Heterogeneous Longitudinal Data Research Articles

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

Mixture of longitudinal factor analyzers and their application to the assessment of chronic pain.

Multivariate longitudinal data are used in a variety of research areas not only because they allow to analyze time trajectories of multiple indicators, but also to determine how these trajectories are influenced by other covariates. In this article, we propose a mixture of longitudinal factor analyzers. This model could be used to extract latent factors representing multiple longitudinal noisy indicators in heterogeneous longitudinal data and to study the impact of one or several covariates on these latent factors. One of the advantages of this model is that it allows for measurement non-invariance, which arises in practice when the factor structure varies between groups of individuals due to cultural or physiological differences. This is achieved by estimating different factor models for different latent classes. The proposed model could also be used to extract latent classes with different latent factor trajectories over time. Other advantages of the model include its ability to take into account heteroscedasticity of errors in the factor analysis model by estimating different error variances for different latent classes. We first define the mixture of longitudinal factor analyzers and its parameters. Then, we propose an EM algorithm to estimate these parameters. We propose a Bayesian information criterion to identify both the number of components in the mixture and the number of latent factors. We then discuss the comparability of the latent factors obtained between subjects in different latent groups. Finally, we apply the model to simulated and real data of patients with chronic postoperative pain.

Temporal profiles of systolic blood pressure variability and neurologic outcomes after endovascular thrombectomy.

Observational studies have found an increased risk of hemorrhagic transformation and worse functional outcomes in patients with higher systolic blood pressure variability (BPV). However, the time-varying behavior of BPV after endovascular thrombectomy (EVT) and its effects on functional outcome have not been well characterized. We analyzed data from an international cohort of patients with large-vessel occlusion stroke who underwent EVT at 11 centers across North America, Europe, and Asia. Repeated time-stamped blood pressure data were recorded for the first 72 h after thrombectomy. Parameters of BPV were calculated in 12-h epochs using five established methodologies. Systolic BPV trajectories were generated using group-based trajectory modeling, which separates heterogeneous longitudinal data into groups with similar patterns. Of the 2041 patients (age 69 ± 14, 51.4% male, NIHSS 15 ± 7, mean number of BP measurements 50 ± 28) included in our analysis, 1293 (63.4%) had a poor 90-day outcome (mRS ⩾ 3) or a poor discharge outcome (mRS ⩾ 3). We identified three distinct SBP trajectories: low (25%), moderate (64%), and high (11%). Compared to patients with low BPV, those in the highest trajectory group had a significantly greater risk of a poor functional outcome after adjusting for relevant confounders (OR 2.2; 95% CI 1.2-3.9; p = 0.008). In addition, patients with poor outcomes had significantly higher systolic BPV during the epochs that define the first 24 h after EVT (p < 0.001). Acute ischemic stroke patients demonstrate three unique systolic BPV trajectories that differ in their association with functional outcome. Further research is needed to rapidly identify individuals with high-risk BPV trajectories and to develop treatment strategies for targeting high BPV.

Order selection for regression-based hidden Markov model

Hidden Markov models (HMMs) describe the relationship between two stochastic processes: an observed process and an unobservable finite-state transition process. Owing to their modeling dynamic heterogeneity, HMMs are widely used to analyze heterogeneous longitudinal data. Traditional HMMs frequently assume that the number of hidden states (i.e., the order of HMM) is a constant and should be specified prior to analysis. This assumption is unrealistic and restrictive in many applications. In this study, we consider regression-based hidden Markov model (RHMM) while allowing the number of hidden states to be unknown and determined by the data. We propose a novel likelihood-based double penalized method, along with an efficient expectation-conditional maximization with iterative thresholding-based descent (ECM–ITD) algorithm, to perform order selection in the context of RHMM. An extended Group-Sort-Fuse procedure is proposed to rank the regression coefficients and impose penalties on the discrepancy of adjacent coefficients. The order selection consistency and convergence of the ECM–ITD algorithm are established under mild conditions. Simulation studies are conducted to evaluate the empirical performance of the proposed method. An application of the proposed methodology to a real-life study on Alzheimer’s disease is presented.

