Prediction Models for Cancer Risk and Prognosis using Clinical and DNA Methylation Biomarkers: Considerations in Study Design and Model Development

The ability to accurately predict the prognosis for any given disease is of immense value for clinicians and patients. It can dictate and optimize an individual treatment plan for a patient and ultimately improve their quality of life and reduce the financial burden associated with unnecessary treatment. To allow the accurate prediction of disease prognosis, ongoing development of prediction models is of crucial importance. We introduce a novel curated, ad-hoc, feature selection (CAFS) strategy in the context of the Prostate Cancer DREAM Challenge. We demonstrate enhanced prediction performance of overall survival differences in patients with metastatic castration-resistant prostate cancer by applying CAFS and identify clinically important risk-predictors. With ongoing advancements in the omics field promising molecular biomarkers are being identified in order to facilitate disease prognosis beyond the capability of clinical information. The identification of such biomarkers depends on the examination of omic marks in adequately powered studies. With the goal to assist researchers in study design and planning of epigenome wide association studies of DNA methylation, we present a user-friendly tool, pwrEWAS, for comprehensive power estimation for epigenome-wide association studies. The R package for pwrEWAS is publicly available at GitHub (https://github.com/stefangraw/pwrEWAS) and the web interface is available at https://biostats-shinyr.kumc.edu/pwrEWAS/. The enormous volume of omic marks requires stringent evaluation to discover combinations of complementary marks that assemble predictive biomarkers. We therefore present a heuristic feature selection approach that allows one to handle such high-dimensional data. Selection Probability Optimization for Feature Selection (SPOFS) is designed to identify an optimal subset of omic features from among a vast pool of such features, which collectively improves prediction accuracy and form a biomarker. The integration of such biomarkers can then be utilized in the development and improvement of prediction models

Statistical methods for prognostic factor and risk prediction research

Prognosis research is an important part of medical research as it seeks to understand, predict, and improve future outcomes in people with a given disease or health condition. This thesis focuses on the application and development of statistical methods for prognosis research, with a particular focus on the identification of prognostic factors and the performance of risk prediction models. The first part of the thesis considers the use of a single study for prognostic factor and prediction model research. Prognostic factors of adverse outcome in monochorionic diamniotic twin pregnancies are investigated and difference in nuchal translucency and crown-rump length were found to have prognostic value. The instability of developing a prediction model in small sample sizes is also illustrated. Then, a review of published prediction models is conducted which reveals potential concerns that measurement error may affect the predictors included in many models, and a lack of clarity about the timing of predictor measurements and the intended moment of using the proposed models. Recommendations for improved reporting are provided. A real example is then used to illustrate how displacing the collection of a time-varying predictor from the intended moment of model use leads to substantial differences in the predictor-outcome association, and the subsequent performance of the prediction model. The second part of the thesis focuses on the synthesis of IPD from multiple studies. An IPD meta-analysis is used to validate existing stillbirth prediction models and demonstrates that the models should not be recommended for clinical practice due to poor predictive performance and insufficient clinical utility. Finally, a novel analytic method is developed to calculate the power of an IPD meta-analysis to examine prognostic factor effects with binary outcomes, based on published study aggregate data, to help researchers decide on the benefit of the IPD approach in advance of collecting IPD