SEER RRSS Using Multiple Imputation to Enhance the Utility of SEER Summary Stage

SEER RRSS 使用多重插补增强 SEER 摘要阶段的实用性

• 批准号: 8351006
• 负责人: THOMAS TUCKER
• 金额: $ 5.3万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8351006
• 关键词: Address; Affect; Age Distribution; Categories; Code; Communities; Computer software; Data; Data Analyses; Data Set; Influentials; Lead; Malignant neoplasm of liver; Malignant neoplasm of lung; Medical; Methods; Pattern; Performance; Procedures; Research Personnel; Staging; Statistical Methods; Techniques; Uncertainty; base; cancer diagnosis; cancer site; data registry; high standard; meetings; neoplasm registry

Multiple Imputation (MI) methods have been widely used in many scientific fields to address missing data issues. Several statistical software packages have implemented MI procedures. However, their performance varies. Attempts were made to compare performance of MI procedures in varying statistical packages. Because of the complexity of MI, comparisons were made based on specific settings, such as assuming missing patterns were monotonic missing, the missing variable was semi-continuous, or the missing data were simple artificial data. None of these comparisons addressed which methods are best used in a large data set, such as the SEER registry data with non-monotonic missing pattern and many variables. This study will investigate issues more specific to the SEER registry data when using MI methods to handle missing data. Through this study, guidance for researchers in the cancer registry community will be provided with regard to properly handling of the missing data issue in cancer registry data. Missing data is a frequent problem in most scientific studies and a common feature of large data sets in general and medical data sets in particular. It can cause bias or lead to inefficient analyses if not handled properly. Because of its high standard requirements, the SEER data have only a small fraction of missing data for most of the variables collected. However, one very important variable, the SEER Summary Stage (1977, 2000 and CS), contains a higher percentage of missing or unknown data, especially for certain cancer sites. For example, 9.8% of the lung cancer cases and 22% of the liver cancer cases were coded as unknown for the variable SEER Summary Stage 2000 for the 2001-2003 SEER data. The complete case method (listwise deletion) is the most commonly used method to address this missing data issue for data analysis among researchers in the cancer registry community. If missing data are not missing completely at random, using the complete case method will introduce bias and generate incorrect results. For the same study data mentioned above, the distributions of age at cancer diagnosis for cases with known stage and cases with unknown stage were significantly different ¿ 34% of cases were 75 years old or older for known stage while 54% of cases were 75 years old or older for unknown stage. This strongly suggests cases with unknown stage were not missing completely at random. Hence, the complete case method is not an ideal method to analyze the data. Coding the cases with unknown stage as a separate sub-category will certainly include all cases in data analysis, but unfortunately, severe bias has been found for this type of analysis when data are not missing completely at random. Compared to the complete case method, MI, one of the more sophisticated methods to handle missing data, provides superior estimates when data are missing at random. First proposed in 1978, MI has become an important and influential approach in the statistical analysis of missing data in recent years because it is easy to use and readily available in many statistical packages. MI replaces each missing value with a set of plausible values that represents the uncertainty about the most appropriate value to impute, then combines results from separate data analyses for each complete dataset to generate the final estimates. It has been suggested that MI often provides valid and robust inferences even when assumptions were not met.

多重插补（MI）方法已广泛应用于许多科学领域，以解决缺失数据问题。若干统计软件包已执行了管理信息程序。然而，它们的表现各不相同。尝试在不同的统计数据包中比较MI手术的性能。由于MI的复杂性，基于特定设置进行比较，例如假设缺失模式为单调缺失、缺失变量为半连续或缺失数据为简单人工数据。这些比较都没有说明哪种方法最适合用于大型数据集，例如具有非单调缺失模式和许多变量的SEER登记数据。本研究将调查使用MI方法处理缺失数据时SEER登记数据的更具体问题。通过这项研究，将为癌症登记研究界的研究人员提供关于正确处理癌症登记数据中缺失数据问题的指导。 缺失数据是大多数科学研究中常见的问题，也是一般大型数据集，特别是医学数据集的共同特征。如果处理不当，它可能会导致偏见或导致低效的分析。由于其高标准的要求，SEER数据只有一小部分的缺失数据的大多数变量收集。然而，一个非常重要的变量，SEER汇总阶段（1977年，2000年和CS），包含更高百分比的缺失或未知数据，特别是对于某些癌症部位。例如，对于2001-2003年SEER数据，9.8%的肺癌病例和22%的肝癌病例被编码为变量SEER汇总阶段2000未知。 完整病例方法（列表删除）是癌症登记社区研究人员在数据分析中解决这种缺失数据问题的最常用方法。如果缺失数据不是完全随机缺失的，则使用完整病例方法将引入偏倚并生成不正确的结果。对于上述相同的研究数据，已知分期病例和未知分期病例的癌症诊断年龄分布存在显著差异，已知分期病例中34%的病例为75岁或以上，而未知分期病例中54%的病例为75岁或以上。这强烈表明，分期未知的病例并非完全随机缺失。因此，完全案例法不是分析数据的理想方法。将分期未知的病例编码为一个单独的子类别，肯定会包括数据分析中的所有病例，但不幸的是，当数据不是完全随机缺失时，这种类型的分析会发现严重的偏倚。 与完整病例方法相比，MI是处理缺失数据的更复杂的方法之一，当数据随机缺失时，MI提供了上级估计。MI于1978年首次提出，近年来已成为缺失数据统计分析中一种重要和有影响力的方法，因为它易于使用，并且可以在许多统计软件包中使用。MI将每个缺失值替换为一组合理值，这些值代表了最适合插补的值的不确定性，然后将每个完整数据集的单独数据分析结果合并，以生成最终估计值。有人建议，MI往往提供有效和强大的推论，即使假设不满足。