Hu Linhan, Mandal Soutrik, Sinha Samiran
Department of Statistics, Texas A&M University, College Station, TX, USA.
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
J Stat Comput Simul. 2021;91(18):3894-3916. doi: 10.1080/00949655.2021.1955884. Epub 2021 Jul 27.
Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can be used for assessing and comparing treatment effects over the control group. Alternatively, as commonly perceived, parametric tests can also be used assuming data are generated from a parametric family of distributions. To provide some guidance on choosing an appropriate method, in this paper, the performance of parametric tests and a series of nonparametric tests are compared through extensive simulation studies that cover a wide range of scenarios with varying sample sizes, varying censoring mechanisms and varying alternative hypotheses. For the purpose of illustration, we also apply these procedures to analyse three real datasets.
在临床研究中，事件实际发生时间难以测量，区间删失数据很常见。已经提出了一些非参数检验方法，用于使用区间删失数据进行两样本检验，这些检验可用于评估和比较治疗组与对照组的治疗效果。另外，如通常所认为的，假设数据来自参数分布族，也可以使用参数检验。为了在选择合适方法方面提供一些指导，本文通过广泛的模拟研究比较了参数检验和一系列非参数检验的性能，这些模拟研究涵盖了各种场景，包括不同的样本量、不同的删失机制和不同的备择假设。为了说明目的，我们还应用这些方法分析了三个真实数据集。
