Latent variable models for longitudinal study with informative missingness

Missing problem is very common in today's public health studies because of responses measured longitudinally. In this dissertation we proposed two latent variable models for longitudinal data with informative missingness. In the first approach, a latent variable model is developed for the categorical data, dividing the observed data into two latent classes: a 'regular' class and a 'special' class. Outcomes belonging to the regular class can be modeled using logistc regression and the outcomes in the special class have pre-deterministic values. Under the important assumption of conditional independence in the latent variable models, the longitudinal responses and the missingness process are independent given the latent classes. Parameters that we are interested in are estimated by the method of maximum likelihood based on the above assumption and correlation between responses. In the second approach, the latent variable in the proposed model is continuous and assumed to be normally distributed with unity variance. In the latent variable model, the values of the latent variable are affected by the missing patterns and the latent variable is also a covariate in modeling the longitudinal responses. We use the EM algorithm to obtain the estimates of the parameters and Gauss-Hermite quadrature is used to approximate the integral of the latent variable. The covariance matrix of the estimates can be calculated by using the bootstrap method or obtained from the inverse of the Fisher information matrix of the final marginal likelihood

BAYESIAN BASED SEMIPARAMETRIC MULTIDIMENSIONAL APPROACHES TO ANALYSIS PARKINSON\u27S DISEASE

In the slow progression of Parkinson\u27s Disease (PD), impairments arise and affect multiple domains (e.g., motor, cognitive, and behavioral). Mixed types, multivariate longitudinal data are commonly used in PD studies. Challenges exist in assessing PD status and investigating disease progression due to lack of biomarkers and ubiquitous impairment in the disease. We proposed a model framework by combining the semi-parametric approach and multi- dimensional framework, and used the proposed model to investigate the heterogeneous disease development and the non-linear treatment effects in the multiple domains predefined in PD. Furthermore, we extended the semi-parametric multidimensional approach to the data with multi-types endpoints. We investigated the multi-type events (competing risks) simultaneously with longitudinal profile in presence of impairment across domains and domain specific heterogeneous disease progression. Our approach provides an explicit framework for defining and estimating the impaired covariate effects, the association between domain specific longitudinal profile and multi-type endpoints. Lastly, we addressed the missing data in PD. We extended the multidimensional joint model to missing data by analyzing two missingness patterns (intermittent and monotone missingness) jointly in domain levels. We provided a statistical method for simultaneous likelihood inference on missing data in presence of two missingness patterns and two missing mechanisms, missing at random (MAR) and missing not at random (MNAR). In summary, the studies in this dissertation add to current PD studies by focusing on those ignored or not fully addressed problems in PD. The applications in longitudinal data, survival data and missing data promote this framework usability in public health research

Chronic Disease Data And Analysis: Current State Of the Field

Chronic disease usually spans years of a person’s lifetime and includes a disease free period, a preclinical, or latent period, where there are few overt signs of disease, a clinical period where the disease manifests and is eventually diagnosed, and a follow-up period where the disease might progress steadily or remain stable. It is often of interest to investigate the relationship between risk factors measured at a point in time (usually during the disease free or preclinical period), and the development of disease at some future point (e.g., 10 years later). We outline some popular designs for the identification of subjects and discuss issues in measurement of risk factors for analysis of chronic disease. We discuss some of the complexities in these analyses, including the time dependent nature of the risk factors and missing data issues. We then describe some popular statistical modeling techniques and outline the situations in which each is appropriate. We conclude with some speculation toward future development in the area of chronic disease data and analysis

Mechanisms Driving the Effect of Weight Loss on Arterial Stiffness

Aims Arterial stiffness decreases with weight loss in overweight and obese adults, but the mechanisms by which this occurs are poorly understood. We aimed to elucidate these mechanisms. Methods We evaluated carotid-femoral pulse wave velocity (cfPWV), a measure of aortic stiffness, and brachial-ankle pulse wave velocity (baPWV), a mixed measure of central and peripheral arterial stiffness, in 344 young adults (mean age 38 yrs, mean body mass index (BMI) 32.9 kg/m2, 23% male) at baseline, 6 and 12 months in a behavioral weight loss intervention. Linear mixed effects models were used to evaluate associations between weight loss and arterial stiffness and to examine the degree to which improvements in obesity-related factors explained these associations. Pattern-mixture models using indicator variables for dropout pattern and Markov Chain Monte Carlo multiple imputation were used to evaluate the influence of different missing data assumptions. Results At 6 months (7% mean weight loss from baseline), there was a statistically significant median decrease of 47.5 cm/s (interquartile range (IQR) -44.5, 148) in cfPWV (p<0.0001) and a mean decrease of 11.7 cm/s (standard deviation (SD) 91.4) in baPWV (p=0.049). At 12 months (6% mean weight loss from baseline) only cfPWV remained statistically significantly reduced from baseline (p=0.02). Change in BMI (p=0.01) was statistically significantly positively associated with change in cfPWV after adjustment for changes in mean arterial pressure (MAP) or any other measured obesity-related factor. Common carotid artery diameter (p=0.003) was associated and heart rate (p=0.08) and MAP (p=0.07) marginally associated longitudinally with cfPWV. Reductions in heart rate (p<0.0001) and C-reactive protein (p=0.02) were associated with reduced baPWV, and each removed the statistical significance of the effect of weight loss on baPWV. Pattern-mixture modeling revealed several differences between completers and non-completers in the models for cfPWV, but marginal parameter estimates changed little from the original models for either PWV measure. Conclusions The public health importance of this thesis is that firstly, weight loss improves arterial stiffness in overweight and obese young adults. Secondly, its effect on baPWV may be explained by concurrent reductions in heart rate and inflammation. Missing data did not appear to bias these results

