV difference in ΔALSFRS-R −0.42, 97.5% CI −0.39 to −0.46, p &lt; 0.0001). All patients benefitted from NIMV usage, regardless of age, site of symptoms onset and presence of cognitive impairment. <h3>Conclusions:</h3> We demonstrate that NIMV significantly improves the rate of disease progression in ALS, regardless of age at symptoms onset, disease duration, presence of bulbar dysfunction or cognitive impairment. Our results reinforce the importance of not delaying NIMV initiation. In addition, NIMV-induced slowing of disease progression should also be accounted for when evaluating clinical trial outcomes. Further studies are required to optimize the effectiveness of respiratory care on disease progression in ALS. <b>Disclosure:</b> Mr. Grassano has nothing to disclose. Dr. Koumantakis has nothing to disclose. Cristina Moglia has nothing to disclose. Antonio Canosa has nothing to disclose. Dr. Manera has nothing to disclose. Dr. Vasta has nothing to disclose. Francesca Palumbo has nothing to disclose. Mr. Callegaro has nothing to disclose. Dr. Marchese has nothing to disclose. Paolina Salamone has nothing to disclose. Dr. Fuda has nothing to disclose. Federico Casale has nothing to disclose. Dr. Calvo has nothing to disclose. Dr. Mora has nothing to disclose. Dr. Chio has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Cytokinetics. Dr. Chio has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Mitsubishi. Dr. Chio has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen.

A hidden Markov model for continuous longitudinal data with missing responses and dropout.

We propose a hidden Markov model for multivariate continuous longitudinal responses with covariates that accounts for three different types of missing pattern: (I) partially missing outcomes at a given time occasion, (II) completely missing outcomes at a given time occasion (intermittent pattern), and (III) dropout before the end of the period of observation (monotone pattern). The missing-at-random (MAR) assumption is formulated to deal with the first two types of missingness, while to account for the informative dropout, we rely on an extra absorbing state. Estimation of the model parameters is based on the maximum likelihood method that is implemented by an expectation-maximization (EM) algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.

Impact of circular economy network building: resilience strategy to climate action

PurposeThis study aims to examine how external and internal conditions drive the impact of circular economy mechanism by decomposing into three policy networks in terms of reduce, reuse and recycle, to better understand the contingency model of climate change and effect of firm size on subsequent performance.Design/methodology/approachDrawing on circular economy network and resource-based view (RBV)-network-resilience strategy framework, a pooled longitudinal cross-sectional data model is developed using a sample of 4,050 Taiwanese manufacturing multinational corporations (MNCs) making foreign direct investment between 2013 and 2018. Structural equation modeling analysis is used to comprehensively examine and investigate each circular economy policy network in the context of climate change and firm size. Post hoc multigroup analysis (MGA) is also conducted.FindingsMGA shows that the reduce policy network is positively and negatively related to manufacturing know-how and production size, respectively. The impact of reuse policy network can enhance the competence of large firms. The recycle policy network is more prominent in terms of competence enhancement of climate change.Practical implicationsMNCs are seeking to build circular economy policy networks to a greater extent, given climate change pressure and guidelines.Originality/valueThis study adds to the circular economy and RBV-network-related literature on climate change and interactions to enhance performance, echoing the recent call on the sustainability of the circular economy of MNCs.

Modelling growth in Suffolk and Charollais sheep populations using random regression models and validation of constrained polynomial correlation values

