Evaluating eight smoking metrics for modelling survival in non-small cell lung cancer
Andrew CL Lam·Geoffrey Liu·Sanjay Shete·Angeline S. Andrew·Marian L. Neuhouser·Michael P.A. Davies·R. Hung·Yangqing Deng·Angela S. Wenzlaff·Jui Kothari·Alberto Ruano-Ravina·Matthew J Barnett·Letícia Ferro Leal·Mónica Pérez-Ríos·Ming Sound Tsao·Guillermo Fernandez-Tardon·Wei Xu·Dana Mates·Ping Yang·Fiona Taylor·Kouya Shiraishi·Hermann Brenner·Katrina Hueniken·John (1782 - 1837) Field·Beata Świątkowska·Rui Manuel Reis·Juncheng Dai·David Zaridze·Yao Ting Li·Takashi Kohno·Hongbing Shen·Natasha B. Leighl·Bernd Holleczek·Frances A. Shepherd·Maria Teresa Landi·Ryan Diver·Jolanta Lissowska·Matthew B. Schabath·Ying Wang (11406)·Adonina Tardon·Milan Savic·Haoran Liu·M.Catherine Brown·Paul Brennan·David C. Christiani·Ann G. Schwartz·Zhichao Wang·Ivana Holcatova·Jie Zhang (64655)·Hongxia Ma·Kiera R. Murison·Curtis Diamond Harris·Angela Cecilia Pesatori
Logcig-years best modelled the relationship between smoking exposure and OS as well as LCSS, and had consistent associations across clinicodemographic subgroups. Logcig-years should be considered in clinical and research applications for quantifying smoking exposure in lung cancer.