A Weighted Risk Set Estimator for Survival Distributions in Two-Stage Randomization Designs with Censored Survival Data

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2005-01-01 DOI:10.2202/1557-4679.1000

Xiang Guo, A. Tsiatis

引用次数: 4

Abstract

In many clinical trials related to diseases such as cancers and HIV, patients are treated by different combinations of therapies. This leads to two-stage designs, where patients are initially randomized to a primary therapy and then depending on disease remission and patients' consent, a maintenance therapy will be randomly assigned. In such designs, the effects of different treatment policies, i.e., combinations of primary and maintenance therapy are of great interest. In this paper, we propose an estimator for the survival distribution for each treatment policy in such two-stage studies with right-censoring using the method of weighted estimation equations within risk sets. We also derive the large-sample properties. The method is demonstrated and compared with other estimators through simulations and applied to analyze a two-stage randomized study with leukemia patients.

查看原文本刊更多论文

带有删节生存数据的两阶段随机化设计中生存分布的加权风险集估计

在许多与癌症和艾滋病毒等疾病有关的临床试验中，患者接受不同的治疗组合。这导致了两阶段设计，患者最初随机接受主要治疗，然后根据疾病缓解和患者同意，随机分配维持治疗。在这样的设计中，不同治疗政策的效果，即初级治疗和维持治疗的组合是非常有趣的。在本文中，我们提出了一个估计器，在这种两阶段的研究中，每个治疗策略的生存分布与权利审查使用风险集中加权估计方程的方法。我们还推导了大样本性质。通过模拟验证了该方法，并与其他估计方法进行了比较，并应用于白血病患者的两期随机研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

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.