United Stated Adolescent Health Literacy Development, Disparities, and Preventive Service Use Throughout the Lifecourse

Health literacy, an individuals ability to access and process health information to make health decisions, is an understudied topic among adolescents and young adults. Low health literacy potentially increases negative health outcomes such as chronic diseases, substance use, and overuse of health care services later in young adulthood. Understanding health literacy throughout the life course presents opportunity to decreases low health literacy, the associated negative health outcomes, and the onus its puts on society and the healthcare system. This dissertation aims to assess health literacy development during adolescent years with theoretical constructs geared towards health literacy development along with social and environmental factors. Adolescent health literacy geographic disparities are also explored. In addition, adolescent health literacy is assessed across specific time points during adolescence and young adulthood. The changes in health literacy from adolescence to young adulthood is evaluated along with changes in preventive service use during young adulthood. Data from the National Longitudinal Survey of Youth 1997 cohort (NLSY97) and the County and City Databook are used to evaluate the development of adolescent health literacy, geographic disparities in adolescent health literacy, and the associations of adolescent health literacy with preventive service use, health behaviors, and health outcomes. This research provides an assessment of adolescent health literacy at the national level and addresses important research gaps for understanding adolescent health literacy development, the geographic proportion of adolescent health literacy and, preventive service use over time. It also provides supporting evidence for health literacy changes throughout the life course. The results have implications for policies that address health literacy development and disparities among adolescents

Investigating emotion regulation and social information processing as mechanisms linking adverse childhood experiences with psychosocial functioning in young swiss adults: the FACE epidemiological accelerated cohort study.

BACKGROUND Adverse childhood experiences increase the risk for psychological disorders and lower psychosocial functioning across the lifespan. However, less is known about the processes through which ACE are linked to multiple negative outcomes. The aim of the FACE epidemiological study is to investigate emotion regulation (emotional reactivity, perseverative thinking and self-efficacy for managing emotions) and social information processing (rejection sensitivity, interpretation biases and social understanding) as potential mechanisms linking adverse childhood experiences and psychosocial functioning in a large population sample of young adults. It is embedded in a larger project that also includes an ecological momentary assessment of emotion regulation and social information processing and informs the development and evaluation of an online self-help intervention for young adults with a history of ACE. METHODS The study plans to recruit 5000 young adults aged 18 to 21 from the German-speaking Swiss population. Addresses are provided by Swiss Federal Statistical Office and participants are invited by mail to complete a self-report online survey. If the targeted sample size will not be reached, a second additional sample will be recruited via educational facilities such as universities or teacher training colleges or military training schools. Three follow-ups are planned after 1 year, 2 years and 3 years, resulting in ages 18-24 being covered. The main exposure variable is self-reported adverse childhood experiences before the age of 18, measured at the baseline. Primary outcomes are psychosocial functioning across the study period. Secondary outcomes are social information processing, emotion regulation and health care service use. Statistical analyses include a range of latent variable models to identify patterns of adverse childhood experiences and patterns and trajectories of psychosocial adaptation. DISCUSSION The results will contribute to the understanding of the underlying mechanisms that link ACE with psychosocial functioning which is crucial for an improved insight into risk and resilience processes and for tailoring interventions. Furthermore, the identification of factors that facilitate or hinder service use among young adults with ACE informs healthcare policies and the provision of appropriate healthcare services. TRIAL REGISTRATION NUMBER NCT05122988. The study was reviewed and authorized by the ethical committee of Northwestern and Central Switzerland (BASEC number 2021-01204)

