Inference on Competing Risks in Breast Cancer Data

While nonparametric methods have been well established for inference on competing risks data, parametric methods for such data have not been developed as much. Because the cumulative incidence functions are improper by their nature, flexible distribution families accommodating improperness are needed for modeling competing data more accurately. Additionally, different types of events present in a competing risks setting may be correlated, yet current inference methods do not permit inferring such data taking into account the correlation between failure time distributions. This work first presents two new distributions which are well-suited for modeling competing risks data. In existing inference procedures for competing risks data, it appears that the correlation between failure time distributions of competing events are fixed as a constant. In the second part of this dissertation, a novel approach is proposed which allows researchers to model competing risks data by taking the correlation into account by estimating it. The methods are illustrated by analyzing survival data from a breast cancer trial of the National Surgical Adjuvant Breast and Bowel Project. Simulation studies are also presented for each of the proposed new distributions.Public Health Significance: Competing risks occur often in many clinical studies, and must be accounted for whenever researchers are interested in only one type of event. For example, researchers may be interested in investigating only local recurrences of breast cancer, but must also take into account all other possible types of events as competing. Parametric methods are not currently as well established as other methods for competing risks data. Development of flexible parametric inference procedures suitable for modeling competing risks data would provide more accurate information, which will serve to improve patient care in clinical settings

Formation of the Wayne City High School into a Community Unit District

No abstract provided by author

Polymer solid acid composite membranes for fuel-cell applications

A systematic study of the conductivity of polyvinylidene fluoride (PVDF) and CsHSO4 composites, containing 0 to 100% CsHSO4, has been carried out. The polymer, with its good mechanical properties, served as a supporting matrix for the high proton conductivity inorganic phase. The conductivity of composites exhibited a sharp increase with temperature at 142°C, characteristic of the superprotonic phase transition of CsHSO4. At high temperature (160°C), the dependence of conductivity on vol % CsHSO4 was monotonic and revealed a percolation threshold of ~10 vol %. At low temperature (100°C), a maximum in the conductivity at ~80 vol % CsHSO4 was observed. Results of preliminary fuel cell measurements are presented

Challenges and Barriers to Resistance Training in Middle-Aged Women

Click the PDF icon to download the abstrac

Alcohol Fuel Cells at Optimal Temperatures

High-power-density alcohol fuel cells can relieve many of the daunting challenges facing a hydrogen energy economy. Here, such fuel cells are achieved using CsH2PO4 as the electrolyte and integrating into the anode chamber a Cu-ZnO/Al2O3 methanol steam-reforming catalyst. The temperature of operation, ~250°C, is matched both to the optimal value for fuel cell power output and for reforming. Peak power densities using methanol and ethanol were 226 and 100 mW/cm^2, respectively. The high power output (305 mW/cm^2) obtained from reformate fuel containing 1% CO demonstrates the potential of this approach with optimized reforming catalysts and also the tolerance to CO poisoning at these elevated temperatures