Analysis of prevalent cohort survival data

流行队列生存数据分析

• 批准号: 217398-2008
• 负责人: Asgharian, Masoud
• 金额: $ 1.09万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=497326
• 关键词: Analysis; prevalent; cohort; survival; data

Cross-sectional sampling is a data collection scheme often used in follow-up studies on duration of a condition when logistic or other constraints preclude the possibility of following-up subjects since initiation of the condition. The condition of interest may be, for instance, a disease, in medical applications, or the unemployment spell in labour force studies. Subjects recruited through a cross-sectional survey have already experienced the onset of the condition of interest, say a disease. Such subjects are known as prevalent cases in epidemiological studies. It is well-known that the duration data collected on prevalent cases are biased. In fact, subjects with longer duration have a greater chance to be included in the sample. A further complication encountered in a cross-sectional survery with follow-up is the issue of informative censoring. This latter feature of cross-sectional surverys, render any direct application of existing methodologies in the classical survival analysis, i.e. analysis based on incident cases, impossible. For, the independence between failure and censoring time plays a crucial role in the martingale machinary used in the classical setting. In addition to the aforementioned features, the data presented to us for analysis have quite a number of other peculiarities which are seemingly shared by many other prevalent cohort studies. Ascertainment of the disease onset is, for example, a great challenge in many prevalent cohort studies. The onset of disease may be completely unknown or, at best, known with uncertainty for many subjects. Modelling and analysis of covariates is another complicated issue in prevalent cohort studies. While a modification of Cox partial likelihood has been used quite often in application, it is possible to show that the estimators obtained using this model, while consistent, are not most efficient.

横断面抽样是一种数据收集方案，常用于对疾病持续时间的随访研究，当逻辑或其他限制因素排除了自疾病开始以来对受试者进行随访的可能性时。感兴趣的条件可以是，例如，在医疗应用中的疾病，或在劳动力研究中的失业期。通过横断面调查招募的受试者已经经历了感兴趣的病症的发作，比如一种疾病。此类受试者在流行病学研究中被称为普遍病例。众所周知，流行病例的持续时间数据存在偏差。事实上，持续时间较长的受试者被纳入样本的机会更大。一个进一步的并发症遇到的横断面调查与后续的问题是信息删失。横截面调查的后一个特征，使得现有方法在经典生存分析中的任何直接应用，即基于事件病例的分析，是不可能的。因为，在经典的鞅机制中，失效时间和截尾时间之间的独立性起着至关重要的作用。除了上述特征外，提交给我们进行分析的数据还有许多其他特点，这些特点似乎与许多其他流行的队列研究相同。例如，在许多流行的队列研究中，确定疾病发作是一个巨大的挑战。对于许多受试者，疾病的发作可能完全未知，或者充其量是已知的不确定性。 协变量的建模和分析是流行队列研究中的另一个复杂问题。虽然修改后的考克斯偏似然已在应用中经常使用，它是可能的，使用该模型获得的估计，而一致的，是不是最有效的。