Tilbake til søkeresultatene

FRINATEK-Fri prosj.st. mat.,naturv.,tek

Stochastic differential equations for robust evaluation of cancer treatments with registry data

Alternativ tittel: Stokastiske differensialligninger for robust evaluering avd kreftbehandling basert på registerdata.

Tildelt: kr 11,8 mill.

I de første månedene av prosjektet har vi arbeidet med å forstå de statistiske modellene som opprinnelige ble foreslått i prosjektplanen i lys av såkalt semi-parametrisk teori. Dette handler primært om å tenke på de statistiske modellene som en type geometriske objekter der man kan snakke differensialer. Motivasjonen vår er at man ofte på denne måten kan identifisere hvor godt det er mulig å bruke dataene vi har til å estimere forskjellige parametere. Vi vil bruke dette til å avgjøre hvorvidt de konkrete estimeringsmetodene vi tenker å bruke er optimale eller ikke. Semiparametrisk teori er godt kjent i matematisk statistikk, men har ikke vært brukt tidligere for modellene som er basert på stokastiske differensial ligninger som vi er interessert.

The overall objective of this project is to develop better causal inference methodology for trustworthy evaluation of cancer treatments from registry data in order to support decision makers when forming official clinical guidelines. Making such decisions based on results from emulated trials will necessarily involve uncertainty. However, in the absence of RCTs, this might be the best source of information that is available. Choosing the right treatment can be a matter of life and death, so it is important to also pay attention to the amount of uncertainty that is involved in these analyses. We will therefore put much emphasis on developing methods for assessing how trustworthy such analyses will be in various settings. This will both involve robustness towards model misspecification and general tools to evaluate the statistical uncertainty. Most of the available observational data for evaluating cancer treatments is on a time-to-event form, like the data one usually consider in survival analysis. We claim, with some exceptions, that the survival analysis aspect has not been taken sufficiently into account in causal inference. Methods, designed for other settings, are often applied to survival data in an ad hoc manner that results in less transparent modeling assumptions and more statistical uncertainty than necessary. We believe, however, that there is much potential in exploiting more of the mathematical machinery related to SDEs that is currently being used in mathematical finance and related fields. Our research plan will if successful provide a streamlined toolbox for using registry data to evaluate effects of cancer treatments based on such techniques.

Aktivitet:

FRINATEK-Fri prosj.st. mat.,naturv.,tek