Use of period analysis to provide a timely assessment of 5-year relative survival for pancreatic cancer patients from Taizhou, eastern China

Abstract

Assessing long-term tumor survival rates is crucial for evaluating the effectiveness of tumor treatment and burden. However, timely assessment of long-term survival in patients with pancreatic cancer is lagging in China. In this study, we applied period analysis to estimate the long-term survival of pancreatic cancer patients using data from four population-based cancer registries in Taizhou city, eastern China. A total of 1121 patients diagnosed with pancreatic cancer between 2004 and 2018 were included. We assessed the 5-year relative survival (RS) using period analysis and further stratified by sex, age at diagnosis, and region. The 5-year RS during 2014–2018 overall reached 18.9% (14.7% for men and 23.3% for women, respectively). A decrease of the 5-year RS from 30.3% to 11.2% was observed in four diagnostic age gradients (< 55, 55–64, 65–74, and > 74 years age groups). The 5-year RS was higher in urban (24.2%) than in rural (17.4%) areas. Moreover, the 5-year RS of pancreatic cancer patients showed an overall increasing trend for the three periods (2004–2008, 2009–2013, and 2014–2018). Our study, using period analysis for the first time in China, provides the latest estimates of the survival of patients with pancreatic cancer, which provides essential evidence for the prevention and intervention of pancreatic cancer. The results also indicate the importance of further applications of the period analysis for more up-to-date and accurate survival estimates.

Analysis and prediction of 5-year survival in patients with cutaneous melanoma: a model-based period analysis

“…The ratio between the observed survival rate of the population with cancer and the anticipated survival rate of the general population is known as the relative survival rate, which is most often used in cancer statistics and is a crucial metric for assessing patient prognoses ( 11 , 12 ). Analyzing relative survival rates using the period analysis can help reveal cyclical patterns in the data, improve prediction accuracy, and provide more insights for decision making ( 13 ). It can help medical professionals better understand and utilize time series data to improve medical decisions and treatment strategies.…”