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A Comparison of Different Analysis Methods for Reconstructed Survival Data to Inform Cost-Effectiveness Analysis

机译：重建生存数据的不同分析方法对成本效益分析的不同分析方法

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Objectives The aim of this study was to use Microsoft Excel spreadsheet software to fit parametric survival distributions. We also explain the differences between individual patient data (IPD) and survival data reconstructed in Excel and SAS. Methods Three sets of patient data on overall survival were compared using different methods: 'original' IPD, 'reconstructed SAS', and 'reconstructed Excel'. The best-fit distribution was selected using visual observation, supported by linear plots of predicted probabilities, goodness-of-fit coefficients, and the sum of squared error of prediction. Outcomes included the incremental cost-effectiveness ratio (ICER), incremental net benefit (INB), incremental cost, and life-years gained over short-term and lifetime horizons. These were compared for different data sets. Results In this example, log-normal, log-logistic, and Weibull distributions showed best-fit with the visual tests and goodness-of-fit statistics. Weibull and exponential distributions showed significant differences compared with IPD data. Data on short-term (5 years) time horizons produced by different data re-creation methods showed closeness with data reconstructed from SAS. The ICER and INB results were dependent on the time horizon and selected parametric distribution from the model. Conclusions Different approaches used in fitting parametric survival distributions yielded predicted probabilities that substantially differed from those using original IPD. Our study highlights the importance of following guidelines for economic evaluations with a systematic approach to parametric survival analysis techniques in order to select best fitting parametric survival distributions.
机译：目的这项研究的目的是使用Microsoft Excel电子表格软件来适应参数的生存分布。我们还解释了在Excel和SAS中重建的个体患者数据（IPD）和生存数据之间的差异。方法使用不同方法比较三套关于总体生存的患者数据：'原始'IPD，'重建SAS'和“重建Excel”。使用视觉观察选择最合适的分布，由预测概率，拟合良好系数的线性曲线，以及预测的平方误差之和。结果包括增量成本效益率（ICER），增量净福利（INB），增量成本和在短期和终身视野上获得的生命年份。将这些与不同的数据集进行比较。结果在此示例中，日志正常，日志逻辑和Weibull分布显示最适合视觉测试和拟合良好统计数据。与IPD数据相比，威布尔和指数分布显示出显着差异。关于短期（5年）的数据，由不同数据重新创建方法产生的时间视野显示与从SA重建的数据进行亲密关系。 ICER和INB结果取决于时间范围和来自模型的选定参数分布。结论拟合参数生存分布中使用的不同方法产生了预测的概率，与使用原始IPD的概率大大不同。我们的研究突出了以下经济评估指南的重要性，以对参数生存分析技术的系统方法进行系统方法，以便选择最佳拟合参数存活分布。

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  • 来源
    《PharmacoEconomics》 |2019年第12期|共12页
  • 作者

    Djalalov Sandjar; Beca Jaclyn; Ewara Emmanuel M.; Hoch Jeffrey S.;

  • 作者单位

    Westminster Int Univ Tashkent 12 Istiqbol St Tashkent 100047 Uzbekistan;

    Canc Care Ontario Toronto ON Canada;

    Janssen Inc 19 Green Belt Dr Toronto ON M3C 1L9 Canada;

    Univ Calif Davis Dept Publ Hlth Sci Div Hlth Policy &

    Management Davis CA 95616 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 药学;
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