Nonparametric interaction test with censored data

点击翻译按钮获取中文摘要

The test of interaction between two variables is of interest in many practical fields.  For example, in clinical trials, it is often of interest to perform subgroup analyses to assess whether treatment effect is uniform in all patient subgroups or to identify a subgroup of patients who particularly benefit from the treatment.  In this proposal, we will consider the test of interaction based on censored survival data.  In current medical applications, this is done almost exclusively based on the Cox proportional hazard models that require an assumption of proportional hazards that may not be satisfied by the clinical data.  An alternative method is needed, especially if the statistical test or practical background indicates possible violation of this assumption. We propose first to simplify some general nonparametric methods proposed for the factorial survival studies in the context of two binary independent variables and to construct the jackknife variance estimate for the test statistics, which would make these methods more accessible to applied statisticians.  New nonparametric test procedures will also be developed based on the smoothed estimate for a nonparametric measure of the interaction or the kernel estimate of the hazard ratio we derived recently.  Practical issues such as bandwidth selection will be discussed and the resampling method such as bootstrap will be used to approximate the distribution of the new test statistics derived. Extensive simulations will be performed to evaluate and compare all of these procedures in terms of actual significance level (type I error) and power (1-type II error).  The software implementing these procedures will be developed.  It is expected this research will provide new tools to the applied statisticians involved in the practical research and also build a solid theoretical foundation for the applications of these statistical methods in the practical fields to ensure that conclusions drawn with these methods are accurate and reliable.