Lee to Develop Statistical Tools for Predicting Breast Cancer Survival
Development of the statistical tools will be funded by a two-year, $154,791 NIH grant awarded to Chi Hyun Lee.
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Assistant Professor of Biostatistics Chi Hyun Lee has received a two-year, $154,791 grant from the National Institutes of Health (NIH) to develop statistical tools that will better predict breast cancer survival rates and survival time after breast cancer recurrence.
Breast cancer is a complex disease whose progression can be hard to predict. While many factors can contribute to a breast cancer prognosis, the roles these factors play in the disease’s clinical development remain unclear. However, one risk factor – a biomarker called an androgen receptor (AR) – has recently emerged for its potential as a predictive factor in the disease’s progression. AR plays a role at the cellular level in regulating hormones, including in female sexual, somatic, and behavioral functions. In excess, however, it has been linked to an increased risk of breast cancer.
For the project, Lee will work with data from the Nurses’ Health Study (NHS), one of the world’s large prospective cohort studies conducted to investigate the risk factors for major chronic diseases in women including breast cancer. The data from the NHS contain invaluable information for breast cancer research such as lifestyle, hormonal, and genetic risk factors, including AR, as well as clinical outcomes such as breast cancer diagnosis, recurrence, and death.
“In many epidemiologic studies on breast cancer survival,” explains Lee, “researchers rely on the hazard ratio, or the likelihood of a harmful event such as death or disease progression compared to a control group. This ratio is determined by using a statistical method called the proportional hazard model. However, we have found in the NHS data that the assumption of the model on the association between AR expression and breast cancer survival to be faulty. This means that the results of the hazard ratio are often misleading when it comes to assessing AR’s prognostic values.”
Lee notes another statistical method based on the restricted mean survival time (RMST) has much better prognostic value. RMST is a summary metrics defined as the life expectancy up to a specific time point, eliminating assumptions of proportional hazards which may prove to be faulty. The RMST has many advantageous features such as its straightforward interpretation and robustness.
“Specifically, we can assess the prognostic factor’s effects in terms of absolute effect, which is clinically more interpretable,” notes Lee.
Lee’s funding will allow her to develop novel statistical methods based on RMST to fully utilize the rich data from the NHS. As a result, she expects to gain a better understanding of the complex effect of AR on breast cancer progression and survival. Ultimately, the funding will support two goals: to develop a flexible regression method based on RMST that will be used to elucidate the clinical significance of AR in survival by different subtypes of breast cancers; and to develop a model-free approach to compare survival rates after breast cancer recurrence between groups with different AR status.
“Our goal is that these novel statistical approaches will help us determine the prognostic values of AR and potentially lead to better targeted therapies for patients and advances in breast cancer survival,” explains Lee.
She notes that while the proposed research focuses on breast cancer research, the proposed statistical methods will have a broad applicability for other chronic diseases.
