Emerging adulthood is often-overlooked in current gastrointestinal (GI) health research; however, epidemiological evidence suggests that GI disorders are increasing in this population. The purpose of this study was to first define common GI symptom subgroups within emerging adults and then to characterize these group differences with key biopsychosocial factors encompassing diet, depression and anxiety symptoms, as well as physical and social functioning related to quality of life. A total of 956 emerging adults from a southeastern US university were surveyed on GI symptoms, psychosocial factors, and demographics. Latent class analysis uncovered three statistically significant GI symptom patterns within the sample identified by the degree of severity: Normal (n=649), Mild (n=257), and Moderate (n=50). This study demonstrated that significant impairment in GI functioning emerges at much earlier ages that are commonly assumed. In addition, these GI symptom levels were associated with important biopsychosocial factors. Assessing GI functioning in emerging adults may provide important insights into understanding the development of FGIDs.

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Latent class modelling is one method used in the evaluation of diagnostic tests when there is no gold standard test that is perfectly accurate. The technique demonstrates maximum likelihood estimates of the prevalence of a disease or a condition and the error rates of diagnostic tests or observers. This study reports the effect of departures from the latent class model assumption of independent misclassifications between observers or tests conditional on the true state of the individual being tested. It is found that estimates become biased in the presence of dependence. Most commonly the prevalence of the disease is overestimated when the true prevalence is at less than 50% and the error rates of dependent observers are underestimated. If there are also independent observers in the group, their error rates are overestimated. The most dangerous scenario in which to use latent class methods int he evaluation of tests is when the true prevalence is low and the false positive rate is high. This is common to many screening situations. / Thesis / Master of Science (MS)

Intimate partner violence (IPV) is a prevalent potentially traumatic experience that increases risk for posttraumatic stress symptoms (PTSS). However, there is still considerable heterogeneity in PTSS among women exposed to IPV. Research on IPV has examined the ways in which different characteristics of IPV exposure have separately related to risk for PTSS, specifically the type (physical, psychological, economic, sexual), frequency (number of incidents), severity (minor, severe), and mode of violence (in-person, online). However, it may be important to examine how the integration of these characteristics of IPV differ across ���������������������� ���� ������ ���� ������������ �������������������� �������������� ���������� The current study integrated these characteristics to assess classes of IPV and the relevant associations between concurrent and future PTSS. 264 women between the ages of 18-24 (Mage=20.41, SD=2.99) were recruited as part of a greater longitudinal study examining the relationship between PTSS and co-occurring psychopathology following exposure to IPV and/or sexual assault in the past year. Four classes of IPV across four characteristics of IPV (type, severity, frequency, and mode) were identified with latent class analysis (LCA). (1) history of both mild and severe psychological, physical, and sexual IPV in person and online, (2) history of mild and severe psychological IPV and mild sexual IPV occurring in person and online, (3) history of mild psychological IPV occurring in person and online, (4) past history of one type of IPV occurring in person. Class membership and concurrent and future PTSS were found to be associated with class membership.

No / Almost all studies in health research control or investigate socioeconomic position (SEP) as exposure or confounder. Different measures of SEP capture different aspects of the underlying construct, so efficient methodologies to combine them are needed. SEP and ethnicity are strongly associated, however not all measures of SEP may be appropriate for all ethnic groups. Methods We used latent class analysis (LCA) to define subgroups of women with similar SEP profiles using 19 measures of SEP. Data from 11,326 women were used, from eight different ethnic groups but with the majority from White British (40%) or Pakistani (45%) s, who were recruited during pregnancy to the Born in Bradford birth cohort study. Results Five distinct SEP subclasses were identified in the LCA: (i) "Least socioeconomically deprived and most educated" (20%); (ii) "Employed and not materially deprived" (19%); (iii) "Employed and no access to money" (16%); (iv) "Benefits and not materially deprived" (29%) and (v) "Most economically deprived" (16%). Based on the magnitude of the point estimates, the strongest associations were that compared to White British women, Pakistani and Bangladeshi women were more likely to belong to groups: (iv) "benefits and not materially deprived" (relative risk ratio (95% CI): 5.24 (4.44, 6.19) and 3.44 (2.37, 5.00), respectively) or (v) most deprived group (2.36 (1.96, 2.84) and 3.35 (2.21, 5.06) respectively) compared to the least deprived class. White Other women were more than twice as likely to be in the (iv) "benefits and not materially deprived group" compared to White British women and all ethnic groups, other than the Mixed group, were less likely to be in the (iii) "employed and not materially deprived" group than White British women. Conclusions LCA allows different aspects of an individual’s SEP to be considered in one multidimensional indicator, which can then be integrated in epidemiological analyses. Ethnicity is strongly associated with these identified subgroups. Findings from this study suggest a careful use of SEP measures in health research, especially when looking at different ethnic groups. Further replication of these findings is needed in other populations.

