Flinders Health and Medical Research Institute (FHMRI), Flinders University, Adelaide, South Australia, Australia.
South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia.
Med Decis Making. 2021 Feb;41(2):179-193. doi: 10.1177/0272989X20978958. Epub 2020 Dec 22.
It is often important to extrapolate survival estimates beyond the limited follow-up times of clinical trials. Extrapolated survival estimates can be highly sensitive to model choice; thus, appropriate model selection is crucial. Flexible parametric spline models have been suggested as an alternative to standard parametric models; however, their ability to extrapolate is not well understood.
To determine how well standard parametric and flexible parametric spline models predict survival when fitted to registry cohorts with artificially right-censored follow-up times.
Adults with advanced breast, colorectal, small cell lung, non-small cell lung, or pancreatic cancer with a potential follow-up time of 10 y were selected from the SEER 1973-2015 registry data set. Patients were classified into 15 cohorts by cancer and age group at diagnosis (18-59, 60-69, 70+ y). Follow-up times for each cohort were right censored at 20%, 35%, and 50% survival. Standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, generalized gamma) and spline models (proportional hazards, proportional odds, normal/probit) were fitted to the 10-y data set and the 3 right-censored data sets. Predicted 10-y restricted mean survival time and percentage surviving at 10 y were compared with the observed values.
Across all data sets, the spline odds and spline normal models most frequently gave accurate predictions of 10-y survival outcomes. Visually, spline models tended to demonstrate better fit to the observed hazard functions than standard parametric models, both in the censored and 10-y data.
In these cohorts, where there was little uncertainty in the observed data, the spline models performed well when extrapolating beyond the observed data. Spline models should be routinely included in the set of models that are fitted when extrapolating cancer survival data.
在临床试验的有限随访时间之外推断生存估计通常很重要。推断的生存估计对模型选择非常敏感；因此，适当的模型选择至关重要。灵活的参数样条模型已被提议作为标准参数模型的替代方法；然而，它们的外推能力还不太清楚。
确定标准参数和灵活参数样条模型在拟合具有人为右删失随访时间的登记队列时预测生存的能力。
从 SEER 1973-2015 登记数据集中选择有潜在 10 年随访时间的晚期乳腺癌、结直肠癌、小细胞肺癌、非小细胞肺癌或胰腺癌成人患者。患者按癌症和诊断时年龄组（18-59、60-69、70+ 岁）分为 15 个队列。每个队列的随访时间在 20%、35%和 50%生存时被右删失。标准参数模型（指数、Weibull、Gompertz、对数-逻辑、对数正态、广义伽马）和样条模型（比例风险、比例优势、正态/概率）被拟合到 10 年数据集和 3 个右删失数据集。比较预测的 10 年限制性平均生存时间和 10 年时的生存百分比与观察值。
在所有数据集上，样条优势和样条正态模型最频繁地准确预测 10 年生存结果。从视觉上看，样条模型在有删失和 10 年数据中，与标准参数模型相比，往往更能拟合观察到的危险函数。
在这些队列中，观察数据中几乎没有不确定性，样条模型在观察数据之外进行外推时表现良好。在推断癌症生存数据时，应该常规包括样条模型作为拟合模型的一部分。
