3-524 Department of Pediatrics, University of Alberta, Edmonton Clinic Health Academy, Edmonton, T6G 1C9, Canada.
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
BMC Med Res Methodol. 2020 Dec 7;20(1):297. doi: 10.1186/s12874-020-01181-x.
Administrative databases offer vast amounts of data that provide opportunities for cost-effective insights. They simultaneously pose significant challenges to statistical analysis such as the redaction of data because of privacy policies and the provision of data that may not be at the level of detail required. For example, ages in years rather than birthdates available at event dates can pose challenges to the analysis of recurrent event data.
Hu and Rosychuk provided a strategy for estimating age-varying effects in a marginal regression analysis of recurrent event times when birthdates are all missing. They analyzed emergency department (ED) visits made by children and youth and privacy rules prevented all birthdates to be released, and justified their approach via a simulation and asymptotic study. With recent changes in data access rules, we requested a new extract of data for April 2010 to March 2017 that includes patient birthdates. This allows us to compare the estimates using the Hu and Rosychuk (HR) approach for coarsened ages with estimates under the true, known ages to further examine their approach numerically. The performance of the HR approach under five scenarios is considered: uniform distribution for missing birthdates, uniform distribution for missing birthdates with supplementary data on age, empirical distribution for missing birthdates, smaller sample size, and an additional year of data.
Data from 33,299 subjects provided 58,166 ED visits. About 67% of subjects had one ED visit and less than 9% of subjects made over three visits during the study period. Most visits (84.0%) were made by teenagers between 13 and 17 years old. The uniform distribution and the HR modeling approach capture the main trends over age of the estimates when compared to the known birthdates. Boys had higher ED visit frequencies than girls in the younger ages whereas girls had higher ED visit frequencies than boys for the older ages. Including additional age data based on age at end of fiscal year did not sufficiently narrow the widths of potential birthdate intervals to influence estimates. The empirical distribution of the known birthdates was close to a uniform distribution and therefore, use of the empirical distribution did not change the estimates provided by assuming a uniform distribution for the missing birthdates. The HR approach performed well for a smaller sample size, although estimates were less smooth when there were very few ED visits at some younger ages. When an additional year of data is added, the estimates become better at these younger ages.
Overall the Hu and Rosychuk approach for coarsened ages performed well and captured the key features of the relationships between ED visit frequency and covariates.
行政数据库提供了大量数据，为经济高效地获取洞察提供了机会。但同时，它们也给统计分析带来了重大挑战，例如由于隐私政策而需要对数据进行删改，以及提供的可能不符合所需详细程度的数据。例如，事件日期提供的是年龄而不是出生日期，这会给复发性事件数据的分析带来挑战。
Hu 和 Rosychuk 提供了一种策略，用于在复发性事件时间的边缘回归分析中估计年龄变化的影响，当时所有的出生日期都缺失。他们分析了儿童和青少年急诊就诊的数据，隐私规则阻止了所有出生日期的发布，并通过模拟和渐近研究证明了他们的方法是合理的。最近数据访问规则发生了变化，我们请求了一个新的 2010 年 4 月至 2017 年 3 月的数据提取，其中包含患者的出生日期。这使我们可以使用 Hu 和 Rosychuk（HR）方法对粗化年龄进行比较，并根据真实、已知年龄进行估计，以进一步从数值上检验他们的方法。考虑了 HR 方法在五种情况下的性能：缺失出生日期的均匀分布、缺失出生日期和年龄补充数据的均匀分布、缺失出生日期的经验分布、较小的样本量和额外一年的数据。
来自 33299 名受试者的数据提供了 58166 次急诊就诊。约 67%的受试者有一次急诊就诊，不到 9%的受试者在研究期间就诊超过三次。大多数就诊（84.0%）是 13 至 17 岁的青少年。与已知出生日期相比，均匀分布和 HR 建模方法捕捉了年龄对估计值的主要趋势。在较年轻的年龄组中，男孩的急诊就诊频率高于女孩，而在较年长的年龄组中，女孩的急诊就诊频率高于男孩。根据财政年度结束时的年龄额外增加年龄数据并不能充分缩小潜在出生日期区间的宽度，从而影响估计值。已知出生日期的经验分布接近均匀分布，因此，对于缺失出生日期假设均匀分布，使用经验分布不会改变提供的估计值。HR 方法在较小的样本量下表现良好，尽管在某些较年轻的年龄组中，急诊就诊次数非常少时，估计值不太平滑。当增加一年的数据时，这些较年轻的年龄组的估计值会更好。
总体而言，Hu 和 Rosychuk 的粗化年龄方法表现良好，捕捉到了急诊就诊频率与协变量之间关系的关键特征。
