Thesis (M.S.)--University of Michigan, 1983. / "A research report submitted in partial fulfillment of the requirements for the degree ..."

Thesis (M.S.)--University of Michigan, 1983. / "A research report submitted in partial fulfillment of the requirements for the degree ..."--T.p.

Thesis (Ed. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vi, 166 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 102-116).

Highway safety evaluation has traditionally been performed using crash data. However crash data based safety analysis has limitations in terms of timeliness and efficiency. Previous studies show that the use of surrogate safety data allows for earlier evaluation of safety in comparison to the significantly longer time horizon required for collecting crash data. However, the predictive capability of surrogate measures is an area of ongoing research. Previous studies have often resulted in inconsistent findings in the relationship between surrogates and crashes, one of the primary reasons being inconsistent definitions of a conflict. This study evaluated the effectiveness of certain surrogate measures (Acceleration-Deceleration profile, intersection entering speed of through vehicles, and Post Encroachment Time (PET)) in assessing the safety of opposing left-turn interactions at 4-legged signalized intersections by collection of time resolved video from eighteen selected intersections throughout Georgia. Overall, this research demonstrated that surrogate measures can be effective in safety evaluation, specifically demonstrating the use of PET as a surrogate for crashes between left-turning vehicles and opposing through vehicles. The analysis of data found that the selected surrogate threshold is critical to the effectiveness of any surrogate measure. For example, the required PET threshold was found to be as low as 1 second to identify high crash intersections, significantly lower than the commonly reported 3 second threshold. Non-parametric rank analysis methods and generalized linear modeling techniques were used to model PET with other intersection and traffic characteristics to demonstrate the degree to which these surrogates can be used to identify potential high-crash intersections without resorting to a crash history. Finally, the effectiveness of PET and its assistance to decision makers is also been demonstrated through an example that helped find errors in reported crash data.

No description available.

<p>Using surrogate approximations (e.g. Kriging interpolation or artifical neural networks) is an established technique for decreasing the execution time of simulation optimization problems. However, constructing surrogate approximations can be impossible when facing complex simulation inputs, and instead one is forced to use a surrogate model, which explicitly attempts to simulate the inner workings of the underlying simulation model. This dissertation has investigated if postprocessing the output of a surrogate model with an artificial neural network can increase its accuracy and value in simulation optimization problems. Results indicate that the technique has potential in that when output post-processing was enabled the accuracy of the surrogate model increased, i.e. its output more losely matched the output of the real simulation model. No apparent improvement in optimization performance could be observed however. It was speculated that this was due to either the optimization algorithm used not taking advantage of the improved accuracy of the surrogate model, or the fact the the improved accuracy of the surrogate model was to small to make any measurable impact. Further investigation of these issues must be conducted in order to get a better understanding of the pros and cons of the technique.</p>

Using surrogate approximations (e.g. Kriging interpolation or artifical neural networks) is an established technique for decreasing the execution time of simulation optimization problems. However, constructing surrogate approximations can be impossible when facing complex simulation inputs, and instead one is forced to use a surrogate model, which explicitly attempts to simulate the inner workings of the underlying simulation model. This dissertation has investigated if postprocessing the output of a surrogate model with an artificial neural network can increase its accuracy and value in simulation optimization problems. Results indicate that the technique has potential in that when output post-processing was enabled the accuracy of the surrogate model increased, i.e. its output more losely matched the output of the real simulation model. No apparent improvement in optimization performance could be observed however. It was speculated that this was due to either the optimization algorithm used not taking advantage of the improved accuracy of the surrogate model, or the fact the the improved accuracy of the surrogate model was to small to make any measurable impact. Further investigation of these issues must be conducted in order to get a better understanding of the pros and cons of the technique.

No description available.

Background Surrogate outcomes are measures of treatment effect that can be used to predict treatment effect on the true outcome of interest. Surrogates are valued as they can be used in place of true outcomes to reduce the length, size, or intrusiveness of a clinical trial. However, validation of surrogacy is a conceptually complicated area and much theoretical and practical statistical development has been conducted in recent years. Methods A systematic review was conducted to identify which surrogate evaluation approach was best suited to be extended to ordinal outcomes. I extended a foremost approach to the case where the surrogate, the true clinical outcome, or both are ordinal outcomes. This extension investigated surrogacy at both the trial and individual levels; trial level surrogacy was based on a two stage method. The extension was developed through large simulation studies and used to investigate whether deep venous thromboembolism (DVT) was a surrogate for the ongoing measure of death and disability the Oxford Handicap Scale (OHS), using data from the stroke trial CLOTS3. CLOTS3 was a large multi-centre randomised clinical trial which investigated whether intermittent pneumatic compression (IPC) applied to the legs reduced the occurrence of deep venous thromboembolism (DVT) in stroke clinical trial patients. Results The systematic review identified the information theory approach as the most intuitively and practically worthwhile approach to surrogacy evaluation. I extended this approach to: a binary surrogate and ordinal true outcome (the binary-ordinal setting); the ordinal-binary and the ordinal-ordinal settings. The simulation studies showed that the approach worked well in most scenarios tested. However, trial level surrogacy was impacted by loss of efficiency due to the use of the two stage method. Bias imposed at the trial level by separation of discrete outcomes was effectively dealt with using a penalised likelihood method. The information theory approach for ordinal outcomes identified no surrogate that would predict treatment effect of IPC on the true outcome OHS measured at six months in the stroke trial CLOTS3.

We consider a Bayesian approach to the study of independence in a two-way contingency table obtained from a two-stage cluster sampling design. We study the association between two categorical variables when (a) there are no covariates and (b) there are covariates at both unit and cluster levels. Our main idea for the Bayesian test of independence is to convert the cluster sample into an equivalent simple random sample which provides a surrogate of the original sample. Then, this surrogate sample is used to compute the Bayes factor to make an inference about independence. For the test of independence without covariates, the Rao-Scott corrections to the standard chi-squared (or likelihood ratio) statistic were developed. They are ``large sample' methods and provide appropriate inference when there are large cell counts. However, they are less successful when there are small cell counts. We have developed the methodology to overcome the limitations of Rao-Scott correction. We have used a hierarchical Bayesian model to convert the observed cluster samples to simple random samples. This provides the surrogate samples which can be used to derive the distribution of the Bayes factor to make an inference about independence. We have used a sampling-based method to fit the model. For the test of independence with covariates, we first convert the cluster sample with covariates to a cluster sample without covariates. We use multinomial logistic regression model with random effects to accommodate the cluster effects. Our idea is to fit the cluster samples to the random effect models and predict the new samples by adjusting with the covariates. This provides the cluster sample without covariates. We then use a hierarchical Bayesian model to convert this cluster sample to a simple random sample which allows us to calculate the Bayes factor to make an inference about independence. We use Markov chain Monte Carlo methods to fit our models. We apply our first method to the Third International Mathematics and Science Study (1995) for third grade U.S. students in which we study the association between the mathematics test scores and the communities the students come from, and science test scores and the communities the students come from. We also provide a simulation study which establishes our methodology as a viable alternative to the Rao-Scott approximations for relatively small two-stage cluster samples. We apply our second method to the data from the Trend in International Mathematics and Science Study (2007) for fourth grade U.S. students to assess the association between the mathematics and science scores represented as categorical variables and also provide the simulation study. The result shows that if there is strong association between two categorical variables, there is no difference between the significance of the test in using the model (a) with covariates and (b) without covariates. However, in simulation studies, there is a noticeable difference in the significance of the test between the two models when there are borderline cases (i.e., situations where there is marginal significance).