Random regression modelling has been used across multiple animal species to model longitudinal data. The random regression model for growth accounts for the genetic correlation between measures of the same trait over time and the wide environmental variability in growth, but this requires adequate weight records across the age range. However, contemporary management practices in sheep in the United Kingdom generally focus on growing lambs and neglect mature weight recordings. This study examined modelling strategies for growth data in Suffolk and Charollais sheep, provided by the Agriculture and Horticulture Development Board, with polynomial random regression modelling with many early life weight recordings but limited weight recordings in mature animals. Two methods were employed to model the data. In Method A, missing mature weight records were predicted for those animals that did not have a recorded mature weight. The animals were sorted into groups based on the identity of their sires and the year in which the animal was born. Mature weight values were predicted within each group with a multiple regression model. The dataset, including predicted values, was analysed with random regression models using polynomials and simple linear regression for animal and permanent environmental (PE) effects. In Method B, the dataset with missing mature weight records was analysed using a random linear regression animal model with random animal and PE effects. Due to problems of convergence because the parameters were close to the boundary space, fixing the correlation between the intercept and slope of the Legendre polynomial at different levels was investigated. The heritability values resulting from the model with a fixed correlation between intercept and slope parameters at 0.5 for the Suffolk dataset resulted in heritability values ranging from 0.2 to 0.5 from 1 to 619 days of age. Corresponding estimates for the Charollais dataset ranged from 0.18 to 0.49 from 1 to 640 days of age. For the Suffolk data, the genetic correlations ranged from 1.00 to 0.08 between weight at day 1 to weight at day 619, while for the Charollais, the correlations ranged from 1.00 to 0.05 from 1 to 640 days of age. Validation procedures were undertaken using a multitrait approach to examine the estimated breeding values when the correlation between the intercept and slope are fixed at different levels. The results indicated that fixing the correlation at 0.5 gave the most appropriate estimates for the Suffolk and Charollais datasets.

Bayesian Analysis of Multivariate Longitudinal Ordinal Data Using Multiple Multivariate Probit Models

Multivariate longitudinal ordinal data are often involved in longitudinal studies with each individual having more than one longitudinal ordinal measure. However, due to complicated correlation structures within each individual and no explicit likelihood functions, analyzing multivariate longitudinal ordinal data is quite challenging. In this paper, Markov chain Monte Carlo (MCMC) sampling methods are developed to analyze multivariate longitudinal ordinal data by extending multivariate probit (MVP) models for univariate longitudinal ordinal data to multiple multivariate probit models (MMVP) for multivariate longitudinal ordinal data. The identifiable MVP models require the covariance matrix of the latent multivariate normal variables underlying the longitudinal ordinal variables to be a correlation matrix, thus a Metropolis-Hastings (MH) algorithm is usually necessitated, which brings a rigorous task to develop efficient MCMC sampling methods. In contrast to the identifiable MVP models, the non-identifiable MVP models can be constructed to circumvent a MH algorithm to sample a correlation matrix by a Gibbs sampling to sample a covariance matrix, and hence improve the mixing and convergence of the MCMC components. Therefore, both the identifiable MMVP models and the non-identifiable MMVP models for multivariate longitudinal ordinal data are presented, and their corresponding MCMC sampling methods are developed. The performances of these methods are illustrated through simulation studies and an application using data from the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE).

The shortsighted victim: Short-term mindsets mediate the link between victimization and later offending

BackgroundPredominant explanations of the victim-offender overlap tend to focus on shared causes, such as (low) self-control or risky lifestyles. Such explanations bypass the possibility of a causal link between victimization and offending. We draw on evolutionary developmental psychology and criminological research to propose and test the hypothesis that victimization induces what we refer to as a short-term mindset, i.e., an orientation towards the here-and-now at the expense of considering the future, which in turn increases offending. MethodsWe test this mediation hypothesis using structural equation modeling of longitudinal data from a representative sample of urban youth from the city of Zurich, Switzerland (N = 1675). ResultsIn line with our preregistered predictions, we find that short-term mindsets mediate the effect of victimization on offending, net of prior levels of offending and short-term mindsets, and other controls. ConclusionsWe discuss implications for criminological theory and interventions.

Deep learning and differential equationsfor modeling changes in individual-level latent dynamics between observation periods.

