Kravdal Ø
Department of Economics, University of Oslo, PO Box 1095 Blindern, N-0317 Oslo, Norway.
J Epidemiol Community Health. 2002 Apr;56(4):309-18. doi: 10.1136/jech.56.4.309.
Sociodemographic differentials in cancer survival have occasionally been studied by using a relative survival approach, where all cause mortality among persons with a cancer diagnosis is compared with that among similar persons without such a diagnosis ("normal" mortality). One should ideally take into account that this "normal" mortality not only depends on age, sex, and period, but also various other sociodemographic variables. However, this has very rarely been done. A method that permits such variations to be considered is presented here, as an alternative to an existing technique, and is compared with a relative survival model where these variations are disregarded and two other methods that have often been used.
DESIGN, SETTING, AND PARTICIPANTS: The focus is on how education and marital status affect the survival from 12 common cancer types among men and women aged 40-80. Four different types of hazard models are estimated, and differences between effects are compared. The data are from registers and censuses and cover the entire Norwegian population for the years 1960-1991. There are more than 100 000 deaths to cancer patients in this material.
A model for registered cancer mortality among cancer patients gives results that for most, but not all, sites are very similar to those from a relative survival approach where educational or marital variations in "normal" mortality are taken into account. A relative survival approach without consideration of these sociodemographic variations in "normal" mortality gives more different results, the most extreme example being the doubling of the marital differentials in survival from prostate cancer. When neither sufficient data on cause of death nor on variations in "normal" mortality are available, one may well choose the simplest method, which is to model all cause mortality among cancer patients. There is little reason to bother with the estimation of a relative-survival model that does not allow sociodemographic variations in "normal" mortality beyond those related to age, sex, and period. Fortunately, both these less data demanding models perform well for the most aggressive cancers.
癌症生存方面的社会人口统计学差异偶尔会通过相对生存方法进行研究，即将癌症诊断患者的全因死亡率与无此类诊断的相似人群（“正常”死亡率）的全因死亡率进行比较。理想情况下，应该考虑到这种“正常”死亡率不仅取决于年龄、性别和时期，还取决于各种其他社会人口统计学变量。然而，这种情况很少发生。本文提出了一种允许考虑此类差异的方法，作为现有技术的替代方法，并将其与忽略这些差异的相对生存模型以及另外两种常用方法进行比较。
设计、地点和参与者：重点在于教育程度和婚姻状况如何影响40至80岁男性和女性中12种常见癌症类型的生存情况。估计了四种不同类型的风险模型，并比较了效应之间的差异。数据来自登记册和人口普查，涵盖了1960年至1991年的整个挪威人口。该材料中有超过10万名癌症患者死亡。
癌症患者登记癌症死亡率模型得出的结果，对于大多数（但并非所有）癌症部位来说，与考虑了“正常”死亡率中教育或婚姻差异的相对生存方法得出的结果非常相似。不考虑“正常”死亡率中的这些社会人口统计学差异的相对生存方法会得出更不同的结果，最极端的例子是前列腺癌生存中婚姻差异翻倍。当既没有足够的死亡原因数据，也没有“正常”死亡率差异的数据时，人们很可能会选择最简单的方法，即对癌症患者的全因死亡率进行建模。几乎没有理由费心去估计一个不允许“正常”死亡率中除年龄、性别和时期相关差异之外的社会人口统计学差异的相对生存模型。幸运的是，这两种对数据要求较低的模型对于侵袭性最强的癌症表现良好。