Explaining patterns of age-specific performance

Individual life histories are frequently studied to gain insight into the mechanisms of ageing. However, various challenges complicate the accurate quantification of age-specific variation in fitness. In this thesis I develop and apply methods to accurately characterise patterns of ageing, and to explain why such patterns arise. All mammals and birds have an upper bound on litter size, and for many species this limit is quite low. In addition, in many species, not all individuals breed at every possible opportunity. Reproduction should consequently be considered as two processes: whether an individual breeds or not and the number of offspring produced. These processes mean that reproduction in many species does not follow a Poisson process as is often assumed in analyses of breeding performance. A more appropriate model for a repeated ordinal response like annual reproductive success is a proportional odds model with a random intercept for individuals. Such a model has not previously been used in ecology or evolutionary biology. I apply this model to analyse age and temporal variation in the number of fledglings produced annually by male and female common terns (Sterna hirundo). I use data collected from this intensively studied, long-lived species, repeatedly throughout the thesis. The proportional odds analysis reveal that reproductive performance in females initially increased with age, before declining as individuals began to senesce. But why does this pattern arise? Is it purely an effect of getting physiologically older or are other processes involved? I estimate the effect of the length of time spent with the current partner using the common tern data. Despite the quality of the data, it is not always obvious if unmarked partners are new or not. I use a hierarchical Bayesian model of the steps that lead to the number of fledglings. Modelling this complicated process requires a complex model, but results show that no substantial amount of observed age-related patterns in reproductive performance can be attributed to length of pair bond. While the proportional odds and Bayesian analyses account for repeated measures on individuals they do not account for compositional change. Such a change in the composition of the population caused by heterogeneity between individuals can mask true rates of individual change. I develop a novel retrospective decomposition method related to the Price equation to address this issue. The equation gives the exact contributions of selective disappearance and average change in individual performance among survivors to the aggregate change at the level of the population. This equation can be extended by including a term for the compositional change due to selective appearance of individuals in the study population. I apply this decomposition to the common tern dataset to disentangle whether apparent increases and decreases in reproductive performance with age reflect genuine changes within individuals or are an artefact of compositional change in a heterogeneous population. I show an improvement in average reproductive performance of individuals over most of adult life and give support for reproductive senescence at old ages. I show that the contribution of compositional change is of minor importance, suggesting that population-level averages accurately capture the individual-level ageing process well. Can the decomposition method I develop be applied to other systems? Does it lead to similar conclusions? I apply it to two different datasets dealing with functioning at old age in humans: the ability to live independently in the Danish 1905-cohort, and cognitive functioning for people aged 80 and older participating in the Chinese Longitudinal Health and Longevity Survey. In both studies I reveal that average individual functioning declines at old ages. Although the decline is also apparent at the population level it is less strong due to the tendency of individuals with lower functioning to drop out earlier. Finally, I illustrate the general use of the decomposition by applying it to epidemiological and economic studies in the appendix. Overall, I find that reproductive performance improves over many age classes before senescence begins. Numerous processes can influence rates of age-related change, with results apparently specific to the trait and population under study

Informative censoring with an imprecise anchor event: estimation of change over time and implications for longitudinal data analysis

A number of methods have been developed to analyze longitudinal data with dropout. However, there is no uniformly accepted approach. Model performance, in terms of the bias and accuracy of the estimator, depends on the underlying missing data mechanism and it is unclear how existing methods will perform when little is known about the missing data mechanism. Here we evaluate methods for estimating change over time in longitudinal studies with informative dropout in three settings: using a linear mixed effect (LME) estimator in the presence of multiple types of dropout; proposing an update to the pattern mixture modeling (PMM) approach in the presence of imprecision in identifying informative dropouts; and utilizing this new approach in the presence of prognostic factor by dropout interaction. We demonstrate that amount of dropout, the proportion of dropout that is informative, and the variability in outcome all affect the performance of an LME estimator in data with a mixture of informative and non-informative dropout. When the amount of dropout is moderate to large (>20% overall) the potential for relative bias greater than 10% increases, especially with large variability in outcome measure, even under scenarios where only a portion of the dropouts are informative. Under conditions where LME models do not perform well, it is necessary to take the missing data mechanism into account. We develop a method that extends the PMM approach to account for uncertainty in identifying informative dropouts. In scenarios with this uncertainty, the proposed method outperformed the traditional method in terms of bias and coverage. In the presence of interaction between dropout and a prognostic factor, the LME model performed poorly, in terms of bias and coverage, in estimating prognostic factor-specific slopes and the interaction between the prognostic factor and time. The update to the PMM approach, proposed here, outperformed both the LME and traditional PMM. Our work suggests that investigators must be cautious with any analysis of data with informative dropout. We found that particular attention must be paid to the model assumptions when the missing data mechanism is not well understood