Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine

Abstract

Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models. However, the presence of censored data such as survival time, often the main endpoint in cancer studies, has hampered the use of quantile regression techniques because of the incompleteness of data. With the advent of the precision medicine era and availability of high throughput data, quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches. This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low- or high-dimensionality. We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort, a hospital-based prospective cohort study, with the goals of broadening the scope of cancer research, maximizing the utility of collected data, and offering useful statistical alternatives. We use quantile regression to identify clinical and molecular predictors, for example CpG methylation sites, associated with high-risk lung cancer patients, for example those with short survival.

Clinical and Inflammatory Features of Exacerbation-Prone Asthma: A Cross-Sectional Study Using Multidimensional Assessment

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“…We also used quantile regression to analyze quantile treatment effects [19,20]. To draw the curve for the percentile treatment effect of carrageenan, we used the ci.qte procedure of the qte package of R [21].…”

Vitamin C May Increase the Recovery Rate of Outpatient Cases of SARS-CoV-2 Infection by 70%: Reanalysis of the COVID A to Z Randomized Clinical Trial

“…The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2021. 674681/full#supplementary-material -4.61 to -3 days; t = 7.3 P = 10 -10 ), calculated with the fit.crq procedure of the R-statistical software (15)(16)(17). The red numbers at the bottom indicate the lowest percentile level for the odd number of days of duration of SARS-CoV-2 infection in the usual care arm.…”

“…We also analyzed the effect of vitamin C by quantile regression as the quantile treatment effect (QTE) ( 15 – 17 ). In this approach, the distribution of symptom duration in the usual care arm is set on the horizontal axis as percentiles and the difference between the usual care and vitamin C arms is shown as the QTE on the vertical axis ( Figure 1 ).…”