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首页> 外文学位 >Extension of the Regression Method for Imputation of Data with Monotone Missing Pattern using Multivariate Adaptive Regression Splines (MARS), with Applications to Systematic- Missing-At-Random (SMAR) Study Designs
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Extension of the Regression Method for Imputation of Data with Monotone Missing Pattern using Multivariate Adaptive Regression Splines (MARS), with Applications to Systematic- Missing-At-Random (SMAR) Study Designs

机译：利用多元自适应回归样条（MARS）扩展单调缺失模式数据插补的回归方法，并应用于系统随机缺失研究（SMAR）研究设计

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摘要

Systematic-Missing-At-Random (SMAR) studies are designed to deal with resource limitations. These designs use the entire study group to measure primary endpoints, important covariates and `inexpensive variables' and use nested random sub- samples to measure more `expensive' variables. These study designs generate monotone missing data. The imputation method used to restore the complete data is key to the accuracy of the statistical analysis after the data collection.;This thesis reviews some generally accepted imputation methods that can be used in monotone missing data such as data generated from SMAR study designs. These methods include the EM algorithm, the regression method, and the predictive mean nearest neighbor method (PMN), among others. We discuss the underlying principles and compare advantages and disadvantages of these methods. We propose a new regression-based imputation method: multivariate adaptive regression splines (MARS) imputation. We compare the performance of this new method in logistic regression models to three other imputation methods: the EM algorithm, the regression method and the predictive mean nearest neighbor (PMN) under four simulated scenarios: (1) highly correlated covariates; (2) nonlinearity between covariates; (3) uncorrelated covariates; and (4) non-normal covariates. We evaluate the performance of these methods in these settings using three different measures of performance. We demonstrate that the performance of MARS is superior when the covariates are highly correlated or nonlinearly related, and its performance is non-inferior to the EM algorithm in the other two situations. Both MARS and EM outperform the regression method and PMN in general. We also examine the effects of sample size, number of variables, outliers and varying percentages of missingness on the performance of these methods. MARS imputation is more robust than the other regression-based methods that we evaluate and does not underperform the EM algorithm in the presence of these interferences. An example that uses biomarker data from NYU Lung Cancer Early Detection Research Network Center (Grant Number: EDRN - U01CA86137 from the National Cancer Institute) cohort is provided to illustrate the application of MARS imputation in practice. Because of the relative simplicity of the MARS method compared to the EM algorithm, we propose MARS imputation as a better imputation method when data result from SMAR-type study designs with monotone missing data, particularly when there is high correlation or nonlinearity among imputed variables.
机译：系统性随机遗漏（SMAR）研究旨在处理资源限制。这些设计使用整个研究小组来衡量主要终点，重要的协变量和“廉价变量”，并使用嵌套的随机子样本来衡量更多“昂贵”变量。这些研究设计生成单调缺失数据。用于恢复完整数据的归因方法是数据收集后统计分析准确性的关键。本论文回顾了一些可用于单调缺失数据（例如从SMAR研究设计生成的数据）的归因方法。这些方法包括EM算法，回归方法和预测平均最近邻方法（PMN）等。我们讨论了基本原理，并比较了这些方法的优缺点。我们提出了一种新的基于回归的插补方法：多元自适应回归样条（MARS）插补。我们将这种新方法在逻辑回归模型中与其他三种归因方法的性能进行比较：EM算法，回归方法和四种模拟情况下的预测平均最近邻（PMN）：（1）高相关协变量； （2）协变量之间的非线性； （3）不相关的协变量； （4）非正态协变量。我们使用三种不同的性能度量来评估这些方法在这些设置中的性能。我们证明，当协变量高度相关或非线性相关时，MARS的性能优越，并且在其他两种情况下，其性能也不低于EM算法。一般而言，MARS和EM均优于回归方法和PMN。我们还研究了样本量，变量数量，离群值和不同百分比的缺失对这些方法的性能的影响。与我们评估的其他基于回归的方法相比，MARS归因方法更可靠，并且在存在这些干扰的情况下不会落后于EM算法。提供了一个使用来自纽约大学肺癌早期检测研究网络中心（国家癌症研究所的授权号：EDRN-U01CA86137）的生物标记数据的示例，以说明MARS归因在实践中的应用。由于与EM算法相比，MARS方法相对简单，因此，当SMAR类型研究设计的数据结果具有单调缺失数据时，尤其是在估算变量之间存在高度相关性或非线性时，我们建议采用MARS估算作为更好的估算方法。

著录项

  • 作者

    Lu, Feng.;

  • 作者单位

    New York University.;

  • 授予单位 New York University.;
  • 学科 Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 252 p.
  • 总页数 252
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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