Bernatsky Sasha, Joseph Lawrence, Bélisle Patrick, Boivin Jean-François, Rajan Raghu, Moore Andrew, Clarke Ann
Division of Clinical Epidemiology, Department of Medicine, Montreal General Hospital, 1650 Cedar Avenue, Montreal, Que., Canada H3G 1A4.
Stat Med. 2005 Aug 15;24(15):2365-79. doi: 10.1002/sim.2116.
Tumour registry linkage, chart review and patient self-report are all commonly used ascertainment methods in cancer epidemiology. These methods are used for estimating the incidence or prevalence of different cancer types in a population, and for investigating the effects of possible risk factors for cancer. Tumour registry linkage is often treated as a gold standard, but in fact none of these methods is error free, and failure to adjust for imperfect ascertainment can lead to biased estimates. This is true both if the goal of the study is to estimate the properties of each ascertainment type, or if it is to estimate cancer incidence or prevalence from one or more of these methods. Although rarely applied in the literature to date, when cancer is ascertained by three or more methods, standard latent class models can be used to estimate cancer incidence or prevalence while adjusting for the estimated imperfect sensitivities and specificities of each ascertainment method. These models, however, do not account for variations in these properties across different cancer sites. To address this problem, we extend latent class methodology to include a hierarchical component, which accommodates different ascertainment properties across cancer sites. We apply our model to a data set of 169 lupus patients with three ascertainment methods and eight cancer types. This allows us to estimate the properties of each ascertainment method without assuming any to be a gold standard, and to calculate a standardized incidence ratio for cancer for lupus patients compared to the general population. As our data set is small, we also illustrate the effects as more data become available. We show that our model produces parameter estimates that are substantially different from the currently most popular method of ascertainment, which uses tumour registry data alone.
肿瘤登记链接、病历审查和患者自我报告都是癌症流行病学中常用的确定方法。这些方法用于估计人群中不同癌症类型的发病率或患病率，并用于调查癌症可能的风险因素的影响。肿瘤登记链接通常被视为金标准，但实际上这些方法都并非无差错，未能对不完美的确定情况进行调整可能会导致有偏差的估计。无论是研究目的是估计每种确定类型的属性，还是从这些方法中的一种或多种来估计癌症发病率或患病率，都是如此。尽管迄今为止在文献中很少应用，但当通过三种或更多种方法确定癌症时，标准潜在类别模型可用于估计癌症发病率或患病率，同时针对每种确定方法估计的不完美敏感性和特异性进行调整。然而，这些模型没有考虑这些属性在不同癌症部位之间的差异。为了解决这个问题，我们扩展了潜在类别方法，使其包含一个层次成分，以适应不同癌症部位的不同确定属性。我们将我们的模型应用于一个包含169名狼疮患者、三种确定方法和八种癌症类型的数据集。这使我们能够在不将任何一种方法假定为金标准的情况下估计每种确定方法的属性，并计算狼疮患者相对于一般人群的癌症标准化发病率比。由于我们的数据集较小，我们还说明了随着更多数据可用时的影响。我们表明，我们的模型产生的参数估计与目前最流行的仅使用肿瘤登记数据的确定方法有很大不同。
