Chan Kwun Chuen Gary, Chen Ying Qing, Di Chong-Zhi
Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A. ,
Biometrika. 2012 Dec;99(4):995-1000. doi: 10.1093/biomet/ass049. Epub 2012 Sep 30.
To study disease association with risk factors in epidemiologic studies, cross-sectional sampling is often more focused and less costly for recruiting study subjects who have already experienced initiating events. For time-to-event outcome, however, such a sampling strategy may be length biased. Coupled with censoring, analysis of length-biased data can be quite challenging, due to induced informative censoring in which the survival time and censoring time are correlated through a common backward recurrence time. We propose to use the proportional mean residual life model of Oakes & Dasu (, 409-10, 1990) for analysis of censored length-biased survival data. Several nonstandard data structures, including censoring of onset time and cross-sectional data without follow-up, can also be handled by the proposed methodology.
在流行病学研究中,为了研究疾病与风险因素的关联,对于已经经历起始事件的研究对象,横断面抽样在招募时通常更具针对性且成本更低。然而,对于事件发生时间结局,这种抽样策略可能存在长度偏倚。再加上删失,由于诱导信息删失(即生存时间和删失时间通过共同的反向复发时间相关),对长度偏倚数据的分析可能颇具挑战性。我们建议使用奥克斯和达苏(1990年,第409 - 410页)的比例平均剩余寿命模型来分析删失的长度偏倚生存数据。所提出方法还能处理几种非标准数据结构,包括发病时间的删失以及无随访的横断面数据。