Statistical methods

The development of statistical data analysis methods helps us gain a better understanding of the causes of cancer and epidemiology, and of cancer screening in Finland.


Cancer research is supported and facilitated by using the appropriate statistical methods. The assessment of cancer survival is a key part of methodological cancer research. It has focused on more reliable methods of the studies on long-term survival and better computational regional comparative methods. Methods based on computational statistics have also been developed for assessing population attributable fractions.

The modelling of the hidden factors of cancer was investigated in a cooperative project with Aalto University, in addition to finding new ways to evaluate the overdiagnosis associated with screening. Also, new methods of assessing the heritability and familial aggregation of cancer are studied and possibilities for utilizing self-learning computational statistics in cancer coding are currently under investigation.

All of the projects described above are currently ongoing.

Most important publications:

Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation

On Exploring Hidden Structures Behind Cervical Cancer Incidence

Comparing net survival estimators of cancer patients

Choosing the net survival method for cancer survival estimation

Regional variation in relative survival—quantifying the effects of the competing risks of death by using a cure fraction model with random effects

Research team: Janne Pitkäniemi, Karri Seppä, Joonas Miettinen, Tiina Hakanen, Matti Rantanen, Heidi Ryynänen

Funding: Cancer Society of Finland, Cancer Foundation of Finland.

Collaborating institutions: University of Helsinki / Department of Computer Science, Aalto University, University of Oulu, Department of Public Health

Lead researcher: Janne Pitkäniemi