Simulation model of disease incidence driven by diagnostic activity
| Autor: | Marcus Westerberg, Hans Garmo, Pär Stattin, Lars Holmberg, Rolf Larsson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Statistics and Probability Oncology medicine.medical_specialty real-life screening Epidemiology Population real‐life Disease Lower risk 01 natural sciences 010104 statistics & probability 03 medical and health sciences Prostate cancer 0302 clinical medicine population‐based Internal medicine medicine Humans Sannolikhetsteori och statistik 030212 general & internal medicine 0101 mathematics Medical diagnosis Overdiagnosis education Probability Theory and Statistics Research Articles Early Detection of Cancer Sweden education.field_of_study simulatiion model simulation model business.industry screening Incidence Incidence (epidemiology) Prostate Prostatic Neoplasms Prostate-Specific Antigen medicine.disease prostate cancer population-based Clinical trial incidence business Research Article |
| Zdroj: | Statistics in Medicine |
| Popis: | It is imperative to understand the effects of early detection and treatment of chronic diseases, such as prostate cancer, regarding incidence, overtreatment and mortality. Previous simulation models have emulated clinical trials, and relied on extensive assumptions on the natural history of the disease. In addition, model parameters were typically calibrated to a variety of data sources. We propose a model designed to emulate real-life scenarios of chronic disease using a proxy for the diagnostic activity without explicitly modeling the natural history of the disease and properties of clinical tests. Our model was applied to Swedish nation-wide population-based prostate cancer data, and demonstrated good performance in terms of reconstructing observed incidence and mortality. The model was used to predict the number of prostate cancer diagnoses with a high or limited diagnostic activity between 2017 and 2060. In the long term, high diagnostic activity resulted in a substantial increase in the number of men diagnosed with lower risk disease, fewer men with metastatic disease, and decreased prostate cancer mortality. The model can be used for prediction of outcome, to guide decision-making, and to evaluate diagnostic activity in real-life settings with respect to overdiagnosis and prostate cancer mortality. |
| Databáze: | OpenAIRE |
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