Methods for studying cancer patient survival with application in Stata
Course leader: Paul Dickman, Karolinska Institutet

Relative survival is the outcome measure of choice for studying cancer patient survival by using data collected by population-based cancer registries. The aim of this workshop is to discuss and compare approaches to estimating and modeling relative survival (excess mortality) and their application in Stata. The workshop will include presentations from invited speakers who have developed methodology that is implemented in Stata.

Speakers include Paul Dickman (estimating and modeling relative survival, the strs command), Paul Lambert (cure models, the strsmix and strsnmix commands), Bernard Rachet (estimating relative survival by using the strel command, modeling using fractional polynomials and splines, modeling and smoothing population mortality rates), Chris Nelson (fitting spline functions for relative survival models, the strsrcs command) and Ula Nur (modeling relative survival in the presence of incomplete data). The primary focus will be on approaches to modeling excess mortality, particularly when proportional excess hazards are not appropriate. Many of the approaches to modeling differ only in the method used for modeling the baseline hazard; the various approaches will be discussed from both a theoretical and practical approach.

All speakers will use standard datasets in their presentations to facilitate comparison of the various approaches. These datasets will be distributed to all participants in advance (along with research questions to be explored) and Stata code presented during the workshop will be available on the web. We will discuss proposals for future methodological development and possible collaboration, as well as possibilities for standardizing the Stata commands (at least to the extent it is possible). The workshop is aimed at participants who have prior knowledge of methods for population-based cancer survival analysis or have a very strong background in medical statistics and Stata.