PubMed · 15700861
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Pat Healy. Good relations.. https://doi.org/10.7748/ns.19.19.18.s29
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Trend tests for genetic association using a matched case-control design are studied, which allows for a variable number of controls per case. However, the tests depend on the scores based on the underlying genetic model, thus it may result in substantial loss of power when the model is misspecified. Since the mode of inheritance may be unknown for complex diseases, robust trend tests in matched case-control studies are developed. Simulation is conducted to compare the trend tests and the robust trend tests under various genetic models. The results are applied to detect candidate-gene association using an example from a case-control aetiologic study of sarcoidosis.
In population-based cancer survival studies, the cause-specific survival measures the net survival (excess mortality) due to cancer when the cause of death information is available and reliable. In contrast, when the cause of death is uncertain or unavailable, relative survival, the ratio of the survival rate due to all causes to the expected survival rate, is more appropriate. There is a large body of work on the modelling and hypothesis testing of cause-specific survival, but many of these methods are not directly applicable to relative survival. In this paper, we extend the multiple imputation (MI) methods (Stat. Methods Med. Res. 1999; 8:3-15) to the case of relative survival data. The MI methodology is combined with relative survival to estimate the net survival by changing relative survival data to cause-specific data. This facilitates the direct application to statistical methods developed for the cause-specific survival to the special situation of relative survival. The parameter estimates and the log-rank statistics are obtained by combining the results from multiple imputed cause-specific data. The likelihood-based methods for modelling relative survival data have been implemented by a Windows application called CANSURV (Comput. Meth. Prog. Biomed 2005). Although these methods produce accurate parameter estimates, the choice for models and diagnostic tools is limited. The MI method is presented as a simpler alternative. The relative survival data for the colorectal cancer patients from Surveillance, Epidemiology, and End Results (SEER) program (SEER Cancer Statistics Review, 1973-1999. National Cancer Institute: Bethesda, 2002) is used as an illustration. The results are compared with those obtained from the likelihood-based relative survival analysis methods. A sample SAS macro for the MI method is provided.
BACKGROUND: The North Carolina-Louisiana Prostate Cancer Project (PCaP) is a multidisciplinary study of social, individual, and tumor-level causes of racial differences in prostate cancer aggressiveness. METHODS: A population-based sample of incident prostate cancer cases from North Carolina and Louisiana will include 1,000 African Americans and 1,000 Caucasian Americans. Study nurses administer structured questionnaires and collect blood, adipose tissue, urine, and toenail samples during an in-home visit. Clinical data are abstracted from medical records, diagnostic biopsies are reviewed and assayed, and tissue microarrays are constructed from prostatectomy samples. Prostate cancer aggressiveness is classified based on PSA, clinical stage, and Gleason grade. RESULTS: Preliminary data demonstrate between- and within-group differences in patient characteristics, screening, and treatment by race and state. Participation exceeds 70% in all groups. CONCLUSIONS: Preliminary data support the feasibility of this comprehensive study to help determine the focus of public health efforts to reduce racial disparities in prostate cancer mortality.