Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up.

IF 1.2 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2020-10-12 DOI:10.1515/ijb-2020-0042

James McVittie, David Wolfson, David Stephens, Vittorio Addona, David Buckeridge

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引用次数: 3

Abstract

A classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.

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来自事件队列研究和流行队列研究的合并失效时间数据的参数化模型。

生存分析中的一个经典问题是从事件队列研究中获得的右截尾观察值估计失效时间分布。然而，失效时间数据通常由两个独立的样本组成，一个来自事件队列研究，另一个来自随访的流行队列研究，这是已知的，会产生长度偏差的观察失效时间。分开来看，这两种类型的研究都有各自的缺点。我们在这里解决了两个主要问题:(i)我们的统计推断是否可以通过将事件队列研究的数据与带有随访的流行队列研究的数据相结合来增强?何种统计方法适合于这些合并的数据?我们为解决这些问题而开发的理论是基于参数化定义的失效时间分布，并得到仿真的支持。我们应用我们的方法来估计住院时间。

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 MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

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.