When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction and differential equationsfor dynamic modeling of individual-level trajectories. However, such approaches so far assume that parameters of individual-level dynamics are constant throughout the observation period. Motivated by an application from psychological resilience research, we propose an extension where different sets of differential equationparameters are allowed for observation subperiods. Still, estimation for intra-individual subperiods is coupled for being able to fit the model also with a relatively small dataset. We subsequently derive prediction targets from individual dynamic models of resilience in the application. These serve as outcomes for predicting resilience from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors. Our approach is seen to successfully identify individual-level parameters of dynamic models that allow to stably select predictors, that is, resilience factors. Furthermore, we can identify those characteristics of individuals that are the most promising for updates at follow-up, which might inform future study design. This underlines the usefulness of our proposed deep dynamic modeling approach with changes in parameters between observationsubperiods.

Cholesterol de novo Biosynthesis in Paired Samples of Breast Cancer and Adjacent Histologically Normal Tissue: Association with Proliferation Index, Tumor Grade, and Recurrence-Free Survival

Background: Cholesterol is an essential component of cell membranes whose local de novo biosynthesis may occur in response to additional cellular requirements, especially cell proliferation. In this study, we investigated: (1) the differential expression of the genes coding for the main enzymes involved in cholesterol biosynthesis (ACAT2, HMGCS1, HMGCR, FDFT1, SQLE, LSS, and NSDHL), or the proteins that control their activity (SREBF2, SCAP, and INSIG1), in patient-matched samples of breast cancer and adjacent histologically normal (HN) tissue; (2) their association with the expression of MKI67 or the histologic tumor grade (in particular, G2); (3) their association with recurrence-free survival (RFS). Methods: Nonparametric rank-based models for longitudinal data were applied to assess the differential gene expression between the tumor and the adjacent HN tissue from the same patient or between the classes of tumor grade. Spearman’s rank correlation and Cox proportional hazard models were used to assess the correlation between the genes and their association with RFS. Results: Compared to the adjacent HN tissue, HMGCS1, HMGCR, SQLE, and NSDHL genes were more expressed in the tumor. Their expression progressively increased according to tumor grade and correlated positively with MKI67. ACAT2, HMGCR, and NSDHL genes were associated with a high risk of recurrence even when adjusted for age, tumor grade, or immunohistochemical Ki67. Conclusion: The findings indicated that some genes involved in cholesterol biosynthesis were more expressed in cancerous tissue, correlated positively with tumor grade and MKI67 expression, and were associated with RFS, thus substantiating the relationship between de novo cholesterol biosynthesis and tumor aggressiveness.

A two-level copula joint model for joint analysis oflongitudinal and competing risks data.

In this article, we propose a two-level copula joint model to analyze clinical data with multiple disparate continuous longitudinal outcomes and multiple event-times in the presence of competing risks. At the first level, we use a copula to model the dependence between competing latent event-times, in the process constructing the submodel for the observed event-time, and employ the Gaussian copula to construct the submodel for the longitudinal outcomes that accounts for their conditional dependence; these submodels are glued together at the second level via the Gaussian copula to construct a joint model that incorporates conditional dependence between the observed event-time and the longitudinal outcomes. To have the flexibility to accommodate skewed data and examine possibly different covariate effects on quantiles of a non-Gaussian outcome, we propose linear quantile mixed models for the continuous longitudinal data. We adopt a Bayesian framework for model estimation and inference via Markov Chain Monte Carlo sampling. We examine the performance of the copula joint model through a simulation study and show that our proposed method outperforms the conventional approach assuming conditional independence with smaller biases and better coverage probabilities of the Bayesian credible intervals. Finally, we carry out an analysis of clinical data on renal transplantation for illustration.

Bayesian semiparametric joint modeling of a count outcome and inconveniently timed longitudinal predictors.

