Apply response adaptive randomization to group sequential with early stopping

Group sequential methods and response adaptive randomization (RAR) procedures have been applied in clinical trials due to economical and ethical considerations. Group sequential methods are able to reduce the average sample size by inducing early stopping, but patients are equally allocated with half of chance to inferior arm. RAR procedures incline to allocate more patients to better arm; however it requires more sample size to obtain a certain power. This study intended to combine these two procedures. We applied the Bayesian decision theory approach to define our group sequential stopping rules and evaluated the operating characteristics under RAR setting. The results showed that Bayesian decision theory method was able to preserve the type I error rate as well as achieve a favorable power; further by comparing with the error spending function method, we concluded that Bayesian decision theory approach was more effective on reducing average sample size.^

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

A systematic review of the methods used to assess race and racial disparities in uterine fibroid research

Context: Black women are reported to have a higher prevalence of uterine fibroids, and a threefold higher incidence rate and relative risk for clinical uterine fibroid development as compared to women of other races. Uterine fibroid research has reported that black women experience greater uterine fibroid morbidity and disproportionate uterine fibroid disease burden. With increased interest in understanding uterine fibroid development, and race being a critical component of uterine fibroid assessment, it is imperative that the methods used to determine the race of research participants is defined and the operational definition of the use of race as a variable is reported for methodological guidance, and to enable the research community to compare statistical data and replicate studies. ^ Objectives: To systematically review and evaluate the methods used to assess race and racial disparities in uterine fibroid research. ^ Data Sources: Databases searched for this review include: OVID Medline, NML PubMed, Ebscohost Cumulative Index to Nursing and Allied Health Plus with Full Text, and Elsevier Scopus. ^ Review Methods: Articles published in English were retrieved from data sources between January 2011 and March 2011. Broad search terms, uterine fibroids and race, were employed to retrieve a comprehensive list of citations for review screening. The initial database yield included 947 articles, after duplicate extraction 485 articles remained. In addition, 771 bibliographic citations were reviewed to identify additional articles not found through the primary database search, of which 17 new articles were included. In the first screening, 502 titles and abstracts were screened against eligibility questions to determine citations of exclusion and to retrieve full text articles for review. In the second screening, 197 full texted articles were screened against eligibility questions to determine whether or not they met full inclusion/exclusion criteria. ^ Results: 100 articles met inclusion criteria and were used in the results of this systematic review. The evidence suggested that black women have a higher prevalence of uterine fibroids when compared to white women. None of the 14 studies reporting data on prevalence reported an operational definition or conceptual framework for the use of race. There were a limited number of studies reporting on the prevalence of risk factors among racial subgroups. Of the 3 studies, 2 studies reported prevalence of risk factors lower for black women than other races, which was contrary to hypothesis. And, of the three studies reporting on prevalence of risk factors among racial subgroups, none of them reported a conceptual framework for the use of race. ^ Conclusion: In the 100 uterine fibroid studies included in this review over half, 66%, reported a specific objective to assess and recruit study participants based upon their race and/or ethnicity, but most, 51%, failed to report a method of determining the actual race of the participants, and far fewer, 4% (only four South American studies), reported a conceptual framework and/or operational definition of race as a variable. However, most, 95%, of all studies reported race-based health outcomes. The inadequate methodological guidance on the use of race in uterine fibroid studies, purporting to assess race and racial disparities, may be a primary reason that uterine fibroid research continues to report racial disparities, but fails to understand the high prevalence and increased exposures among African-American women. A standardized method of assessing race throughout uterine fibroid research would appear to be helpful in elucidating what race is actually measuring, and the risk of exposures for that measurement. ^

U.S. school based sealant programs: Review of their most common characteristics

In the United States, dental caries is the most common chronic illness in children, occurring five to eight times as frequently as asthma. 11 Dental caries is an unmet health need, disproportionately affecting minority groups and individuals with low socio-economic status.15,34,36 School-Based Sealant Programs were developed to target children at risk, to provide dental services in a closer geographic area, to offer low cost preventive dental services, and to educate families about oral health and prevention.1 There is scientific, evidence based literature that shows the effectiveness of dental sealants preventing dental decay. 13^ Currently, there is no central source for cataloging School-Based Sealant Programs (SBSPs). Information is scattered around publications and documents. For instance, the National Health and Nutrition Examination Survey (NHANES) does not have information about all the existing SBSPs. ^ This literature review determined which are the most common characteristics of SBSPs in the U.S. and determined the extent to which these programs provide sealants to children of low socio-economic status. The method utilized was an electronic database search. Pubmed and EBESCO host databases were searched with Mesh terms like “dental school sealant programs”, “community dentistry”, “school based sealant programs” and “oral preventive programs”. Results were organized in terms of location, population served, providers, funding source and data shared. ^ The searches produced 77 studies, from which 40 were included in this work. Only 18 U.S. states were represented in the results; however these findings are very consistent with the Best Practice Approach – School Based Sealant Programs3. Most of the SBSPs provide their services to children from low income families, and utilized the lower labor cost providers permitted by their state regulations. The author intends that this thesis work will become an aide in the development of future programs, and as evidence for the sustainability of these programs.^

Bifurcation and singularity analysis of a molecular network for the induction of long-term memory.