Continuous time hidden Markov model for longitudinal data

Hidden Markov models (HMMs) describe the relationship between two stochastic processes, namely, an observed outcome process and an unobservable finite-state transition process. Given their ability to model dynamic heterogeneity, HMMs are extensively used to analyze heterogeneous longitudinal data. A majority of early developments in HMMs assume that observation times are discrete and regular. This assumption is often unrealistic in substantive research settings where subjects are intermittently seen and the observation times are continuous or not predetermined. However, available works in this direction restricted only to certain special cases with a homogeneous generating matrix for the Markov process. Moreover, early developments have mainly assumed that the number of hidden states of an HMM is fixed and predetermined based on the knowledge of the subjects or a certain criterion. In this article, we consider a general continuous-time HMM with a covariate specific generating matrix and an unknown number of hidden states. The proposed model is highly flexible, thereby enabling it to accommodate different types of longitudinal data that are regularly, irregularly, or continuously collected. We develop a maximum likelihood approach along with an efficient computer algorithm for parameter estimation. We propose a new penalized procedure to select the number of hidden states. The asymptotic properties of the estimators of the parameters and number of hidden states are established. Various satisfactory features, including the finite sample performance of the proposed methodology, are demonstrated through simulation studies. The application of the proposed model to a dataset of bladder tumors is presented.

Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states

Hidden Markov models (HMMs) are widely used to analyze heterogeneous longitudinal data owing to their capability to model dynamic heterogeneity. Early advancements in HMMs have mainly assumed that the number of hidden states is fixed and predetermined based on the knowledge of the subjects or a certain criterion. However, as a limitation, this approach determines the number of hidden states on a pairwise basis, which becomes increasingly tedious when the state space is enlarged. Moreover, criterion-based statistics tend to select complex models with overestimated numbers of components in mixture modeling. A full Bayesian approach is developed to analyze hidden Markov structural equation models with an unknown number of hidden states. An efficient and hybrid algorithm that combines the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm, the forward filtering and backward sampling scheme, and the Metropolis-Hastings algorithm is proposed to simultaneously select the number of hidden states and perform parameter estimation. The simulation study shows the satisfactory performance of the proposed method. Two real datasets collected from the UCLA Drug Abuse Research Center and National Longitudinal Survey of Youth are analyzed.

Bayesian analysis of latent Markov models with non-ignorable missing data

Latent Markov models (LMMs) are widely used in the analysis of heterogeneous longitudinal data. However, most existing LMMs are developed in fully observed data without missing entries. The main objective of this study is to develop a Bayesian approach for analyzing the LMMs with non-ignorable missing data. Bayesian methods for estimation and model comparison are discussed. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from National Longitudinal Survey of Youth 1997 is presented.

Finite mixture of varying coefficient model: Estimation and component selection

Heterogeneous longitudinal data have become prevalent in medical, biological, and social studies. This paper proposes a finite mixture of varying coefficient models for handling heterogeneous populations. Each component of the mixture is modeled by a varying coefficient mixed-effect model that characterizes the longitudinal relations among variables. The identifiability of the mixture model is studied. Regression splines with equally spaced knots are used to approximate the varying coefficient functions, and a nested expectation maximization algorithm is developed to obtain the maximum likelihood estimation. We propose a penalized likelihood method based on the smoothly clipped absolute deviation (SCAD) penalty for the component selection of finite mixture of varying coefficient model. A modified BIC-based criterion based on the SCAD penalty, the BICSCAD, is proposed for selecting the penalty parameter and spline space simultaneously. The asymptotic properties of parameter estimation and component selection consistency are studied under mild conditions. Simulation studies are conducted to illustrate the component selection, parameter estimation, and inference of the proposed method. The model is then applied to a heterogeneous longitudinal data set from a study of the treatment effect on the use of heroin in the California Civil Addict Program.

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Bayesian inference on mixed-effects location scale models with skew-t distribution and mismeasured covariates for longitudinal data.

In AIDS studies, heterogeneous between and within subject variations are often observed on longitudinal endpoints. To accommodate heteroscedasticity in the longitudinal data, statistical methods have been developed to model the mean and variance jointly. Most of these methods assume (conditional) normal distributions for random errors, which is not realistic in practice. In this article, we propose a Bayesian mixed-effects location scale model with skew-t distribution and mismeasured covariates for heterogeneous longitudinal data with skewness. The proposed model captures the between-subject and within-subject (WS) heterogeneity by modeling the between-subject and WS variations with covariates as well as a random effect at subject level in the WS variance. Further, the proposed model also takes into account the covariate measurement errors, and commonly assumed normal distributions for model errors are substituted by skew-t distribution to account for skewness. Parameter estimation is carried out in a Bayesian framework. The proposed method is illustrated with a Multicenter AIDS Cohort Study. Simulation studies are performed to assess the performance of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd.

Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors.

The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared.

Bayesian joint analysis of heterogeneous- and skewed-longitudinal data and a binary outcome, with application to AIDS clinical studies.

In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome. In this article, we propose a joint model in which a flexible finite mixture of nonlinear mixed-effects models with skew distributions is connected with binary logistic model by a latent class membership indicator. Simulation studies are conducted to assess the performance of the proposed models and method, and a real example from an AIDS clinical trial study illustrates the methodology by modeling the viral dynamics to compare potential models with different distribution specifications; the analysis results are reported.

Temporal electronic phenotyping by mining careflows of breast cancer patients

In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.

Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies

ABSTRACTFinite mixture models are currently used to analyze heterogeneous longitudinal data. By releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, finite mixture models not only can estimate model parameters but also cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, which might be associated with a clinically important binary outcome. This article develops a joint modeling of a finite mixture of NLME models for longitudinal data in the presence of covariate measurement errors and a logistic regression for a binary outcome, linked by individual latent class indicators, under a Bayesian framework. Simulation studies are conducted to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and logistic regression are fitted separately, followed by an application to a real data set from an AIDS clinical trial, in which the viral dynamics and dichotomized time to the first decline of CD4/CD8 ratio are analyzed jointly.

Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data

We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization.

An Introduction to Latent Variable Mixture Modeling (Part 2): Longitudinal Latent Class Growth Analysis and Growth Mixture Models

Pediatric psychologists are often interested in finding patterns in heterogeneous longitudinal data. Latent variable mixture modeling is an emerging statistical approach that models such heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of the second of a 2-article set is to offer a nontechnical introduction to longitudinal latent variable mixture modeling. 3 latent variable approaches to modeling longitudinal data are reviewed and distinguished. Step-by-step pediatric psychology examples of latent growth curve modeling, latent class growth analysis, and growth mixture modeling are provided using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999 data file. Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar longitudinal data patterns to determine the extent to which these patterns may relate to variables of interest.

The clinical course of alcohol use disorders: Using joinpoint analysis to aid in interpretation of growth mixture models

BackgroundThe clinical course of alcohol use disorders (AUD) is marked by great heterogeneity both within and between individuals. One approach to modeling this heterogeneity is latent growth mixture modeling (LGMM), which identifies a number of latent subgroups of drinkers with drinking trajectories that are similar within a latent subgroup but different between subgroups. LGMM is data-driven and uses an iterative process of testing a sequential number researcher-selected of latent subgroups then selecting the best fitting model. Despite the advantages of LGMM (e.g., identifying subgroups among heterogeneous longitudinal data), one limitation is the lack of precision of LGMM to model abrupt changes in drinking during treatment that are often observed by clinicians. Joinpoint analysis (JPA) is a data analysis procedure that is used to identify discrete change points in longitudinal data (e.g., changes from increasing to decreasing or decreasing to increasing). MethodThis study presents a demonstration of using JPA as a post hoc procedure for LGMM to improve accuracy in modeling abrupt changes in clinical course of AUD. ResultsResults from this secondary data analysis of 549 AUD participants participating in the NIAAA sponsored relapse replication and extension project uncovered four latent classes of drinking trajectories. DiscussionWithin these trajectories the addition of JPA improved precision in modeling the clinical course of AUDs.

Finite mixture varying coefficient models for analyzing longitudinal heterogenous data

This paper aims to develop a mixture model to study heterogeneous longitudinal data on the treatment effect of heroin use from a California Civil Addict Program. Each component of the mixture is characterized by a varying coefficient mixed effect model. We use the Bayesian P-splines approach to approximate the varying coefficient functions. We develop Markov chain Monte Carlo algorithms to estimate the smooth functions, unknown parameters, and latent variables in the model. We use modified deviance information criterion to determine the number of components in the mixture. A simulation study demonstrates that the modified deviance information criterion selects the correct number of components and the estimation of unknown quantities is accurate. We apply the proposed model to the heroin treatment study. Furthermore, we identify heterogeneous longitudinal patterns.