Layman’s abstract for paper on continuous threshold models with two‐way interactions in survival analysis

Each week, we will be publishing layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is from the Canadian Journal of Statistics, with the full article now available to read in Early View here.

Shuo Liu, S. and Chen, B.E. (2020), Continuous threshold models with two‐way interactions in survival analysis. Can J Statistics. doi:10.1002/cjs.11561

In biomedical research, a cut-point is often applied to certain biomarker measurement to identify a sub-group of patients who may have more benefit from a new treatment than the rest of patients. For example, it is observed that serum prostatic acid phosphatase (AP) activity is significantly higher in prostate cancer patients. Patients with AP biomarker values above a certain cut-off point might be more responsive to an anti-cancer treatment to reduce the prostate cancer mortality. On the other hand patients with AP biomarker values below such a cut-off point may have less benefit (or no benefit at all) from the same treatment. By studying how the treatment interacts with a biomarker variable will lead us to personalized treatment decisions based on the patient’s unique clinical and biological characteristics. This paper considers modelling how the biomarker variable modifies the treatment effect on patient’s survival time using a piece-wise linear function that changes direction at certain biomarker cut-point. The authors propose new statistical methods to estimate the treatment effects at different values of biomarker variables and further study the theoretical results of the estimators. They use the proposed model to analyze a prostate cancer clinical trial with serum prostatic acid phosphatase biomarker. This work might be of interest to researchers who work on threshold models in statistics, physicians who design the treatment schedule, patients who seek for personalized medication.