存活率和逆向复发结果的联合建模:美国生育治疗相关因素分析

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Siyuan Guo, Jiajia Zhang, Alexander C McLain
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引用次数: 0

摘要

本文的目的是通过横断面样本,确定目前试图怀孕的妇女中与不孕症治疗时间(TTFT)相关的因素。由于怀孕时间(TTP)与 TTFT 之间存在依赖关系,因此存在挑战。我们建议采用边际加速失败时间模型来识别 TTFT 的风险因素,同时采用 TTP 模型,将生育治疗作为时变治疗纳入其中,以考虑两者的依赖性。后者需要扩展后向复现生存法,以纳入具有时变系数的时变协变量。由于后向递推生存率方法是平均生存率的函数,当生育治疗是非观测变量时,即 TTFT 是有删减的,在计算平均生存率时就会遇到困难。我们为 TTP 和 TTFT 的双重期望开发了便于计算的形式,从而解决了这些难题。我们通过全面的模拟研究对其性能进行了验证。我们将这一方法应用于全国家庭成长调查,并探讨了与美国 TTFT 延长相关的因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint modelling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment in the U.S.

The motivation for this paper is to determine factors associated with time-to-fertility treatment (TTFT) among women currently attempting pregnancy in a cross-sectional sample. Challenges arise due to dependence between time-to-pregnancy (TTP) and TTFT. We propose appending a marginal accelerated failure time model to identify risk factors of TTFT with a model for TTP where fertility treatment is included as a time-varying treatment to account for their dependence. The latter requires extending backwards recurrence survival methods to incorporate time-varying covariates with time-varying coefficients. Since backwards recurrence survival methods are a function of mean survival, computational difficulties arise in formulating mean survival when fertility treatment is unobserved, i.e. when TTFT is censored. We address these challenges by developing computationally friendly forms for the double expectation of TTP and TTFT. The performance is validated via comprehensive simulation studies. We apply our approach to the National Survey of Family Growth and explore factors related to prolonged TTFT in the U.S.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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