Research Protocol for an Observational Health Data Analysis to Assess the Long-term Outcomes of Prostate Cancer Patients Undergoing Non-Interventional Management (i.e., Watchful Waiting) and the Impact of Comorbidities and Life Expectancy – PIONEER IMI’s “Big Data for Better Outcomes” program

Abstract

\n This is a study protocol for an observational health data analysis, submitted as a preprint to facilitate transparency and open science. Watchful waiting (WW) represents a deferred treatment option for prostate cancer (PCa) patients when curative treatment seems overtreatment right from the outset. Patients are ‘watched’ for the development of local or systemic progression with disease-related symptoms, at which stage they are then treated palliatively according to their symptoms, in order to maintain quality of life. When choosing WW, it is important to adequately assess life expectancy of patients. Although previous studies reported the outcomes of PCa patients managed with WW, which is the impact of individual patient characteristics and comorbidities on long-term outcomes is still largely unknown. The PIONEER, which is a novel project of the Innovative Medicine Initiative’s (IMI’s) “Big Data for Better Outcomes” program with the mission to transform PCa care with particular focus on improving cancer-related outcomes, health system efficiency and the quality of health and social care across Europe, aims at assessing which are the long-term outcomes of PCa patients undergoing WW overall and after stratification according to disease characteristics, comorbidities and life expectancy. Of note, this topic emerged as the second one with the highest agreement score among different stakeholders after an international consensus to identify and prioritize the most important questions in the field of PCa. This study aims to describe demographics, clinical characteristics and estimate outcomes of PCa patients under delayed treatment (WW) across a network of databases in the overall population and subgroups of patients identified by individual disease characteristics, demographics and comorbidities. The study will rely on large observational data, namely population-based registries, electronic health records and insurance claims data. The study will be an observational cohort study based on routinely collected health care data which has been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).