Azzato E M, Greenberg D, Shah M, Blows F, Driver K E, Caporaso N E, Pharoah P D P
Department of Oncology, Strangeways Research Laboratory, University of Cambridge, Worts Causeway, Cambridge CB1 8RN, UK.
Br J Cancer. 2009 Jun 2;100(11):1806-11. doi: 10.1038/sj.bjc.6605062. Epub 2009 Apr 28.
Observational epidemiological studies often include prevalent cases recruited at various times past diagnosis. This left truncation can be dealt with in non-parametric (Kaplan-Meier) and semi-parametric (Cox) time-to-event analyses, theoretically generating an unbiased hazard ratio (HR) when the proportional hazards (PH) assumption holds. However, concern remains that inclusion of prevalent cases in survival analysis results inevitably in HR bias. We used data on three well-established breast cancer prognosticators - clinical stage, histopathological grade and oestrogen receptor (ER) status - from the SEARCH study, a population-based study including 4470 invasive breast cancer cases (incident and prevalent), to evaluate empirically the effectiveness of allowing for left truncation in limiting HR bias. We found that HRs of prognostic factors changed over time and used extended Cox models incorporating time-dependent covariates. When comparing Cox models restricted to subjects ascertained within six months of diagnosis (incident cases) to models based on the full data set allowing for left truncation, we found no difference in parameter estimates (P=0.90, 0.32 and 0.95, for stage, grade and ER status respectively). Our results show that use of prevalent cases in an observational epidemiological study of breast cancer does not bias the HR in a left truncation Cox survival analysis, provided the PH assumption holds true.
观察性流行病学研究通常纳入在过去不同时间确诊的现患病例。这种左截断问题可在非参数（Kaplan-Meier）和半参数（Cox）生存分析中处理，理论上在比例风险（PH）假设成立时可产生无偏风险比（HR）。然而，仍有人担心在生存分析中纳入现患病例不可避免地会导致HR偏差。我们使用了基于人群的SEARCH研究中的数据，该研究纳入了4470例浸润性乳腺癌病例（新发病例和现患病例），涉及三个已确立的乳腺癌预后因素——临床分期、组织病理学分级和雌激素受体（ER）状态，以实证评估考虑左截断在限制HR偏差方面的有效性。我们发现预后因素的HR随时间变化，并使用了纳入时间依存协变量的扩展Cox模型。当将限于诊断后六个月内确诊的受试者（新发病例）的Cox模型与基于允许左截断的完整数据集的模型进行比较时，我们发现参数估计值没有差异（分期、分级和ER状态的P值分别为0.90、0.32和0.95）。我们的结果表明，在乳腺癌观察性流行病学研究中使用现患病例，在左截断Cox生存分析中不会使HR产生偏差，前提是PH假设成立。
