Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
AstraZeneca, Gothenburg, Sweden.
PLoS One. 2019 Feb 14;14(2):e0211918. doi: 10.1371/journal.pone.0211918. eCollection 2019.
Recent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history. An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register. After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC. Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55-69 years was predicted to reduce prostate cancer incidence by 14% and increase prostate cancer mortality by 2%. The model is open source and suitable for planning for effective prostate cancer screening into the future.
最近的前列腺癌筛查试验结果相互矛盾，目前尚不清楚如何在降低前列腺癌死亡率的同时最大限度地减少过度诊断和过度治疗。前列腺癌检测是一个部分可观察的过程，检测规划需要从随机对照试验中推断，或者更灵活地对癌症自然史进行建模。现有的美国前列腺癌自然史模型（Gulati 等人，生物统计学 2010 年；11：707-719）没有对 Gleason 6 和 7 癌症之间的生存率差异进行建模，并且预测当代瑞典的 Gleason 7 癌症太少。我们重新实现并重新校准了美国模型以适应瑞典。我们扩展了模型，以更精细地描述疾病状态、它们发展为可通过活检检测到的癌症的时间以及前列腺癌的生存情况。我们首先根据欧洲前列腺癌筛查研究（ERSPC）观察到的发病率比以及斯德哥尔摩 PSA（前列腺特异性抗原）和活检登记处观察到的特定年龄的癌症分期来校准模型；然后根据当前检测和治疗下不同疾病状态的年龄特异性生存率来校准模型。校准后，我们能够很好地匹配瑞典观察到的前列腺癌发病率趋势。假设通过筛查更早发现的患者生存获益相当，我们发现校准模型在 ERSPC 的模拟中复制了观察到的死亡率降低。使用由此产生的模型，我们预测了定期检测引入后的发病率和死亡率。与当前检测模式的模型相比，对 55-69 岁男性进行 8 年一次的定期检测，预计将使前列腺癌发病率降低 14%，并使前列腺癌死亡率增加 2%。该模型是开源的，适合未来有效的前列腺癌筛查规划。
