Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, University Road, LE1 7RH, Leicester, UK.
Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, University Road, LE1 7RH, Leicester, UK.
Cancer Epidemiol. 2023 Oct;86:102408. doi: 10.1016/j.canep.2023.102408. Epub 2023 Aug 15.
In population-based cancer studies it is common to try to isolate the impact of cancer by estimating net survival. Net survival is defined as the probability of surviving cancer in the absence of any other-causes of death. Net survival can be estimated either in the cause-specific or relative survival framework. Cause-specific survival considers deaths from the cancer as the event of interest. Relative survival incorporates general population expected mortality rates to represent the other-cause mortality rate. Estimation approaches in both frameworks are impacted by the systematic removal of patients from the risk-set, commonly referred to as informative censoring in the cause-specific framework. In the relative survival framework, the Pohar Perme estimator combats the effect of this systematic removal of patients through weighting. When the two frameworks have been compared, informative censoring is rarely accounted for in the cause-specific framework.
We investigate the use of weighted cause-specific Kaplan-Meier estimates to overcome the impact of informative censoring and compared approaches to defining weights. Individuals remaining in the risk-set are upweighted using their predicted other-cause survival obtained through various model-based approaches. We also compare weights derived from expected mortality rates. We applied the approaches to US cancer registry data and conducted a simulation study.
Using weighted cause-specific estimates provides a better estimate of marginal net survival. The unweighted Kaplan-Meier estimates have a similar bias to the Ederer II method for relative survival. Weighted Kaplan-Meier estimates are unbiased and similar to the Pohar Perme estimator. There was little variation between the several weighting approaches.
In comparisons of cause-specific and relative survival, it is important to compare "like-with-like", therefore, a weighted approach should be considered for both frameworks. If researchers are interested in obtaining net measures in a cause-specific framework, then weighting is needed to account for informative censoring.
在基于人群的癌症研究中，通常尝试通过估计净生存来分离癌症的影响。净生存定义为在没有任何其他死因的情况下生存癌症的概率。净生存可以在特定原因或相对生存框架中进行估计。特定原因生存将癌症死亡视为感兴趣的事件。相对生存将一般人群的预期死亡率纳入代表其他死因死亡率。两种框架中的估计方法都受到从风险集系统删除患者的影响，在特定原因框架中通常称为信息性删失。在相对生存框架中，Pohar Perme 估计量通过加权来对抗这种系统删除患者的影响。当比较这两种框架时，在特定原因框架中很少考虑信息性删失。
我们研究了使用加权特定原因 Kaplan-Meier 估计来克服信息性删失的影响，并比较了定义权重的方法。通过各种基于模型的方法获得的预测其他原因生存，对留在风险集中的个体进行了加权。我们还比较了从预期死亡率中得出的权重。我们将这些方法应用于美国癌症登记数据并进行了模拟研究。
使用加权特定原因估计可以更好地估计边际净生存。未加权的 Kaplan-Meier 估计对相对生存的 Ederer II 方法具有相似的偏差。加权 Kaplan-Meier 估计是无偏的，与 Pohar Perme 估计器相似。几种加权方法之间几乎没有差异。
在比较特定原因和相对生存时，重要的是要进行“相似比较”，因此，应考虑对两种框架都采用加权方法。如果研究人员有兴趣在特定原因框架中获得净测量，则需要加权来考虑信息性删失。
