The Prospective Dutch Colorectal Cancer (PLCRC) cohort: real-world data facilitating research and clinical care

Real-world data (RWD) sources are important to advance clinical oncology research and evaluate treatments in daily practice. Since 2013, the Prospective Dutch Colorectal Cancer (PLCRC) cohort, linked to the Netherlands Cancer Registry, serves as an infrastructure for scientific research collecting additional patient-reported outcomes (PRO) and biospecimens. Here we report on cohort developments and investigate to what extent PLCRC reflects the “real-world”. Clinical and demographic characteristics of PLCRC participants were compared with the general Dutch CRC population (n = 74,692, Dutch-ref). To study representativeness, standardized differences between PLCRC and Dutch-ref were calculated, and logistic regression models were evaluated on their ability to distinguish cohort participants from the Dutch-ref (AU-ROC 0.5 = preferred, implying participation independent of patient characteristics). Stratified analyses by stage and time-period (2013–2016 and 2017–Aug 2019) were performed to study the evolution towards RWD. In August 2019, 5744 patients were enrolled. Enrollment increased steeply, from 129 participants (1 hospital) in 2013 to 2136 (50 of 75 Dutch hospitals) in 2018. Low AU-ROC (0.65, 95% CI: 0.64–0.65) indicates limited ability to distinguish cohort participants from the Dutch-ref. Characteristics that remained imbalanced in the period 2017–Aug’19 compared with the Dutch-ref were age (65.0 years in PLCRC, 69.3 in the Dutch-ref) and tumor stage (40% stage-III in PLCRC, 30% in the Dutch-ref). PLCRC approaches to represent the Dutch CRC population and will ultimately meet the current demand for high-quality RWD. Efforts are ongoing to improve multidisciplinary recruitment which will further enhance PLCRC’s representativeness and its contribution to a learning healthcare system

A multi-objective genetic algorithm for software development team staffing based on personality types

This paper proposes a multi-objective genetic algorithm for software project team staffing that focuses on optimizing human resource usage based on technical skills and personality traits of software developers. Human factors are recognized as critical aspects affecting the rate of success of software projects, as well as other properties, such as productivity, software quality, performance, and job satisfaction. However, managers often rely solely on technical criteria to staff their projects, which risks overlooking these important aspects of software development, such as the abilities and work styles of developers. The behaviour and scalability of the algorithm was validated against a series of hypothetical projects of varying size and complexity, and also through a real-world project of an SME in the local IT industry. The approach demonstrated a sufficient ability to generate both feasible and optimal staffing solutions by assigning developers most technically competent and suited personality-wise for each project task