Brookmeyer R, Yasui Y
Department of Biostatistics, Johns Hopkins University, School of Hygiene and Public Health, Baltimore, Maryland 21205, USA.
Biometrics. 1995 Sep;51(3):831-42.
Passive surveillance disease data involve a registry of individuals who are at risk of disease and who are not under active follow up. The most serious limitations with such data are incomplete ascertainment of cases of disease and little or no follow-up information on patient vital status. This paper considers whether it is possible to estimate disease risk from such data and, if not, what additional information is required. In general, relative risks based on passive surveillance data will be biased even under the assumption that the probability of disease reporting and the hazard of death from other causes are the same for all individuals in the registry. However if the disease is rare, this bias is negligible. Methods are developed for estimating absolute disease incidence rates by combining passive surveillance data with a cohort study. Analytic approaches are proposed for the situations when death rates from all other causes are known and also unknown, and it is found that there is little loss in efficiency even if death rates are not known. There are considerable gains in efficiency for estimating absolute disease incidence rates by supplementing a cohort study with passive surveillance registry data compared to using the cohort study alone, especially if the exposure is rare and the cohort study is small relative to the size of the registry. Intuitively, the cohort data provides information about absolute rates of disease, while the passive surveillance data provides information about relative risks. The methods are applied to a registry of patients with an artificial heart valve that is at risk of breaking.
被动监测疾病数据涉及对有疾病风险且未接受主动随访的个体进行登记。此类数据最严重的局限性在于疾病病例的确定不完整，以及关于患者生命状态的随访信息很少或没有。本文探讨了是否有可能从此类数据中估计疾病风险，如果不能，还需要哪些额外信息。一般来说，即使假设登记册中所有个体的疾病报告概率和其他原因导致的死亡风险相同，基于被动监测数据的相对风险也会存在偏差。然而，如果疾病罕见，这种偏差可以忽略不计。本文开发了通过将被动监测数据与队列研究相结合来估计绝对疾病发病率的方法。针对所有其他原因导致的死亡率已知和未知的情况，提出了分析方法，结果发现即使死亡率未知，效率损失也很小。与仅使用队列研究相比，通过用被动监测登记数据补充队列研究来估计绝对疾病发病率，效率有显著提高，特别是当暴露罕见且队列研究相对于登记册规模较小时。直观地说，队列数据提供了疾病绝对发病率的信息，而被动监测数据提供了相对风险的信息。这些方法应用于一个有心脏瓣膜破裂风险的患者登记册。
