With the increase in research on age-related decline in human cognitive capabilities has come an increased demand for a nonhuman cognitive aging model that controls for confounds in the human research, including education, socio-economic class, and language abilities. A nonhuman primate model of cognitive aging is particularly attractive given the similarities in physiology and behavior between nonhuman primates and humans. The rhesus macaque has proven to be a highly effective model of human aging, but apes, the closest genetic relatives to humans, are virtually unstudied. Only three studies have examined age-related changes in cognitive abilities in apes, and all three studies were conducted using tasks that have had relatively little success in revealing age-related performance decline in monkeys. Sixteen gorillas housed at Zoo Atlanta and Disneys Animal Kingdom underwent five experiments to determine the effects of age, sex, and previous research experience on performance. Testing occurred in a modified Wisconsin General Test Apparatus using the delayed response paradigm. When delay intervals were increased from zero to 90 seconds performance decreased across all subjects. However, there was no univariate effect of age, sex, or experience on performance. Similarly, when the number of potential choice sites was increased from two to four there was a decrease in performance, but again there was no univariate effect of age, sex, or experience. However, older subjects were more likely to have a response bias and choose a single site across all tasks, irrespective of where the reward was located. This effect was more pronounced in inexperienced subjects. As a result, age does not appear to affect performance accuracy in the delayed response task in gorillas, but there may be an effect on response rigidity. Given the shortcomings of the delayed response task in simple cognitive studies with nonhuman primates, further research on cognitive aging in gorillas should utilize a research naïve population and use an experimental protocol that is titrated based on individual responses. This will allow the delay interval to be gradually increased to a point where age-related differences may be apparent.

We have constructed a response adaptive clinical trial to treat patients sequentially in order to maximize the total survival time of all patients. Typically the response adaptive design is based on the urn models or on sequential estimation procedures, but we used a bandit process in this dissertation. The objective of a bandit process is to optimize a measure of sequential selections from several treatments. Each treatment consist of a sequence of conditionally independent and identically distributed random variables, and some of these treatment have unknown distribution functions. For the purpose of this clinical trial, we are focusing on the bandit process with delayed response. These responses are lifetime variables which may be censored upon their observations. Following the Bayesian approach and dynamic programming technique, we formulated a controlled stochastic dynamic model. In addition, we used an example to illustrate the possible application of the main results as well as "R" to implement a model simulation.

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Response adaptive designs intend to allocate more patients to better treatments without undermining the validity and the integrity of the trial. The immediacy of the primary response (e.g. deaths, remission) determines the efficiency of the response adaptive design, which often requires outcomes to be quickly or immediately observed. This presents difficulties for survival studies, which may require long durations to observe the primary endpoint. Therefore, we introduce auxiliary endpoints to assist the adaptation with the primary endpoint, where an auxiliary endpoint is generally defined as any measurement that is positively associated with the primary endpoint. Our proposed design (referred to as bivariate adaptive design) is based on the classical response adaptive design framework. The connection of auxiliary and primary endpoints is established through Bayesian method. We extend parameter space from one dimension to two dimensions, say primary and auxiliary efficacies, by implementing a conditional weigh function on the loss function of the design. The allocation ratio is updated at each stage by optimization of the loss function subject to the information provided for both the auxiliary and primary outcomes. We demonstrate several methods of joint modeling the auxiliary and primary outcomes. Through simulation studies, we show that the bivariate adaptive design is more effective in assigning patients to better treatments as compared with univariate optimal and balanced designs. As hoped, this joint-approach also reduces the expected number of patient failures and preserves the comparable power as compared with other designs.