Abstract
As oesophageal cancer has short survival, it is likely pre-diagnosis health behaviours will have carry-over effects on post-diagnosis survival times. Cancer registry data sets do not usually contain pre-diagnosis health behaviours and so need to be augmented with data from external health surveys. A new algorithm is introduced and tested to augment cancer registries with external data when one-to-one data linkage is not available. The algorithm is to use external health survey data to impute pre-diagnosis health behaviour for cancer patients, estimate misclassification errors in these imputed values and then fit misclassification corrected Cox regression to quantify the association between pre-diagnosis health behaviour and post-diagnosis survival. Data from US cancer registries and a US national health survey are used in testing the algorithm. It is demonstrated that the algorithm works effectively on simulated smoking data when there is no age confounding. But age confounding does exist (risk of death increases with age and most health behaviours change with age) and interferes with the performance of the algorithm. The estimate of the hazard ratio (HR) of pre-diagnosis smoking was HR = 1.32 (95% CI 0.82,2.68) with HR = 1.93 (95% CI 1.08,7.07) in the squamous cell sub-group and pre-diagnosis physical activity was protective of survival with HR = 0.25 (95% CI 0.03, 0.81). But the method failed for less common behaviours (such as heavy drinking). Further improvements in the I2C2 algorithm will permit enrichment of cancer registry data through imputation of new variables with negligible risk to patient confidentiality, opening new research opportunities in cancer epidemiology.
Highlights
The estimate of the hazard ratio (HR) of pre-diagnosis smoking was HR = 1.32 with HR = 1.93 in the squamous cell sub-group and pre-diagnosis physical activity was protective of survival with HR = 0.25
The second chart suggests that smoking 5 years prior to diagnosis may be a hazard to survival (HR 1.32, 95% confidence interval (CI) 0.82,2.68) but binge drinking could be protective (HR = 0.49, 95% CI 0.13,1.29)
Physical activity outside work is a statistically significantly protective of survival (HR = 0.25, 95% CI 0.03,0.81)
Summary
Worldwide it is estimated that it accounts for 3.1% of all cancers and 5.5% of all cancer deaths [1]. It is still a relatively rare disease. Given the relatively short survival time, it is likely that pre-diagnosis health behaviour is an important contributor to post-diagnosis survival time. At the patient level, clearer understanding of the effect of pre-diagnosis health behaviour may assist in addressing the current weaknesses in prognostic indexes for oesophageal cancer [8]. As oesophageal cancer has short survival, it is likely pre-diagnosis health behaviours will have carry-over effects on post-diagnosis survival times. Cancer registry data sets do not usually contain pre-diagnosis health behaviours and so need to be augmented with data from external health surveys. A new algorithm is introduced and tested to augment cancer registries with external data when one-to-one data linkage is not available
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