Simulation model of disease incidence driven by diagnostic activity

Abstract

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.

Variation in Prostate-Specific Antigen Testing Rates and Prostate Cancer Treatments and Outcomes in a National 20-Year Cohort

“…For each scenario, we ran the entire simulation 100 times (as described by Westerberg et al 7 ) and calculated the number of men diagnosed, year of diagnosis, risk category, person-years in each treatment (from start of treatment to end of follow-up) and prostate cancer-specific mortality.…”

“…The simulation model, Proxy-Based Risk-Stratified Incidence Simulation Model–Prostate Cancer (PRISM-PC), which has been previously described 7 and is summarized in the eAppendix in the Supplement , is based on 25 years of data from PCBaSe. In short, the model works by first simulating incidence and mortality for a population of men aged 40 to 100 years during a specified time period in steps of 1 year.…”

“…For each scenario, we ran the entire simulation 100 times (as described by Westerberg et al 7 ) and calculated the number of men diagnosed, year of diagnosis, risk category, person-years in each treatment (from start of treatment to end of follow-up) and prostate cancer–specific mortality. Incidence, treatment prevalence, and mortality are age-standardized (age distribution of men was 40 to 100 years in 2016), calculated yearly per 100 000 men at risk, and presented as the mean per year over the entire study period, 1996 to 2016.…”

“…Confidence intervals were constructed accounting for parameter uncertainty and simulation variability. 7 The incidence rate ratio (IRR) of prostate cancer mortality was computed comparing the incidence rate in the high–diagnostic activity scenario with each of the extreme low–diagnostic activity scenarios. Statistical analysis was performed using R software version 4.0.2 (R Project for Statistical Computing) from February 2020 to January 2021.…”

“…[2][3][4][5][6] The aim of this study was to quantify the outcomes associated with increasing diagnostic activity during the past couple decades since PSA testing began, comparing it with a hypothetical scenario with more restrictive diagnostic activity to see whether the increased diagnostic activity was associated with more treatment and decreased mortality by use of a new model that simulates incidence, treatment trajectories, and mortality when given a specific scenario of diagnostic activity. 7 androgen deprivation therapy. Data on treatment trajectories were obtained in PCBaSe 8 for each man diagnosed with prostate cancer between 1996 and 2016 (eAppendix in Supplement).…”