Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved.

Associations between depressive symptoms and subjective cognitive decline among diverse hispanics/latinos: Results from the study of Latinos-investigation of Neurocognitive Aging (SOL-INCA).

Introduction: Depressive symptoms and subjective cognitive decline (SCD) are commonly reported prior to cognitive impairment. We examined associations between depressive symptoms and SCD among diverse Hispanic/Latino adults to better understand how depressive symptoms should be considered when interpreting SCD.

Methods: This cross-sectional study utilized data from Hispanic/Latino adults [n = 6189; Age: M= 63.

SEMIPARAMETRIC LINEAR REGRESSION WITH AN INTERVAL-CENSORED COVARIATE IN THE ATHEROSCLEROSIS RISK IN COMMUNITIES STUDY.

In longitudinal studies, investigators are often interested in understanding how the time since the occurrence of an intermediate event affects a future outcome. The intermediate event is often asymptomatic such that its occurrence is only known to lie in a time interval induced by periodic examinations. We propose a linear regression model that relates the time since the occurrence of the intermediate event to a continuous response at a future time point through a rectified linear unit activation function while formulating the distribution of the time to the occurrence of the intermediate event through the Cox proportional hazards model.

A Maximum Likelihood Method for High-Dimensional Structural Equation Modeling.

Factor analysis provides an intuitive approach for dimension reduction when working with big data, allowing researchers to represent an extensive number of correlated variables via a subset of underlying latent factors. Traditional methods of factor analysis, such as Structural Equation Modeling (SEM) and factor regression, lack properties desirable for analyzing big data, such as the ability to handle high-dimensionality or the ability to enforce sparsity on the estimates of the factor loading matrices. These methods also assume that the number of latent constructs is known beforehand, a problem unique to factor analysis that often goes unaddressed or overlooked, with ad hoc methods being the most common ways to deal with such a fundamental question.

Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens.

Dynamic treatment regimens (DTRs), where treatment decisions are tailored to individual patient's characteristics and evolving health status over multiple stages, have gained increasing interest in the modern era of precision medicine. Identifying important features that drive these decisions over stages not only leads to parsimonious DTRs for practical use but also enhances the reliability of learning optimal DTRs. Existing methods for learning optimal DTRs, such as Q-learning and O-learning, rely on a sequential procedure to estimate the optimal decision at each stage backward.

DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.

Alzheimer's Disease (AD) is a common neurodegenerative disorder impairing multiple domains. Recent AD studies, for example, the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, collect multimodal data to better understand AD severity and progression. To facilitate precision medicine for high-risk individuals, it is essential to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions of dementia occurrences.

Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference.

Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, i.e.

Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories.

Language features may reflect underlying cognitive and emotional processes following a traumatic event that portend clinical outcomes. The authors sought to determine whether language features from usual smartphone use were markers associated with concurrent posttraumatic symptoms and worsening or improving posttraumatic symptoms over time following a traumatic exposure. This investigation was a secondary analysis of the Advancing Understanding of RecOvery afteR traumA study, a longitudinal study of traumatic outcomes among survivors recruited from 33 emergency departments across the United States.

Expert consensus on imaging diagnosis and analysis of early correction of childhood malocclusion.

Early correction of childhood malocclusion is timely managing morphological, structural, and functional abnormalities at different dentomaxillofacial developmental stages. The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion. This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence, aiming to provide general guidance on appropriate imaging examination selection, comprehensive and accurate imaging assessment for early orthodontic treatment patients.

Improving power in adaptive expansion of biomarker populations in phase 3 clinical trials.

With the availability of unprecedented human genomic biomarker data, incorporating such biomarker data has received a lot of attention in phase 3 clinical trials. One particular enrichment design proposed recently in the literature is to recruit more biomarker positive patients in an all-comer study if the treatment effect in the biomarker negative group is less promising than expected. The intuition is to improve the chance of success of the trial since the success rate in the all-comer population may be low.

Continuous-Time Hidden Markov Factor Model for Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae.

Each year, a significant portion of the 40 million individuals in the United States who seek care in emergency departments (EDs) following traumatic experiences develop adverse posttraumatic neuropsychiatric sequelae (APNS). This highlights the widespread impact of trauma and the critical need for effective interventions to address the health outcomes of these events. Despite significant research efforts, advancements in understanding the neurobiological mechanisms of APNS have been hindered, primarily due to reliance on subjective self-reports, which are susceptible to recall biases and careless responses.

Development of a Radiomic Model to Detect the Retromolar Canal on Panoramic Radiographs.

Background: The retromolar canal (RMC) is an extension of the mandibular canal located in the distal region of the mandibular third molar. Accurately detecting the RMC using conventional two-dimensional images is challenging, potentially leading to anesthetic failure and sensory disorders. This study aims to explore the clinical application of a radiomic model based on panoramic radiographs in detecting the RMC.

Fusing Individualized Treatment Rules Using Secondary Outcomes.

An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their individual feature variables. In many practices, the ideal ITR for the primary outcome is also expected to cause minimal harm to other secondary outcomes. Therefore, our objective is to learn an ITR that not only maximizes the value function for the primary outcome, but also approximates the optimal rule for the secondary outcomes as closely as possible.

Optimizing personalized treatments for targeted patient populations across multiple domains.

Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population.

Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data.

Interval-censored multistate data arise in many studies of chronic diseases, where the health status of a subject can be characterized by a finite number of disease states and the transition between any two states is only known to occur over a broad time interval. We relate potentially time-dependent covariates to multistate processes through semiparametric proportional intensity models with random effects. We study nonparametric maximum likelihood estimation under general interval censoring and develop a stable expectation-maximization algorithm.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory.

Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes.

Cardiovascular disease risk exacerbates brain aging among Hispanic/Latino adults in the SOL-INCA-MRI Study.

Background: Cardiovascular disease (CVD) risk factors are highly prevalent among Hispanic/Latino adults, while the prevalence of MRI infarcts is not well-documented. We, therefore, sought to examine the relationships between CVD risk factors and infarcts with brain structure among Hispanic/Latino individuals.

Methods: Participants included 1,886 Hispanic/Latino adults (50-85 years) who underwent magnetic resonance imaging (MRI) as part of the Study of Latinos-Investigation of Neurocognitive Aging-MRI (SOL-INCA-MRI) study.

Associations of Allostatic Load with Level of and Change in Cognitive Function Among Middle-Aged and Older Hispanic/Latino Adults: The Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA).

Background: Higher allostatic load (AL), a multi-system measure of physiological dysregulation considered a proxy for chronic stress exposure, is associated with poorer global cognition (GC) in older non-Hispanic white adults. However, evidence of these associations in middle-aged and older US-based Hispanic/Latino adults is limited.

Objective: To examine associations of AL with level of cognition, performance in cognition 7 years later, and change in cognition over 7 years among middle-aged and older US-based Hispanic/Latino adults.

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.

Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components.

A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders.

Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability.