Santé Publique France, French National Public Health Agency, F-94415 Saint-Maurice, France.
Hospices Civils de Lyon, Service de Biostatistique, F-69495, Pierre-Bénite, France and Laboratoire de Biométrie et Biologie Évolutive, Equipe Biotatistique-Santé, CNRS UMR5558, F-69100, Villeurbanne, France.
Biostatistics. 2019 Jul 1;20(3):452-467. doi: 10.1093/biostatistics/kxy012.
There is a growing interest in using health care (HC) data to produce epidemiological surveillance indicators such as incidence. Typically, in the field of cancer, incidence is provided by local cancer registries which, in many countries, do not cover the whole territory; using proxy measures from available nationwide HC databases would appear to be a suitable approach to fill this gap. However, in most cases, direct counts from these databases do not provide reliable measures of incidence. To obtain accurate incidence estimations and prediction intervals, these databases need to be calibrated using a registry-based gold standard measure of incidence. This article presents a calibration model for count data developed to predict cancer incidence from HC data in geographical areas without cancer registries. First, the ratio between the proxy measure and incidence is modeled in areas with registries using a Poisson mixed model that allows for heterogeneity between areas (calibration stage). This ratio is then inverted to predict incidence from the proxy measure in areas without registries. Prediction error admits closed-form expression which accounts for heterogeneity in the ratio between areas. A simulation study shows the accuracy of our method in terms of prediction and coverage probability. The method is further applied to predict the incidence of two cancers in France using hospital data as the proxy measure. We hope this approach will encourage sound use of the usually imperfect information extracted from HC data.
人们越来越感兴趣地利用医疗保健 (HC) 数据生成流行病学监测指标，如发病率。通常，在癌症领域，发病率是由当地癌症登记处提供的，而在许多国家，这些登记处并未覆盖整个领土；使用全国范围内可用的 HC 数据库中的代理措施似乎是填补这一空白的合适方法。然而，在大多数情况下，这些数据库中的直接计数并不能提供可靠的发病率衡量标准。为了获得准确的发病率估计和预测区间，需要使用基于登记的发病率金标准衡量标准对这些数据库进行校准。本文提出了一种用于计数数据的校准模型，旨在为没有癌症登记处的地理区域从 HC 数据中预测癌症发病率。首先，使用泊松混合模型在有登记处的地区对代理措施与发病率之间的比率进行建模，该模型允许地区之间存在异质性（校准阶段）。然后，通过反转该比率，根据无登记处的代理措施预测发病率。预测误差允许使用封闭形式的表达式来解释地区之间比率的异质性。一项模拟研究表明了我们的方法在预测和覆盖率概率方面的准确性。该方法进一步应用于使用医院数据作为代理措施预测法国两种癌症的发病率。我们希望这种方法将鼓励合理利用从 HC 数据中提取的通常不完美的信息。
