Marginal Models for Censored Longitudinal Cost Data: Appropriate Working Variance Matrices in Inverse-Probability-Weighted GEEs Can Improve Precision

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-02-07 DOI:10.2202/1557-4679.1170

E. Pullenayegum, A. Willan

引用次数: 1

Abstract

When cost data are collected in a clinical study, interest centers on the between-treatment difference in mean cost. When censoring is present, the resulting loss of information can be limited by collecting cost data for several pre-specified time intervals, leading to censored longitudinal cost data. Most models for marginal costs stratify by time interval. However, in few other areas of biostatistics would we stratify by default. We argue that there are benefits to considering more general models: for example, in some settings, pooling regression coefficients across intervals can improve the precision of the estimated between-treatment difference in mean cost. Previous work has used inverse-probability-weighted GEEs coupled with an independent working variance to estimate parameters from these more general models. We show that the greatest precision benefits of non-stratified models are achieved by using more sophisticated working variance matrices.

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删减纵向成本数据的边际模型:在反概率加权GEEs中适当的工作方差矩阵可以提高精度

当在临床研究中收集成本数据时，兴趣集中在平均成本的治疗间差异上。当存在审查时，可以通过收集几个预先指定的时间间隔的成本数据来限制所导致的信息丢失，从而导致审查的纵向成本数据。大多数边际成本模型按时间间隔分层。然而，在生物统计学的其他领域，我们不会默认分层。我们认为，考虑更一般的模型是有好处的:例如，在某些情况下，跨区间的回归系数池化可以提高估计平均成本的处理间差异的精度。以前的工作使用了逆概率加权的GEEs，再加上一个独立的工作方差，从这些更一般的模型中估计参数。我们表明，非分层模型的最大精度效益是通过使用更复杂的工作方差矩阵来实现的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.