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Quantile regression for right‐ or left‐censored outcomes has attracted attention due to its ability to accommodate heterogeneity in regression analysis of survival times. Rank‐based inferential methods have desirable properties for quantile regression analysis, but censored data poses challenges to the general concept of ranking. In this article, we propose a notion of censored quantile regression rank scores, which enables us to construct rank‐based tests for quantile regression coefficients at a single quantile or over a quantile region. A model‐based bootstrap algorithm is proposed to implement the tests. We also illustrate the advantage of focusing on a quantile region instead of a single quantile level when testing the effect of certain covariates in a quantile regression framework.

 

Results obtained from reliably designed randomized experiments are often considered to be evidence of the highest grade for assessing the effectiveness of biomedical or behavioral interventions. Nevertheless, even with randomized experiments, statistical bias can arise in post hoc analysis of the data or through adaptive data collection. In this article, we discuss the need for and review some of the recent developments in statistical methodologies to address the issue of potential bias in adaptive experiments and in subgroup analysis. For adaptive experiments, we focus on adaptive treatment assignments. For subgroup analysis, we focus on post hoc subgroup selection and review several frequentist approaches for debiased inference on the best‐selected subgroup effects.

This article is categorized under:

Applications of Computational Statistics > Clinical Trials

 

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.