Using estimated probability of pre-diagnosis behavior as a predictor of cancer survival time: an example in esophageal cancer

• Main Authors: Paul P. Fahey, Andrew Page, Glenn Stone, Thomas Astell-Burt
• Format: Article
• Language: English
• Published: BMC 2020-04-01
• Series: BMC Medical Research Methodology
• Subjects: Esophageal cancer; Survival; Health behavior; Tobacco; Alcohol; Physical activity
• Online Access: http://link.springer.com/article/10.1186/s12874-020-00957-5

Abstract Background Information on the associations between pre-diagnosis health behavior and post-diagnosis survival time in esophageal cancer could assist in planning health services but can be difficult to obtain using established study designs. We postulated that, with a large data set, using estimated probability for a behavior as a predictor of survival times could provide useful insight as to the impact of actual behavior. Methods Data from a national health survey and logistic regression were used to calculate the probability of selected health behaviors from participant’s demographic characteristics for each esophageal cancer case within a large cancer registry data base. The associations between survival time and the probability of the health behaviors were investigated using Cox regression. Results Observed associations include: a 0.1 increase in the probability of smoking 1 year prior to diagnosis was detrimental to survival (Hazard Ratio (HR) 1.21, 95% CI 1.19,1.23); a 0.1 increase in the probability of hazardous alcohol consumption 10 years prior to diagnosis was associated with decreased survival in squamous cell cancer (HR 1.29, 95% CI 1.07, 1.56) but not adenocarcinoma (HR 1.08, 95% CI 0.94,1.25); a 0.1 increase in the probability of physical activity outside the workplace is protective (HR 0.83, 95% CI 0.81,0.84). Conclusions We conclude that probability for health behavior estimated from demographic characteristics can provide an initial assessment of the association between pre-diagnosis health behavior and post-diagnosis health outcomes, allowing some sharing of information across otherwise unrelated data collections.