Open AccessLong-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study
Author(s) -
Georgios Alexopoulos,
Justin K. Zhang,
Ioannis Karampelas,
Mayur Patel,
Joanna Kemp,
Jeroen Coppens,
Tobias A. Mattei,
Philippe Mercier
Publication year - 2022
Publication title -
neuroepidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.217
H-Index - 87
eISSN - 1423-0208
pISSN - 0251-5350
DOI - 10.1159/000522611
Subject(s) - medicine , proportional hazards model , survival analysis , population , epidemiology , multivariate analysis , multivariate statistics , demography , glioblastoma , incidence (geometry) , relative survival , oncology , cancer registry , statistics , physics , mathematics , environmental health , cancer research , sociology , optics
Objective: Glioblastomas multiforme (GBMs) are the most common primary CNS tumors. Epidemiologic studies have investigated the effect of demographics on patient survival, but the literature remains inconclusive. Methods: This study included all adult patients with intracranial GBMs reported in the surveillance epidemiology and end results (SEER)-9 population database (1975–2018). The sample consisted of 32,746 unique entries. We forecast the annual GBM incidence in the US population through the year 2060 using time series analysis with autoregressive moving averages. A survival analysis of the GBM-specific time to death was also performed. Multivariate Cox proportional hazards (PH) regression revealed frank violations of the PH assumption for multiple covariates. Parametric models best described the GBM population’s survival pattern; the results were compared to the semi-parametric analysis and the published literature. Results: We predicted an increasing GBM incidence, which demonstrated that by the year 2060, over 1,800 cases will be reported annually in the SEER. All eight demographic variables were significant in the univariable analysis. The calendar year 2005 was the cutoff associated with an increased survival probability. A male survival benefit was eliminated in the year-adjusted Cox. Infratentorial tumors, nonmetropolitan areas, and White patient race were the factors erroneously associated with survival in the multivariate Cox analysis. Accelerated Failure Time (AFT) lognormal regression was the best model to describe the survival pattern in our patient population, identifying age >30 years old as a poor prognostic and patients >70 years old as having the worst survival. Annual income >USD 75,000 and supratentorial tumors had good prognostics, while surgical intervention provided the strongest survival benefit. Conclusions: Annual GBM incidence rates will continue to increase by almost 50% in the upcoming 30 years. Cox regression analysis should not be utilized for time-to-event predictions in GBM survival statistics. AFT lognormal distribution best describes the GBM-specific survival pattern, and as an inherent population characteristic, it should be implemented by researchers for future studies. Surgical intervention provides the strongest survival benefit, while patient age >70 years old is the worst prognostic. Based on our study, the demographics such as gender, race, and county type should not be considered as meaningful prognostics when designing future trials.