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首页> 外文期刊>BMC research notes >Simple parametric survival analysis with anonymized register data: A cohort study with truncated and interval censored event and censoring times
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Simple parametric survival analysis with anonymized register data: A cohort study with truncated and interval censored event and censoring times

机译：具有匿名寄存器数据的简单参数生存分析：截短和间隔审查事件和审查时间的队列研究

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Background To preserve patient anonymity, health register data may be provided as binned data only. Here we consider as example, how to estimate mean survival time after a diagnosis of metastatic colorectal cancer from Norwegian register data on time to death or censoring binned into 30 day intervals. All events occurring in the first three months (90 days) after diagnosis were removed to achieve comparability with a clinical trial. The aim of the paper is to develop and implement a simple, and yet flexible method for analyzing such interval censored and truncated data. Methods Considering interval censoring a missing data problem, we implement a simple multiple imputation strategy that allows flexible sensitivity analyses with respect to the shape of the censoring distribution. To allow identification of appropriate parametric models, a χ 2-goodness-of-fit test--also imputation based--is derived and supplemented with diagnostic plots. Uncertainty estimates for mean survival times are obtained via a simulation strategy. The validity and statistical efficiency of the proposed method for varying interval lengths is investigated in a simulation study and compared with simpler alternatives. Results Mean survival times estimated from the register data ranged from 1.2 (SE = 0.09) to 3.2 (0.31) years depending on period of diagnosis and choice of parametric model. The shape of the censoring distribution within intervals did generally not influence results, whereas the choice of parametric model did, even when different models fit the data equally well. In simulation studies both simple midpoint imputation and multiple imputation yielded nearly unbiased analyses (relative biases of -0.6% to 9.4%) and confidence intervals with near-nominal coverage probabilities (93.4% to 95.7%) for censoring intervals shorter than six months. For 12 month censoring intervals, multiple imputation provided better protection against bias, and coverage probabilities closer to nominal values than simple midpoint imputation. Conclusion Binning of event and censoring times should be considered a viable strategy for anonymizing register data on survival times, as they may be readily analyzed with methods based on multiple imputation.
机译：背景技术为了保持患者的匿名性，可以将健康登记数据仅作为合并数据提供。在此我们以一个示例为例，如何根据挪威死亡时间或检查间隔30天的检查数据来估计转移性结直肠癌的诊断后的平均生存时间。诊断后头三个月（90天）内发生的所有事件均被删除，以实现与临床试验的可比性。本文的目的是开发和实现一种简单而灵活的方法来分析这种间隔删节和截断的数据。方法考虑间隔检查缺失数据的问题，我们实现了一种简单的多重插补策略，该策略允许针对检查分布的形状进行灵活的灵敏度分析。为了确定合适的参数模型，派生了χ 2 -拟合优度检验（也基于插补），并补充了诊断图。平均生存时间的不确定性估计是通过模拟策略获得的。在模拟研究中研究了所提出的方法对于不同间隔长度的有效性和统计效率，并与更简单的替代方法进行了比较。结果根据诊断数据和参数模型的选择，根据寄存器数据估算的平均生存时间为1.2（SE = 0.09）至3.2（0.31）年。间隔内的检查分布的形状通常不会影响结果，而参数模型的选择会影响结果，即使不同的模型对数据的拟合程度相同也是如此。在模拟研究中，简单的中点插补和多重插补都产生了几乎无偏的分析（相对偏差为-0.6％至9.4％）和置信区间，且检查间隔短于六个月，其置信区间接近名义覆盖率（93.4％至95.7％）。在12个月的检查间隔中，与简单的中点插补相比，多次插补可更好地防止偏倚，并且覆盖概率更接近标称值。结论事件和检查时间的分箱应被视为使生存时间匿名的寄存器数据匿名的可行策略，因为可以很容易地使用基于多重插补的方法对其进行分析。

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