Chronic pain affects approximately 100 million adults in the United States annually, and costs exceeding $635 billion. Pain is the most common complaint in primary care, and chronic pain accounts for up to 16% of emergency room visits. Additionally, chronic pain accounts for 25% of missed workdays annually. Veterans are particularly vulnerable to chronic pain and have an increased incidence of chronic non-cancer pain. Chronic pain for veterans cost the Veterans Administration (VA) about $385 billion each year. This project evaluated the Patient Aligned Care Team (PACT) model to manage chronic lower back pain (CLBP) at a VA primary care center. The framework that guided the project was the theory of planned change and the chronic care model. A retrospective electronic chart review of demographic and pain management data was collected from a convenience sample of veterans (20 women, 20 men) with a history of CLBP managed by the primary care center for at least 1 year prior to and one year after the PACT model was implemented. Overall, the paired-samples t-test to was not statistically significant for improvements in veteran reported pain scores over time. However, there was a significant interaction between time and gender that indicates changes over time significantly differed because of gender. In addition, descriptively the mean pain levels were initially higher for men as compared to women, and these levels increased sharply for females over time while the men decreased. This project contributes positively to social change for veterans as the findings indicate an important gender difference in patient reported pain scores over time. There needs to be additional investigation to understand the etiology of the gender difference in the pain outcomes for CLBP.

Pharmacodynamic processes and disease progression are increasingly characterized with pharmacometric models. However, modelling options for discrete-type responses remain limited, although these response variables are commonly encountered clinical endpoints. Types of data defined as discrete data are generally ordinal, e.g. symptom severity, count, i.e. event frequency, and time-to-event, i.e. event occurrence. Underlying assumptions accompanying discrete data models need investigation and possibly adaptations in order to expand their use. Moreover, because these models are highly non-linear, estimation with linearization-based maximum likelihood methods may be biased. The aim of this thesis was to explore pharmacometric methods and novel models for discrete data through (i) the investigation of benefits of treating discrete data with different modelling approaches, (ii) evaluations of the performance of several estimation methods for discrete models, and (iii) the development of novel models for the handling of complex discrete data recorded during (pre-)clinical studies. A simulation study indicated that approaches such as a truncated Poisson model and a logit-transformed continuous model were adequate for treating ordinal data ranked on a 0-10 scale. Features that handled serial correlation and underdispersion were developed for the models to subsequently fit real pain scores. The performance of nine estimation methods was studied for dose-response continuous models. Other types of serially correlated count models were studied for the analysis of overdispersed data represented by the number of epilepsy seizures per day. For these types of models, the commonly used Laplace estimation method presented a bias, whereas the adaptive Gaussian quadrature method did not. Count models were also compared to repeated time-to-event models when the exact time of gastroesophageal symptom occurrence was known. Two new model structures handling repeated time-to-categorical events, i.e. events with an ordinal severity aspect, were introduced. Laplace and two expectation-maximisation estimation methods were found to be performing well for frequent repeated time-to-event models. In conclusion, this thesis presents approaches, estimation methods, and diagnostics adapted for treating discrete data. Novel models and diagnostics were developed when lacking and applied to biological observations.