CONTINUOUS TIME MULTI-STATE MODELS FOR INTERVAL CENSORED DATA

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models. Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant. Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline. Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study

Self-Reported Memory Complaints: Implications from a Longitudinal Cohort with Autopsies

OBJECTIVE: We assessed salience of subjective memory complaints (SMCs) by older individuals as a predictor of subsequent cognitive impairment while accounting for risk factors and eventual neuropathologies. METHODS: Subjects (n = 531) enrolled while cognitively intact at the University of Kentucky were asked annually if they perceived changes in memory since their last visit. A multistate model estimated when transition to impairment occurred while adjusting for intervening death. Risk factors affecting the timing and probability of an impairment were identified. The association between SMCs and Alzheimer-type neuropathology was assessed from autopsies (n = 243). RESULTS: SMCs were reported by more than half (55.7%) of the cohort, and were associated with increased risk of impairment (unadjusted odds ratio = 2.8, p \u3c 0.0001). Mild cognitive impairment (dementia) occurred 9.2 (12.1) years after SMC. Multistate modeling showed that SMC reporters with an APOE ε4 allele had double the odds of impairment (adjusted odds ratio = 2.2, p = 0.036). SMC smokers took less time to transition to mild cognitive impairment, while SMC hormone-replaced women took longer to transition directly to dementia. Among participants (n = 176) who died without a diagnosed clinical impairment, SMCs were associated with elevated neuritic amyloid plaques in the neocortex and medial temporal lobe. CONCLUSION: SMC reporters are at a higher risk of future cognitive impairment and have higher levels of Alzheimer-type brain pathology even when impairment does not occur. As potential harbingers of future cognitive decline, physicians should query and monitor SMCs from their older patients

Mild Cognitive Impairment: Statistical Models of Transition Using Longitudinal Clinical Data

Mild cognitive impairment (MCI) refers to the clinical state between normal cognition and probable Alzheimer's disease (AD), but persons diagnosed with MCI may progress to non-AD forms of dementia, remain MCI until death, or recover to normal cognition. Risk factors for these various clinical changes, which we term “transitions,” may provide targets for therapeutic interventions. Therefore, it is useful to develop new approaches to assess risk factors for these transitions. Markov models have been used to investigate the transient nature of MCI represented by amnestic single-domain and mixed MCI states, where mixed MCI comprised all other MCI subtypes based on cognitive assessments. The purpose of this study is to expand this risk model by including a clinically determined MCI state as an outcome. Analyses show that several common risk factors play different roles in affecting transitions to MCI and dementia. Notably, APOE-4 increases the risk of transition to clinical MCI but does not affect the risk for a final transition to dementia, and baseline hypertension decreases the risk of transition to dementia from clinical MCI

Self-Reported Head Injury and Risk of Late-Life Impairment and AD Pathology in an AD Center Cohort

Aims: To evaluate the relationship between self-reported head injury and cognitive impairment, dementia, mortality, and Alzheimer\u27s disease (AD)-type pathological changes. Methods: Clinical and neuropathological data from participants enrolled in a longitudinal study of aging and cognition (n = 649) were analyzed to assess the chronic effects of self-reported head injury. Results: The effect of self-reported head injury on the clinical state depended on the age at assessment: for a 1-year increase in age, the OR for the transition to clinical mild cognitive impairment (MCI) at the next visit for participants with a history of head injury was 1.21 and 1.34 for the transition from MCI to dementia. Without respect to age, head injury increased the odds of mortality (OR = 1.54). Moreover, it increased the odds of a pathological diagnosis of AD for men (OR = 1.47) but not women (OR = 1.18). Men with a head injury had higher mean amyloid plaque counts in the neocortex and entorhinal cortex than men without. Conclusions: Self-reported head injury is associated with earlier onset, increased risk of cognitive impairment and dementia, increased risk of mortality, and AD-type pathological changes

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Efficient Estimation in Heteroscedastic Varying Coefficient Models

This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method

A Marketplace Price Anomaly Detection System at Scale

Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets

Effects of phosphate on biotite dissolution and secondary precipitation under conditions relevant to engineered subsurface processes.

Brine-mica interfacial interactions affect both the caprock integrity and the fate and transport of reactive fluids at deep subsurface sites. Phosphate naturally exists at low concentration in subsurface brines, and its concentration can be increased significantly during energy-related engineered subsurface processes. However, our understanding of the influence of phosphate on brine-mica interactions is limited, especially under subsurface conditions. Here, biotite dissolution experiments were conducted without and with phosphate (0.1, 1, and 10 mM) at 95 °C and 102 atm CO2. Compared to the control, 0.1 mM, and 1 mM phosphate systems, biotite dissolution was four times higher with 10 mM phosphate. Despite the dissolution differences, in all the phosphate systems, phosphate interacted with Al and Fe sites in biotite, forming surface complexation and precipitating as Fe- or Al-bearing minerals on surfaces and in solutions. Consequently, aqueous Fe and Al concentrations became lower with phosphate than in the control experiments. In addition, the biotite basal surfaces became more hydrophilic after reaction with phosphate, even at 0.1 mM, mainly from phosphate adsorption. This study offers new information on how phosphate-containing brine interacts with caprocks and on the consequent wettability changes, results that can benefit current and future energy-related subsurface engineering processes

Effects of Salinity-Induced Chemical Reactions on Biotite Wettability Changes under Geologic CO₂ Sequestration Conditions

The wettability of rocks and minerals significantly affects the safety and efficiency of energy-related subsurface operations. Salinity is an important controlling factor in terms of wettability but has received limited attention. We studied the effects of salinity-induced chemical reactions on biotite’s wettability changes under relevant subsurface conditions. Biotite was reacted at 95 °C and 102 atm of CO₂ for 70 h in solutions with salinities of 0, 0.1, 0.5, and 1.0 M NaCl. Then, static and dynamic water contact angles on reacted biotite basal surfaces were measured using a captive drop method. As a result of enhanced biotite dissolution at higher salinities, increased roughness, more negatively charged surfaces, and higher densities of hydroxyl groups on the biotite surfaces made biotite basal surface more hydrophilic. These results provide new information about the interplay of chemical reactions and wettability alterations of minerals, providing a better understanding of CO₂ transport in subsurface environments