Marginally-specified Mean Models for Counts with Mixture Distributions

Counts from heterogeneous populations are often modeled using mixture distributions. These models assume that observations are generated from multiple unobserved subpopulations and estimate parameters having latent class interpretations. When interest is to make inferences about marginal means and incidence density ratios for the effects of risk factors in the overall population, regression coefficients obtained from common mixture models do not provide direct interpretations for these population-level parameters. While indirect techniques such as the use of post-modeling transformations may be employed to estimate the marginal effects of explanatory variables of interest, there are many instances where latent class model formulations fail to fully explain relationships between covariates and population-wide parameters (Preisser et al., 2012; Long et al., 2014). First, we employ two-component mixtures of non-degenerate count data distributions to estimate the overall effects of exposure variables on marginal means of zero-inflated and other heterogeneous counts. The models are examined using simulations and further applied to a double-blind dental caries incidence trial. Next, we develop a marginalized model for bivariate zero-inflated counts that allows the estimation of parameters for the overall effects of exposure variables on the marginal means of the two correlated outcomes. The model employs four-component mixture distributions and estimates marginally interpretable regression coefficients. We demonstrate the application of the method by using simulations and dental caries indices of primary and permanent teeth among children from a school-based fluoride mouthrinse study. Finally, extending earlier approaches, we propose an estimation method for marginalized zero-inflated count models when covariates are missing at random. The method, which can also be applied to other missing data problems, is illustrated and compared with complete case analysis by using simulations and dental data.Doctor of Philosoph

Post-Traumatic Stress Symptoms in Long-Term Non-Hodgkin's Lymphoma Survivors: Does Time Heal?

Little is known about the trajectory of post-traumatic stress disorder (PTSD) symptoms in cancer survivors, despite the fact that such knowledge can guide treatment. Therefore, this study examined changes in PTSD symptoms among long-term survivors of non-Hodgkin's lymphoma (NHL) and identified demographic, clinical, and psychosocial predictors and correlates of PTSD symptomatology

Quality of Life Among Long-Term Survivors of Non-Hodgkin Lymphoma: A Follow-Up Study

Little is known about change in quality of life (QOL) among long-term cancer survivors. We examined change over time in QOL among long-term survivors of non-Hodgkin lymphoma and identified demographic, clinical, and psychosocial risk factors for poor outcomes

Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs

The use of hierarchical Bayesian small area models, which take survey estimates along with auxiliary data as input to produce official statistics, has increased in recent years. Survey estimates for small domains are usually unreliable due to small sample sizes, and the corresponding sampling variances can also be imprecise and unreliable. This affects the performance of the model (i.e., the model will not produce an estimate or will produce a low-quality modeled estimate), which results in a reduced number of official statistics published by a government agency. To mitigate the unreliable sampling variances, these survey-estimated variances are typically modeled against the direct estimates wherever a relationship between the two is present. However, this is not always the case. This paper explores different alternatives to mitigate the unreliable (beyond some threshold) sampling variances. A Bayesian approach under the area-level model set-up and a distribution-free technique based on bootstrap sampling are proposed to update the survey data. An application to the county-level corn yield data from the County Agricultural Production Survey of the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) is used to illustrate the proposed approaches. The final county-level model-based estimates for small area domains, produced based on updated survey data from each method, are compared with county-level model-based estimates produced based on the original survey data and the official statistics published in 2016

Additional file 1 of Marginalized mixture models for count data from multiple source populations

Marginalized Mixture Models for Count Data from Multiple Source Populations. Supplemental Material. (PDF 100 kb

Quality of Life Among Long-Term Survivors of Non-Hodgkin Lymphoma: A Follow-Up Study

PURPOSE: Little is known about change in quality of life (QOL) among long-term cancer survivors. We examined change over time in QOL among long-term survivors of non-Hodgkin lymphoma and identified demographic, clinical, and psychosocial risk factors for poor outcomes. METHODS: Surveys were mailed to 682 lymphoma survivors who participated in a study 5 years earlier, when on average they were 10.4 years postdiagnosis. Standardized measures of QOL, perceptions of the impact of cancer, symptoms, medical history, and demographic variables were reported at both time points and examined using linear regression modeling to identify predictors of QOL over time. RESULTS: A total of 566 individuals participated (83% response rate) who were a mean of 15.3 years postdiagnosis; 52% were women, and 87% were white. One third of participants (32%) reported persistently high or improved QOL, yet a notable proportion (42%) reported persistently low or worsening QOL since the earlier survey. Participants who received only biologic systemic therapy reported improvement in physical health despite the passage of time. Older age, more comorbidity, and more or increasing negative and decreasing positive perceptions of cancer's impact were independent predictors of poor QOL. Lymphoma symptom burden, less social support, and having received a transplantation were related to negative perceptions of cancer's impact. CONCLUSION: Moderate to severe symptom burden, limited social support, or having received a transplantation should alert the clinician to potential need for supportive services. Perceptions of cancer's impact are associated with QOL cross-sectionally and longitudinally; modifying these perceptions may thus provide a strategy for improving QOL

Modelling changes in clinical attachment loss to classify periodontal disease progression

AimThe goal of this study was to identify progressing periodontal sites by applying linear mixed models (LMM) to longitudinal measurements of clinical attachment loss (CAL).MethodsNinety‐three periodontally healthy and 236 periodontitis subjects had their CAL measured bi‐monthly for 12 months. The proportions of sites demonstrating increases in CAL from baseline above specified thresholds were calculated for each visit. The proportions of sites reversing from the progressing state were also computed. LMM were fitted for each tooth site and the predicted CAL levels used to categorize sites regarding progression or regression. The threshold for progression was established based on the model‐estimated error in predictions.ResultsOver 12 months, 21.2%, 2.8% and 0.3% of sites progressed, according to thresholds of 1, 2 and 3 mm of CAL increase. However, on average, 42.0%, 64.4% and 77.7% of progressing sites for the different thresholds reversed in subsequent visits. Conversely, 97.1%, 76.9% and 23.1% of sites classified as progressing using LMM had observed CAL increases above 1, 2 and 3 mm after 12 months, whereas mean rates of reversal were 10.6%, 30.2% and 53.0% respectively.ConclusionLMM accounted for several sources of error in longitudinal CAL measurement, providing an improved method for classifying progressing sites.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134161/1/jcpe12539_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134161/2/jcpe12539.pd