Journal Article

Comprehensive modelling of prostate cancer progression: integrating continuous and binary biomarkers with event time data and missing covariates

Kamal, Shahid, Liaqat, Madiha, Khan, Rehan Ahmad

2024

Overview

Longitudinal studies involve recording observations at scheduled visits or time points for individuals until a predetermined event, like reaching satisfactory tumor shrinkage in cancer studies. Furthermore, dropout in longitudinal studies leads to incomplete data, which significantly increases the risk of bias. An amended joint shared-random effects model (SREM) is proposed for mixed continuous and binary longitudinal measurements and a time-to-event (TTE) outcome, incorporating missing covariates. In the proposed model, a conditional model is applied for the mixed continuous and binary longitudinal outcomes; a mixed effect model is considered for the continuous longitudinal outcome. For the binary longitudinal outcome, given the continuous longitudinal outcome, a logistic mixed effect model is considered. These models share common random effects with the model for the event time outcome. The model formulation is based on Bayesian statistical thinking via Markov Chain Monte Carlo (MCMC). The proposed joint modelling is applied to contribute to the understanding of the progression of prostate cancer (PCa) by considering a generalized linear mixed effects model for time-varying covariates that incorporate ignorable missingness. The association between prostate-specific antigen (PSA) with alkaline phosphatase (ALP) and tumor status has been studied with mixed conclusions.Article HighlightsThe utilization of PSA and ALP biomarkers ensures precision in assessing PCa disease progression after treatment, empowering clinicians with comprehensive and accurate dynamic monitoring.Accounting for missing observations of Platelets and Bilirubin during intermittent follow-up is crucial in improving the accuracy of the analysis, ensuring the generation of valid conclusions regarding PCa insights.Utilizing statistical models that incorporate prior information to update current scenarios is paramount in extracting valuable insights from disease data. The application of Bayesian thinking is instrumental in making this process possible, offering clinicians a powerful tool for informed decision-making.

• Publisher: Springer Nature B.V
• Subject: Alkaline phosphatase / Bayesian analysis / Bilirubin / Biomarkers / Body mass index / Datasets / Decision making / Longitudinal studies / Markov chains / Mathematical models / Missing data / Prostate cancer / Prostate-specific antigen / Statistical analysis / Statistical models / Tumors / Variables