Withdrawal reflexes of the mollusk Aplysia exhibit sensitization, a simple form of long-term memory (LTM). Sensitization is due, in part, to long-term facilitation (LTF) of sensorimotor neuron synapses. LTF is induced by the modulatory actions of serotonin (5-HT). Pettigrew et al. developed a computational model of the nonlinear intracellular signaling and gene network that underlies the induction of 5-HT-induced LTF. The model simulated empirical observations that repeated applications of 5-HT induce persistent activation of protein kinase A (PKA) and that this persistent activation requires a suprathreshold exposure of 5-HT. This study extends the analysis of the Pettigrew model by applying bifurcation analysis, singularity theory, and numerical simulation. Using singularity theory, classification diagrams of parameter space were constructed, identifying regions with qualitatively different steady-state behaviors. The graphical representation of these regions illustrates the robustness of these regions to changes in model parameters. Because persistent protein kinase A (PKA) activity correlates with Aplysia LTM, the analysis focuses on a positive feedback loop in the model that tends to maintain PKA activity. In this loop, PKA phosphorylates a transcription factor (TF-1), thereby increasing the expression of an ubiquitin hydrolase (Ap-Uch). Ap-Uch then acts to increase PKA activity, closing the loop. This positive feedback loop manifests multiple, coexisting steady states, or multiplicity, which provides a mechanism for a bistable switch in PKA activity. After the removal of 5-HT, the PKA activity either returns to its basal level (reversible switch) or remains at a high level (irreversible switch). Such an irreversible switch might be a mechanism that contributes to the persistence of LTM. The classification diagrams also identify parameters and processes that might be manipulated, perhaps pharmacologically, to enhance the induction of memory. Rational drug design, to affect complex processes such as memory formation, can benefit from this type of analysis.

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

The transformation of a health care system: An ecology perspective

Beginning in the early 1980s, the health care system experienced momentous realignments. Fundamental changes in structures of traditional health care organizations, shifts in authority and relationships of professionals and institutions, and the increasing influence of managed care contributed to a relatively stable industry entering into a state of turbulence. The dynamics of these changes are recurring themes in the health services literature. The purpose of this dissertation was to examine the content of this literature over a defined time period and within the perspective of a theory of organizational change. ^ Using a theoretical framework based upon the organizational theory known as Organizational Ecology, secondary data from the period between 1983 and 1994 was reviewed. Analysis of the literature identified through a defined search methodology was focused upon determining the manner in which the literature characterized changes that were described. Using a model constructed from fundamentals of Organizational Ecology with which to structure an assessment of content, literature was summarized for the manner and extent of change in specific organizational forms and for the changes in emphasis by the environmental dynamics directing changes in the population of organizations. Although it was not the intent of the analysis to substantiate causal relationships between environmental resources selected as the determinants of organizational change and the observed changes in organizational forms, the structured review of content of the literature established a strong basis for inferring such a relationship. ^ The results of the integrative review of the literature and the power of the appraisal achieved through the theoretical framework constructed for the analysis indicate that there is considerable value in such an approach. An historical perspective on changes which have transformed the health care system developed within a defined organizational theory provide a unique insight into these changes and indicate the need for further development of such an analytical model. ^

The influence of social connectedness and self-esteem on participant retention in a bi-ethnic longitudinal study

In prospective studies it is essential that the study sample accurately represents the target population for meaningful inferences to be drawn. Understanding why some individuals do not participate, or fail to continue to participate, in longitudinal studies can provide an empirical basis for the development of effective recruitment and retention strategies to improve response rates. This study examined the influence of social connectedness and self-esteem on long-term retention of participants, using secondary data from the “San Antonio Longitudinal Study of Aging” (SALSA), a population-based study of Mexican Americans (MAs) and European Americans (EAs) aged over 65 years residing in San Antonio, Texas. We tested the effect of social connectedness, self-esteem and socioeconomic status on participant retention in both ethnic groups. In MAs only, we analyzed whether acculturation and assimilation moderated these associations and/or had a direct effect on participant retention. ^ Low income, low frequency of social contacts and length of recruitment interval were significant predictors of non-completer status. Participants with low levels of social contacts were almost twice as likely as those with high levels of social contacts to be non-completers, even after adjustment for age, sex, ethnic group, education, household income, and recruitment interval (OR = 1.95, 95% CI: 1.26–3.01, p = 0.003). Recruitment interval consistently and strongly predicted non-completer status in all the models tested. Depending on the model, for each year beyond baseline there was a 25–33% greater likelihood of non-completion. The only significant interaction, or moderating, effect observed was between social contacts and cultural values among MAs. Specifically, MAs with both low social contacts and low acculturation on cultural values (i.e., placed high value on preserving Mexican cultural origins) were three and half times more likely to be non-completers compared with MAs in other subgroups comprised of the combination of these variables, even after adjustment for covariates. ^ Long term studies with older and minority participants are challenging for participant retention. Strategies can be designed to enhance retention by paying special attention to participants with low social contacts and, in MAs, participants with both low social contacts and low acculturation on cultural values. Minimizing the time interval between baseline and follow-up recruitment, and maintaining frequent contact with participants during this interval should also be is integral to the study design.^