对于从连续成像中获得的纵向肿瘤测量和总体生存之间的关系建模的方法有很多，许多方法需要不可检验和可争论的强假设。我们使用在III期随机临床试验中获得的图像来说明如何应用一种新型、更加灵活的方法，即部分条件（PC）生存模型。部分条件（PC）生存模型用于建模完成的VELOUR试验中，该试验评估在治疗转移性结直肠癌时添加阿法利贝（aflibercept）到胶注氟尿嘧啶（infusional fluorouracil）、亚叶酸钙（leucovorin）和伊立替康（irinotecan）。部分条件（PC）生存模型是一种半参数方法，用于估计纵向测量与时间-事件结果的关联性。总体生存是我们的结果。协变量包括基线肿瘤负担、从基线到每个随访时间的肿瘤负担变化以及治疗方案。研究了非分层和时间分层模型。在不假设肿瘤生长过程的分布的情况下，我们描述了肿瘤负担变化与生存之间的关系。这种变化与生存显著相关（风险比[HR]，1.04；95%置信区间[CI]，1.02至1.05；P < .001），表明阿法利贝作用至少部分通过改变肿瘤生长轨迹。我们还发现，即使考虑到随时间变化的肿瘤负担，基线肿瘤大小对生存的预测也是有效的（HR，1.02；95%CI，1.01至1.02；P < .001）。部分条件（PC）建模方法提供了一种灵活的方式，可以描述纵向协变量（例如被序列评估的肿瘤负担）与生存时间之间的关系。它可以应用于各种这类数据，并在临床试验正在进行时使用，以按照结直肠癌的示例积累新的疾病评估信息。

There are multiple approaches to modeling the relationship between longitudinal tumor measurements obtained from serial imaging and overall survival. Many require strong assumptions that are untestable and debatable. We illustrate how to apply a novel, more flexible approach, the partly conditional (PC) survival model, using images acquired during a phase III, randomized clinical trial in colorectal cancer as an example.PC survival approaches were used to model longitudinal volumetric computed tomography data of 1,025 patients in the completed VELOUR trial, which evaluated adding aflibercept to infusional fluorouracil, leucovorin, and irinotecan for treating metastatic colorectal cancer. PC survival modeling is a semiparametric approach to estimating associations of longitudinal measurements with time-to-event outcomes. Overall survival was our outcome. Covariates included baseline tumor burden, change in tumor burden from baseline to each follow-up time, and treatment. Both unstratified and time-stratified models were investigated.Without making assumptions about the distribution of the tumor growth process, we characterized associations between the change in tumor burden and survival. This change was significantly associated with survival (hazard ratio [HR], 1.04; 95% CI, 1.02 to 1.05; P < .001), suggesting that aflibercept works at least in part by altering the tumor growth trajectory. We also found baseline tumor size prognostic for survival even when accounting for the change in tumor burden over time (HR, 1.02; 95% CI, 1.01 to 1.02; P < .001).The PC modeling approach offers flexible characterization of associations between longitudinal covariates, such as serially assessed tumor burden, and survival time. It can be applied to a variety of data of this nature and used as clinical trials are ongoing to incorporate new disease assessment information as it is accumulated, as indicated by an example from colorectal cancer.