Mayeda Elizabeth Rose, Glymour M Maria
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.
Cancer Epidemiol Biomarkers Prev. 2017 Jan;26(1):17-20. doi: 10.1158/1055-9965.EPI-16-0559.
The effects of overweight or obesity on survival after cancer diagnosis are difficult to discern based on observational data because these associations reflect the net impact of both causal and spurious phenomena. We describe two sources of bias that might lead to underestimation of the effect of increased body weight on survival after cancer diagnosis: collider stratification bias and heterogeneity in disease bias. Given the mixed evidence on weight status, weight change, and postdiagnosis survival for cancer patients, systematic evaluation of alternative explanations is critical. The plausible magnitudes of these sources of bias can be quantified on the basis of expert knowledge about particular cancer types using simulation tools. We illustrate each type of bias, describe the assumptions researchers need make to evaluate the plausible magnitude of the bias, and provide a simple example of each bias using the setting of renal cancer. Findings from simulations, tailored to specific types of cancer, could help distinguish real from spurious effects of body weight on patient survival. Using these results can improve guidance for patients and providers about the relative importance of weight management after a diagnosis. Cancer Epidemiol Biomarkers Prev; 26(1); 17-20. ©2017 AACR.
基于观察性数据,很难辨别超重或肥胖对癌症诊断后生存率的影响,因为这些关联反映了因果现象和虚假现象的综合影响。我们描述了两种可能导致低估体重增加对癌症诊断后生存率影响的偏差来源:对撞分层偏差和疾病偏差中的异质性。鉴于癌症患者体重状况、体重变化和诊断后生存率方面的证据不一,对其他解释进行系统评估至关重要。可以使用模拟工具,根据关于特定癌症类型的专业知识,对这些偏差来源的可能程度进行量化。我们阐述每种偏差类型,描述研究人员评估偏差可能程度时需要做出的假设,并以肾癌为例提供每种偏差的简单示例。针对特定癌症类型的模拟结果,有助于区分体重对患者生存率的真实影响和虚假影响。利用这些结果可以改善对患者和医疗服务提供者关于诊断后体重管理相对重要性的指导。《癌症流行病学、生物标志物与预防》;26(1);17 - 20。©2017美国癌症研究协会。