On the 22th of June, Tom Heuting defended his thesis entitled "Developing, validating, and evaluating clinical prediction models in breast and prostate cancer". Tom Hueting was a PhD student in the research group Health Technology & Services Research (HTSR). Supervisors are prof.dr. S. Siesling and prof.dr.ir. H. Koffijberg, co-supervisor is dr. M.C. van Maaren, all from the Faculty of Behavioural, Management and Social Sciences (BMS). We congratulate Tom on achieving his doctorate!
In his project, Tom developed, tested and validated clinical prediction models. Clinical prediction models are statistical tools that can be used to estimate the probability of a patient to either have a specific outcome or to develop an outcome in time. This probability is estimated based on patient or disease-specific input variables. It provides insights into the diagnosis (e.g. disease status) or prognosis (e.g. 5-year survival probability) of a patient, and can subsequently be used to support (shared) decision-making regarding the optimal management of the disease. Prediction models are developed and evaluated using data from patients that can be classified in similar patient groups (e.g. diagnosed with estrogen receptor positive breast cancer), but with varying disease characteristics (e.g. tumor stage, treatment received, nodal involvement etc.).
Before the available models are used to support in routine healthcare decision-making some challenges on the identification of currently existing models (accessibility), review of the quality of the models (transparency), assessment how well they perform on external validation (generalizability), and investigation of the potential benefit of recalibrating the validated models (updating) are identified. Subsequently, models showing adequate performance will be ready for implementation in clinical practice after clearly defined intended model use is described (interpretation), and the intended model use is substantiated by evidence regarding added value (impact assessment).
In his thesis, Tom describes multiple studies aimed at overcoming the challenges using examples on breast and prostate cancer. Since breast and prostate cancer are among the top three most commonly diagnosed cancers in women and men, respectively, there is a large amount of data available to establish clinical prediction models for patients diagnosed with breast or prostate cancer. Currently available models for breast and prostate cancer are required to be critically assessed to demonstrate which models are valuable and which information is still lacking when used in Dutch care.