The Women's Health Initiative (WHI) Life and Longevity After Cancer (LILAC) study is an excellent resource for studying the quality of life following breast cancer treatment. At study entry, women were asked about new symptoms that appeared following their initial cancer treatment. In this article, we were interested in using regression modeling to estimate associations of clinical and lifestyle factors at cancer diagnosis (independent variables) with the number of new symptoms (dependent variable). Although clinical and lifestyle data were collected longitudinally, few measurements were obtained at diagnosis or at a consistent timepoint prior to diagnosis, which complicates the analysis. Furthermore, parametric count models, such as the Poisson and negative binomial, do not fit the symptom data well. Thus, motivated by the issues encountered in LILAC, we propose two Bayesian joint models for longitudinal data and a count outcome. Our two models differ according to the assumption on the outcome distribution: one uses a negative binomial (NB) distribution and the other a nonparametric rounded mixture of Gaussians (RMG). The mean of each count distribution is dependent on imputed values of continuous, binary, and ordinal variables at a time point of interest (e.g. diagnosis). To facilitate imputation, longitudinal variables are modeled jointly using a linear mixed model for a latent underlying normal random variable, and a Dirichlet process prior is assigned to the random subject-specific effects to relax distribution assumptions. In simulation studies, the RMG joint model exhibited superior power and predictive accuracy over the NB model when the data were not NB. The RMG joint model also outperformed an RMG model containing predictors imputed using the last value carried forward, which generated estimates that were biased toward the null. We used our models to examine the relationship between sleep health at diagnosis and the number of new symptoms following breast cancer treatment in LILAC.

A mixed effects changepoint quantile regression model for longitudinal data with application on COVID-19 data

IntroductionLongitudinal individual response profiles could exhibit a mixture of two or more phases of increase or decrease in trend throughout the follow-up period, with one or more unknown transition points (changepoints). The detection and estimation of these changepoints is crucial. Most of the proposed statistical methods for detecting and estimating changepoints in literature rely on distributional assumptions that may not hold. In this case, a good alternative is to use a robust approach; the quantile regression model. There are methods in the literature to deal with quantile regression models with a changepoint. These methods ignore the within-subject dependence of longitudinal data.MethodsWe propose a mixed effects quantile regression model with changepoints to account for dependence structure in the longitudinal data. Fixed effects parameters, in addition to the location of the changepoint, are estimated using the profile estimation method. The stochastic approximation EM algorithm is proposed to estimate the fixed effects parameters exploiting the link between an asymmetric Laplace distribution and the quantile regression. In addition, the location of the changepoint is estimated using the usual optimization methods.Results and discussionA simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. The practical use of the proposed model is illustrated using COVID-19 data. The data focus on the effect of global economic and health factors on the monthly death rate due to COVID-19 from 1 April 2020 to 30th April 2021. the results show a positive effect on the monthly number of patients with COVID-19 in intensive care units (ICUs) for both 0.5th and 0.8th quantiles of new monthly deaths per million. The stringency index, hospital beds, and diabetes prevalence have no significant effect on both 0.5th and 0.8th quantiles of new monthly deaths per million.

Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels

We study the depth-weighted L-type location estimator of multivariate data when the observations are measured with noise. Under a drifting asymptotic framework, we show that the depth-weighted mean estimators with noisy data are still consistent and asymptotically mean-zero Gaussian under mild conditions. We apply the results to longitudinal data models of heterogeneous agents and develop the depth-weighted mean-group estimator of a vector of random coefficients, which estimates the multivariate average effect in heterogeneous panels or among heterogeneous treatment effects. As an empirical illustration, we examine the relative purchasing power parity.

Incomplete Antiretroviral Therapy Adherence Is Associated with Lower CD4-CD8 Ratio in Virally Suppressed Patients with HIV Infection in Mexico.

