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Yen Lin Chia

Publications and source records attributed to Yen Lin Chia.

2 recordsLinked to original sources

The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the U.S. SEER database.

PURPOSE: To examine the effect of patient and tumor characteristics on breast cancer survival as recorded in the U.S. National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 1998. METHODS: A sample of 72,367 female cases from 1973 to 1998 aged 21-90 years with invasive ductal breast cancer were examined with Cox proportional hazards regression to determine the effect of age at diagnosis, race, tumor size, tumor grade, disease stage, and year of diagnosis on disease-specific survival. RESULTS: Larger tumor size and higher tumor grade were found to have large negative effects on survival. Blacks had a 47 % greater risk of death than whites. Year of diagnosis had a positive effect, with a 15 % reduction in risk for each decade in the time period under study. The effects of patient age and disease stage violated the proportional hazards assumption, with distant disease having much poorer short-term survival than one would expect from a proportional hazards model, and younger age groups matching or even falling below the survival rate of the oldest group over time. CONCLUSION: Tumor size, grade, race, and year of diagnosis all have significant constant effects on disease-specific survival in breast cancer, while the effects of age at diagnosis and disease stage have significant effects that vary over time.

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Simulation-based parameter estimation for complex models: a breast cancer natural history modelling illustration.

Simulation-based parameter estimation offers a powerful means of estimating parameters in complex stochastic models. We illustrate the application of these ideas in the setting of a natural history model for breast cancer. Our model assumes that the tumor growth process follows a geometric Brownian motion; parameters are estimated from the SEER registry. Our discussion focuses on the use of simulation for computing the maximum likelihood estimator for this class of models. The analysis shows that simulation provides a straightforward means of computing such estimators for models of substantial complexity.

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