Patients who were previously diagnosed with prostate cancer usually undergo a routine clinical monitoring that involves measuring the Prostate-specific antigen (PSA). The trajectory of this biomarker over time serves as an indication of cancer recurrence. If the PSA value begins to increase, the cancer is said to be more likely to recur and thus, the patient is advised to start a treatment. There are two reasons for stopping the patient follow-up and this poses a certain challenge. One of them is starting a salvage hormone therapy and another is actual recurrence of cancer. When analyzing such data, we need to account for informative dropout, otherwise, neglecting it may lead to increased bias in estimation of the PSA trajectory. Thus, hormone therapy serves as a censoring event, which is a defining feature of survival analysis. Motivated by the PSA data, we need to efficiently describe the dropout mechanism using the joint model. The survival submodel is based on the Weibull distribution and we use the Bayesian inference to fit this model, more specifically, we use the R-INLA package, which is a much faster alternative to MCMC-based inference. The fact that our joint model with a linear bivariate Gaussian association structure is a latent Gaussian model (LGM) allows us to use this inferential tool. Based on this work, we are then able to develop dynamic predictions of prostate cancer recurrence. Making accurate prognosis for cancer data is clinically impactful and could ultimately contribute to the development of precision medicine.
|Date made available
|KAUST Research Repository