Background: Cannabis use (CU) and disorders (CUD) are highly prevalent among adolescents and young adults. We aim to identify clinically meaningful latent classes of users of cannabis and other illegal substances with distinct problem profiles. Methods: N= 3021 community subjects aged 14–24 at baseline were followed-up over a period ranging up to 10 years. Substance use (SU) and disorders (SUD) were assessed with the DSM-IV/M-CIDI. Latent class analysis (LCA) was conducted with a subset of N= 1089 subjects with repeated illegal SU. The variables entered in the LCA were CU-related problems, CUD, other SUD, and other mental disorders. Results: Four latent classes were identified: “Unproblematic CU” (class 1: 59.2%), “Primary alcohol use disorders” (class 2: 14.4%), “Delinquent cannabis/alcohol DSM-IV-abuse” (class 3: 17.9%), “CUD with multiple problems” (class 4: 8.5%). Range and level of CU-related problems were highest in classes 3 and 4. Comorbidity with other mental disorders was highest in classes 2 and 4. The probability of alcohol disorders and unmet treatment needs was considerable in classes 2–4. Conclusion: While the majority of subjects with repeated illegal SU did not experience notable problems over the 10-year period, a large minority (40.8%) experienced problematic outcomes, distinguished by clinically meaningful profiles. The data underline the need for specifically tailored interventions for adolescents with problematic CU and highlight the potentially important role of alcohol and other mental disorders.

Background: Cannabis use (CU) and disorders (CUD) are highly prevalent among adolescents and young adults. We aim to identify clinically meaningful latent classes of users of cannabis and other illegal substances with distinct problem profiles. Methods: N= 3021 community subjects aged 14–24 at baseline were followed-up over a period ranging up to 10 years. Substance use (SU) and disorders (SUD) were assessed with the DSM-IV/M-CIDI. Latent class analysis (LCA) was conducted with a subset of N= 1089 subjects with repeated illegal SU. The variables entered in the LCA were CU-related problems, CUD, other SUD, and other mental disorders. Results: Four latent classes were identified: “Unproblematic CU” (class 1: 59.2%), “Primary alcohol use disorders” (class 2: 14.4%), “Delinquent cannabis/alcohol DSM-IV-abuse” (class 3: 17.9%), “CUD with multiple problems” (class 4: 8.5%). Range and level of CU-related problems were highest in classes 3 and 4. Comorbidity with other mental disorders was highest in classes 2 and 4. The probability of alcohol disorders and unmet treatment needs was considerable in classes 2–4. Conclusion: While the majority of subjects with repeated illegal SU did not experience notable problems over the 10-year period, a large minority (40.8%) experienced problematic outcomes, distinguished by clinically meaningful profiles. The data underline the need for specifically tailored interventions for adolescents with problematic CU and highlight the potentially important role of alcohol and other mental disorders.

A Monte Carlo simulation study on class enumeration with latent class regression models. / Latent class regression (LCR) is a statistical method used to identify qualitatively different groups or latent classes within a heterogeneous population and commonly used in the behavioural, health, and social sciences. Despite the vast applications, an agreed fit index to correctly determine the number of latent classes is hotly debated. To add, there are also conflicting views on whether covariates should or should not be included into the class enumeration process. We conduct a simulation study to determine the impact of covariates on the class enumeration accuracy as well as study the performance of several commonly used fit indices under different population models and modelling conditions. Our results indicate that of the eight fit indices considered, the aBIC and BLRT proved to be the best performing fit indices for class enumeration. Furthermore, we found that covariates should not be included into the enumeration procedure. Our results illustrate that an unconditional LCA model can enumerate equivalently as well as a conditional LCA model with its true covariate specification. Even with the presence of large covariate effects in the population, the unconditional model is capable of enumerating with high accuracy. As noted by Nylund and Gibson (2016), a misspecified covariate specification can easily lead to an overestimation of latent classes. Therefore, we recommend to perform class enumeration without covariates and determine a set of candidate latent class models with the aBIC. Once that is determined, the BLRT can be utilized on the set of candidate models and confirm whether results obtained by the BLRT match the results of the aBIC. By separating the enumeration procedure of the BLRT, it still allows one to use the BLRT but reduce the heavy computational burden that is associated with this fit index. Subsequent analysis can then be pursued accordingly after the number of latent classes is determined. / Thesis / Master of Science (MSc)

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Objectives We first sought to assess if bicyclist typology was associated with health. Second, we investigated whether bicyclist typology was related to health through physical activity and commute bicycling. Finally, we sought to develop profiles of disposition toward commute bicycling following proposed changes to a specific destination and the significance of pertinent covariates. Methods Data from the 2014 Georgia State University-Bicycling Survey were used. We first estimated the adjusted odds of worse health-related quality of life by bicyclist typology. A mediation model was then used to estimate the relative total and direct effects of bicyclist typology on health-related quality of life and relative indirect effects through physical activity and commute bicycling. A finite mixture modeling approach was used to identify latent classes of disposition toward whether proposed changes to a specific destination would increase likelihood of commute bicycling. The manual 3-Step protocol was used to assess the effect of covariates on the probability of latent class membership. Results Respondents who had never bicycled, were not motivated to commute bicycle, and who required greater bicycle facilities to feel comfortable commute bicycling had higher odds of worse health-related quality of life. Physical activity and, to a lesser extent, commute bicycling status mediated the effect of bicyclist typology on health-related quality of life. The seven-class solution was decided on as the “best” model for disposition toward whether proposed destination improvements would increase the likelihood of commute bicycling. Several covariates were identified that impact the probability of latent class assignment. Conclusions Initial evidence of a health disparity by bicyclist typology was revealed. Physical activity appears to serve as the primary means through which bicyclist typology has an effect on health. Urban environments that make physical activity, including commute bicycling, more comfortable for a larger proportion of the population may be a potential important health intervention. Understanding the patterns of disposition toward whether proposed destination improvements would increase the likelihood of commute bicycling may assist in targeting and prioritizing commute bicycling-related interventions toward subpopulations of interest.