Modeling survival and the development of second primary tumors among Hodgkin's disease patients

Hodgkin's disease (HD) is a cancer of the lymphatic system. Survivors of HD face varieties of consequent adverse effects, in which secondary primary tumors (SPT) is one of the most serious consequences. This dissertation is aimed to model time-to-SPT in the presence of death and HD relapses during follow-up.^ The model is designed to handle a mixture phenomenon of SPT and the influence of death. Relapses of HD are adjusted as a covariate. Proportional hazards framework is used to define SPT intensity function, which includes an exponential term to estimate explanatory variables. Death as a competing risk is considered according to different scenarios, depending on which terminal event comes first. Newton-Raphson method is used to estimate the parameter estimates in the end.^ The proposed method is applied to a real data set containing a group of HD patients. Several risk factors for the development of SPT are identified and the findings are noteworthy in the development of healthcare guidelines that may lead to the early detection or prevention of SPT.^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^