Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Delineation of a novel genetic region at 5q11 harboring tumor suppressor gene(s) and identification of the telomeric DNA as a marker for human prostate cancer

Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer mortality in American men. The distinction between those cases of prostate cancer destined to progress rapidly to lethal metastatic disease and those with little likelihood of causing morbidity and mortality is a major goal of current research. Some type of diagnostic method is urgently needed to identify which histological prostate cancers have completed the progression to a stage that will produce a life-threatening disease, thus requiring immediate therapeutic intervention. The objectives of this dissertation are to delineate a novel genetic region harboring tumor suppressor gene(s) and to identify a marker for prostate tumorigenesis. I first established an in vitro cell model system from a human prostate epithelial cells derived from tissue fragments surrounding a prostate tumor in a patient with prostatic adenocarcinoma. Since chromosome 5 abnormality was present in early, middle and late passages of this cell model system, I examined long-term established prostate cancer cell lines for this chromosome abnormality. The results implicated the region surrounding marker D5S2068 as the locus of interest for further experimentation and location of a tumor suppressor gene in human prostate cancer. ^ Cancer is a group of complex genetic diseases with uncontrolled cell; division and prostate cancer is no exception. I determined if telomeric DNA, and telomerase activity, alone or together, could serve as biomarkers of prostate tumorigenesis. I studied three newly established human prostate cancer cell lines and three fibroblast cell cultures derived from prostate tissues. In conclusion, my data reveal that in the presence of telomerase activity, telomeric repeats are maintained at a certain optimal length, and analysis of telomeric DNA variations might serve as early diagnostic and prognostic biomarkers for prostate cancer. (Abstract shortened by UMI.)^