Peripheral Blood Biomarkers Associated with Clinical Outcome in Non–Small Cell Lung Cancer Patients Treated with Nivolumab: Methodological Issues
Authors: Saeid Safiri, Ahad Ashrafi-Asgarabad
Journal: Journal of Thoracic Oncology (2018)

We were interested to read the article by Tanizaki et al. that was published in the Journal of Thoracic Oncology in 2018.1Tanizaki J. Haratani K. Hayashi H. et al.Peripheral blood biomarkers associated with clinical outcome in non–small cell lung cancer patients treated with nivolumab.J Thorac Oncol. 2018; 13: 97-105Abstract Full Text Full Text PDF PubMed Scopus (144) Google Scholar They conducted a study to examine the association between baseline peripheral blood biomarkers with clinical outcome in patients with NSCLC treated with nivolumab. They concluded that baseline signature of a low absolute neutrophil count, high absolute lymphocyte count, and high absolute eosinophil count were associated with a better outcome of nivolumab treatment. We found that Tanizaki et al. have conducted an interesting study, but some methodological issues need to be noted. First, the Cox proportional hazard (PH) regression model is one of the most common regression models that is used in survival studies; but its results are fully dependent on PH assumption and may not be valid if this assumption is violated.2Kleinbaum D.G. Klein M. Survival Analysis. Springer, New York, NY2010Google Scholar This assumption can be examined using the various graphical and statistical tests. On the basis of the Kaplan-Meier curves provided in the Figures 1 and 2 provided by Tanizaki et al., the PH assumption does not seem to be established. The extended Cox regression model is one of the most appropriate models that can be used when the PH assumption is violated.2Kleinbaum D.G. Klein M. Survival Analysis. Springer, New York, NY2010Google Scholar Hence, this assumption needs to be examined, and an appropriate Cox model should be used. Second, time-varying variables are one the concerns in the longitudinal studies that need to be addressed in the statistical tests. Otherwise, biased results may result.3Mansournia M.A. Etminan M. Danaei G. Kaufman J.S. Collins G. Handling time varying confounding in observational research.BMJ. 2017; 359: j4587Crossref PubMed Scopus (173) Google Scholar For example, the authors have considered smoking status as a fixed variable, whereas the smoking seems to vary during the study period. Parametric G formula and G estimation are among the efficient methods that have been introduced recently and can address the time-varying exposures and confounders in the regression modeling.3Mansournia M.A. Etminan M. Danaei G. Kaufman J.S. Collins G. Handling time varying confounding in observational research.BMJ. 2017; 359: j4587Crossref PubMed Scopus (173) Google Scholar The take-home message is that the assumptions of the Cox regression model need to be examined before the model building process. Also, time-varying exposures and confounders can be handled appropriately by using advanced statistical methods. Peripheral Blood Biomarkers Associated with Clinical Outcome in Non–Small Cell Lung Cancer Patients Treated with NivolumabJournal of Thoracic OncologyVol. 13Issue 1PreviewThe aim of this study was to identify baseline peripheral blood biomarkers associated with clinical outcome in patients with NSCLC treated with nivolumab. Full-Text PDF Open Archive