Suboptimal adherence to antiretroviral therapy (ART) in people with HIV, even during sustained viral suppression, is associated with persistent inflammation, immune activation, and coagulopathy. Persistently low CD4-CD8 Ratio has been also associated with residual inflammation, is a good predictor of increased risk of death and more widely available than inflammatory biomarkers. We tested the hypothesis that the CD4-CD8 Ratio is associated with ART adherence during periods of complete viral suppression. We used the Medication Possession Ratio based in pharmacy registries as measure of adherence and time-varying, routine care CD4 and CD8 measurements as outcome. We used a linear mixed model for longitudinal data, including fixed effects for sex, age, education, date of ART initiation, AIDS-related conditions, and baseline CD4 to model the outcome. In 988 adults with a median follow-up of 4.13 years, higher ART adherence was independently associated with a modest increase in CD4-CD8. For each increasing percentage point in adherence, the CD4-CD8 Ratio increased 0.000857 (95% confidence interval [CI] -0.000494 to 0.002209, p = .213731) in the first year after achieving viral suppression; 0.001057 (95% CI 0.000262-0.001853, p = .009160) in years 1 to 3; 0.000323 (95% CI -0.000448 to 0.001095, p = .411441) in years 3 to 5; and 0.000850 (95% CI 0.000272-0.001429, p = .003946) 5-10 years after achieving viral suppression. The magnitude of the effect of adherence over CD4-CD8 Ratios varied over time and by baseline CD4 count, with increasing adherence having a larger effect early after ART initiation in people with higher baseline CD4 (>500 cells/μL) and in later years in people with lower baseline CD4 count (≥200 cells/μL). Our findings expand on previous evidence suggesting that the benefits of optimal adherence to modern ART regimens goes beyond maintaining viral suppression. These results highlight the importance of including objective measurements of adherence as part of routine care, even in patients with complete HIV suppression over long-term follow-up.

Semiparametric function-on-function quantile regression model with dynamic single-index interactions

In this paper we propose a new semiparametric function-on-function quantile regression model with time-dynamic single-index interactions. Our model is very flexible in taking into account of the nonlinear time-dynamic interaction effects of the multivariate longitudinal/functional covariates on the longitudinal response, that most existing quantile regression models for longitudinal data are special cases of our proposed model. We propose to approximate the bivariate nonparametric coefficient functions by tensor product B-splines, and employ a check loss minimization approach to estimate the bivariate coefficient functions and the index parameter vector. Under some mild conditions, we establish the asymptotic normality of the estimated single-index coefficients using projection orthogonalization technique, and obtain the convergence rates of the estimated bivariate coefficient functions. Furthermore, we propose a score test to examine whether there exist interaction effects between the covariates. The finite sample performance of the proposed method is illustrated by Monte Carlo simulations and an empirical data analysis.

Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data

It is extremely important to identify patients with acute pancreatitis who are at high risk for developing persistent organ failures early in the course of the disease. Due to the irregularity of longitudinal data and the poor interpretability of complex models, many models used to identify acute pancreatitis patients with a high risk of organ failure tended to rely on simple statistical models and limited their application to the early stages of patient admission. With the success of recurrent neural networks in modeling longitudinal medical data and the development of interpretable algorithms, these problems can be well addressed. In this study, we developed a novel model named Multi-task and Time-aware Gated Recurrent Unit RNN (MT-GRU) to directly predict organ failure in patients with acute pancreatitis based on irregular medical EMR data. Our proposed end-to-end multi-task model achieved significantly better performance compared to two-stage models. In addition, our model not only provided an accurate early warning of organ failure for patients throughout their hospital stay, but also demonstrated individual and population-level important variables, allowing physicians to understand the scientific basis of the model for decision-making. By providing early warning of the risk of organ failure, our proposed model is expected to assist physicians in improving outcomes for patients with acute pancreatitis.

The Strong Consistency for the Estimators of Longitudinal Data in Semiparametric Regression Model with ρ ̃-Mixing Errors

Consider the following semiparametric regression model for longitudinal data with - mixing errors: , where, the response variable and the covariate vector taken from the -th subject at time , is the - mixing random variables, We establish a strong consistency for the least squares estimator of the parametric and the estimator of the non-parametric function under some mild conditions.

Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology.

Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology.