Poor adherence is common in patients with diabetes mellitus and other chronic diseases that require extensive self-management. This behavior has been linked to increased complications, mortality rate, and health care costs. Although much effort has been put into studying the barriers to adherence and ways to improve both patient self-care and clinical outcomes, little success can be observed in the long run. Literature review of studies related to medication adherence in diabetes has shown a lack of uniformity in study parameters and statistical analysis making the juxtaposition of studies difficult and unreliable. Intervention studies in the field have shown general improvement in adherence rate in a short period of time, but rarely making any significant differences in clinical outcomes. Since diabetes mellitus is a chronic disease, it would be important to design studies measuring long term effects of interventions in the future.

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BACKGROUND: There is a need to identify the populations at high risk of nonadherence to outpatient lung cancer appointments in order to reduce the delay from diagnosis to treatment. The patient navigation system, which helps patients with barriers navigate the health care system, was examined to see if the correct high-risk groups were being addressed. METHODS: A case-control study with 195 subjects from the lung cancer clinics at Boston Medical Center (BMC) was conducted examining three nonadherence case groups: no-shows (n=40), cancelations (n=64) and combined (n=20). Nonadherence was defined as any patient who was a no-show for at least one appointment or who canceled more than one appointment over the three month study period. The combined group incorporated both of these factors. The patients were stratified by 10 patient characteristics, including patient navigation. Odds ratios (ORs) and 95% confidence intervals (CIs) were used for the analysis. A second analysis was done on patients in the patient navigation program (n=33) to determine if the high risk groups identified were being addressed. This was done using ORs and 95% CIs. RESULTS: This study has shown that there are certain patient groups in the lung cancer clinics at BMC that are at higher risk of being nonadherent to lung cancer outpatient appointments. Among those are Hispanic/Latino patients, Spanish and Haitian Creole speaking patients, small cell lung cancer (SCLC) patients, and those patients who have Medicaid, and with late stage lung cancer patients at significantly higher risk (no-shows: OR-5.26 (1.85, 14.95), cancelations: OR-2.49 (1.12, 5.54), combined: OR-12.49 (1.48, 105.46)). Patients in the patient navigation system were also found to be at significantly higher risk of nonadherence (no-shows: OR-3.85 (1.72, 8.65), cancelations: OR-4.13 (1.89, 9.00), combined: OR-5.15 (1.93, 13.72)) than those not in the program. Some patients were also found to be at significantly decreased odds of nonadherence, including those who were: 1000-1999 days post diagnosis (no-shows: OR-0.14 (0.03, 0.59), cancelations: OR-0.20 (0.06, 0.65), combined: OR-0.07 (0.01, 0.64)); 2000-2999 days post diagnosis (no-shows: OR-0.09 (0.01, 0.80), cancelations: OR-0.06 (0.01, 0.50)); aged 71-75 (cancelations: OR-0.25 (0.08, 0.79)). The subset analysis with the patient navigation data yielded no statistically significant results. CONCLUSIONS: The study identified high-risk populations within the total lung cancer population at BMC that should be addressed by the patient navigation program. This study demonstrated that while the program does have its flaws, it is decreasing the odds of nonadherence of many of the high-risk populations.

Over the past 13 years, the United States Army has been engaged in armed conflict within Afghanistan. Unfortunately, the United States Army has been forced to evacuate soldiers from the battlefield because of malaria, a parasitic disease that is endemic in Afghanistan. Even though the U.S. Army has adopted an effective chemoprophylaxis protocol, soldiers' adherence to their prescribed medication has been historically low. This research addressed a gap in literature regarding the adherence rates of U.S. Army enlisted soldiers to their prescribed oral malaria chemoprophylaxis. In addition, this research investigated self-reported reasons for soldiers' nonadherence to this medication. The study employed an experimental, correlational research design to aid in understanding the relationship between adherence to malaria chemoprophylaxis and age, gender, military rank, education level, and previous deployment experience. Ninety-four active-duty U.S. Army personnel deployed to Afghanistan participated in the study. The frequency distribution of responses to the 8-questions Morisky Medication Adherence Scale were presented and indicated that for almost all of the questions, the percentage of participants who answered yes was larger than the percentage who answered no, indicating low levels of adherence among the study participants. The findings indicated that age, gender, and perception of risk all significantly contributed to the models predicting medication adherence. With the scientific and medical advances of the 20th and 21st centuries, few if any military personnel should contract malaria. These findings contribute to a greater awareness of medication adherence, which directly supports positive social change within the Armed Forces of the United States.

Background: Findings are inconsistent regarding the degree to which depression may exert a negative impact on glycemic control in patients with type 2 diabetes. We therefore aimed to examine the longitudinal relationship between depression, behavioral factors, and glycemic control. Methods: In a prospective component of a nationally representative sample, 866 patients with type 2 diabetes aged ≧18 years completed a standardized assessment including a laboratory screening, questionnaires, and diagnostic measures. Subsequent to baseline (t0), patients were tracked over a period of 12 months (t1). Depression was assessed according to DSM-IV and ICD-10 criteria. Glycemic control was determined by levels of glycosylated hemoglobin (HbA1c); a level of ≧7% was judged as unsatisfactory. Regression analyses were performed to analyze the prospective relationship between depression, medication adherence, diabetes-related health behavior, and HbA1c. Results: Patients with depression at t0 revealed increased rates of medication nonadherence (adjusted OR: 2.67; CI: 1.38–5.15) at t1. Depression (adjusted regression coefficient: β = 0.96; p = 0.001) and subthreshold depression (β = 1.01; p < 0.001) at t0 also predicted increased problems with diabetes-related health behavior at t1. Adjusted ORs for poor glycemic control (HbA1c ≧7%) at t1 were also increased for patients with baseline depression (2.01; CI: 1.10–3.69). However, problems with medication adherence as well as problems with diabetes-related health behavior at t0 did not predict poor glycemic control at t1. Conclusions: In a prospective representative study of patients with type 2 diabetes, baseline depression predicted problems with medication adherence, problems with health-related behaviors, and unsatisfactory glycemic control at follow-up.

Siekiant nustatyti vaistų vartojimo nurodymų nesilaikymo dažnumą ir su nesilaikymu siejamus veiksnius, namie apklausėme 36 pacientus, kuriems gydymo arba profilaktikos tikslais buvo skirti antimikrobiniai vaistai. Nustatyta, kad nurodymų netiksliai laikėsi 11 pacient�� (30,6 proc.), iš jų 6 (16,7 proc.) suvartojo mažiau vaistų nei nurodė gydytojas, 5 (13,8 proc.) daugiau nei nurodė gydytojas. Veiksniai, statistiškai patikimai siejami su vaistų vartojimo nurodymų nesilaikymu, buvo dažnas augalinių vaistų vartojimas, valgymas 2 kartus per dieną ar rečiau ir sprendimų, prieštaraujančių tiesioginiams vadovo ar sutuoktinio nurodymams, nepriėmimas. Pacientų nurodytos neteisingo vaistų vartojimo priežastys buvo užmir���imas (4/11, 36 proc.), noras pabaigti pakuotę (3/11, 27 proc.), neteisingai suprasti nurodymai (2/11, 18 proc.), nepakankamai įtikinama diagnozė, nenoras maišyti vaistus su alkoholiu, sveikatos pagerėjimas (po 1/11, 9 proc.). / In order to determine the rate of patient nonadherence and factors, related to nonadherence, we questioned 36 patients, who had been prescribed antimicrobial preparations. It was determined, that 11 patients (30,6%) did not adhere to their medications accurately, 6 of them (16.7%) used less doses than prescribed and 5 patients (13,8%) used more doses than prescribed. Factors, that were found to be statistically reliably related to nonadherence, were often use of herbal medicines, having 2 or less meals a day, tendency not to make decisions that would contradict direct guidelines, provided by their spouses or leadership. Reasons for nonadherence, as defined by patients, were forgetting to take a dose (4/11, 36%), intention to finish all doses in package (3/11, 27%), misunderstanding of guidelines, provided by doctor (2/11, 18%), unreliable diagnosis, fear of interaction with alcohol, improved health (1/11, 9% each).

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<p>Clinical trials are the gold standard for inferring the causal effects of treatments or interventions. This thesis is concerned with the development of methodologies for two problems in modern clinical trials. First is analyzing binary repeated measures in clinical trials using models that reflect the complicated autocorrelation patterns in the data, so as to obtain high power when inferring treatment effects. Second is simulating realistic outcomes and subject nonadherence mechanisms in Phase III pharmaceutical clinical trials under the Tripartite Framework.</p><p> </p><p><b>Bayesian Models for Binary Repeated Data: The Bayesian General Logistic Autoregressive Model and the Polya-Gamma Logistic Autoregressive Model</b></p><p>Autoregressive processes in generalized linear mixed effects regression models are convenient for the analysis of clinical trials that have a moderate to large number of binary repeated measurements, collected across a fixed set of structured time points, for each subject. However, much of the existing literature and methods for autoregressive processes on repeated binary measurements permit only one order and only one autoregressive process in the model. This limits the flexibility of the resulting generalized linear mixed effects regression model to fully capture the dynamics in the data, which can result in decreased power for testing treatment effects. Nested autoregressive structures enable more holistic modeling of clinical trials that can lead to increased power for testing effects.</p><p> </p><p>We introduce the Bayesian General Logistic Autoregressive Model (BGLAM) for the analysis of repeated binary measures in clinical trials. The BGLAM extends previous Bayesian models for binary repeated measures by accommodating flexible and nested autoregressive processes with non-informative priors. We describe methods for selecting the order of the autoregressive process in the BGLAM based on the Deviance Information Criterion (DIC) and marginal log-likelihood, and develop an importance sampling-weighted posterior predictive p-value to test for treatment effects in BGLAM. The frequentist properties of BGLAM compared to existing likelihood- and non-likelihood-based statistical models are evaluated by means of extensive simulation studies involving different data generation mechanisms.</p><p> </p><p>Two features of BGLAM that can limit its application in practice is the computational effort involved in executing it and the inability to integrate added heterogeneity across time in its autoregressive processes. We develop the Polya-Gamma Logistic Autoregressive Model (PGLAM) for addressing these limiting features of the BGLAM. This new model enables the integration of additional layers of variability through random effects and heterogeneity across time in nested autoregressive processes. Furthermore, PGLAM is computationally more efficient than BGLAM because it eliminates the need to use the complex types of samplers for truncated latent variables that is involved in the Markov Chain Monte Carlo algorithm for BGLAM.</p><p> </p><p><b>Data Generating Model for Phase III Clinical Trials With Intercurrent Events</b></p><p>Although clinical trials are designed with strict controls, inevitably complications will arise during the course of the trials. One significant type of complication is missing subject outcomes due to subject drop-out or nonadherence during the trial, which are referred to in general as intercurrent events. This complication can arise from, among other causes, adverse reactions, lack of efficacy of the assigned treatment, administrative reasons, and excess efficacy from the assigned treatment. Intercurrent events typically confound causal inferences on the effects of the treatments under investigation because the missingness that occurs as a result corresponds to a Missing Not at Random missing data mechanism, the pharmaceutical industry is increasingly focused on developing methods for obtaining valid causal inferences on the receipt of treatment in clinical trials with intercurrent events. However, it is extremely difficult to compare the frequentist properties and performance of these competing methods, as real-life clinical trial data cannot be easily accessed or shared, and as the different methods consider distinct assumptions for the underlying data generating mechanism in the clinical trial. We develop a novel simulation model for clinical trials with intercurrent events. Our simulator operates under the Rubin Causal Model. We implement the simulator by means of an R Shiny application. This app enables users to control patient compliance through different sources of discontinuity with varying functional trends, and understand the frequentist properties of treatment effect estimators obtained by different models for various estimands.</p>

Developing countries like Nigeria continue to have HIV epidemic challenge due to the scarcity of evidence-based information and lack of resources to boost HIV education. The study population, Owerri, is one of the states in Nigeria with a high incidence rate of HIV. The purpose of this phenomenological study was to explore the experiences of people living with HIV/AIDS regarding the effects of nonadherence to HIV/AIDS drugs. The integrated theory of health behavior model provided the framework for the study. I collected, transcribed, and analyzed interview data to identify clusters and themes. Results showed that various factors influenced and (e.g., free drugs, fear, culture, medication side effects, discrimination, relationship/support system, poverty, belief, easy access) contributed to adherence behavior among respondents. People living with HIV/AIDS may be encouraged to adhere to drug treatments because of these research findings. This study contributed to a positive social change in that respondents were excited and open about sharing their fears, challenges, struggles and hope with the anticipation to influence others to be open about their HIV disease.

abstract: Understanding how adherence affects outcomes is crucial when developing and assigning interventions. However, interventions are often evaluated by conducting randomized experiments and estimating intent-to-treat effects, which ignore actual treatment received. Dose-response effects can supplement intent-to-treat effects when participants are offered the full dose but many only receive a partial dose due to nonadherence. Using these data, we can estimate the magnitude of the treatment effect at different levels of adherence, which serve as a proxy for different levels of treatment. In this dissertation, I conducted Monte Carlo simulations to evaluate when linear dose-response effects can be accurately and precisely estimated in randomized experiments comparing a no-treatment control condition to a treatment condition with partial adherence. Specifically, I evaluated the performance of confounder adjustment and instrumental variable methods when their assumptions were met (Study 1) and when their assumptions were violated (Study 2). In Study 1, the confounder adjustment and instrumental variable methods provided unbiased estimates of the dose-response effect across sample sizes (200, 500, 2,000) and adherence distributions (uniform, right skewed, left skewed). The adherence distribution affected power for the instrumental variable method. In Study 2, the confounder adjustment method provided unbiased or minimally biased estimates of the dose-response effect under no or weak (but not moderate or strong) unobserved confounding. The instrumental variable method provided extremely biased estimates of the dose-response effect under violations of the exclusion restriction (no direct effect of treatment assignment on the outcome), though less severe violations of the exclusion restriction should be investigated. / Dissertation/Thesis / Doctoral Dissertation Psychology 2018