A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^

Measuring value in cancer-related health care: A case study in head and neck cancer

In the current climate of escalating health care costs, defining value and accurately measuring it are two critical issues affecting not only the future of cancer care in particular but also the future of health care in general. Specifically, measuring and improving value in cancer-related health care are critical for continued advancements in research, management, and overall delivery of care. However, in oncology, most of this research has focused on value as it relates to insurance industry and payment reform, with little attention paid to value as the output of clinical interventions that encompass integrated clinical teams focusing on the entire cycle of care and measuring objective outcomes that are most relevant to patients. ^ In this study, patient-centered value was defined as health outcomes achieved per dollar spent, and calculated using objective functional outcomes and total care costs. The analytic sample comprised patients diagnosed with three common head and neck cancers—cancer of the larynx, oral cavity, and oropharynx—who were treated in an integrated tertiary care center over an approximately 10-year period. The results of this study provide initial empirical data that can be used to assess and ultimately to help improve the quality and value of head and neck cancer care, and more importantly they can be used by patients and clinicians to make better-informed decisions about care, particularly what therapeutic services and outcomes matter the most to patients.^