Estimation of relative survival has become the first and the most basic step when reporting cancer survival statistics. Standard estimators are in routine use by all cancer registries. However, it has been recently noted that these estimators do not provide information on cancer mortality that is independent of the national general population mortality. Thus they are not suitable for comparison between countries. Furthermore, the commonly used interpretation ofthe relative survival curve is vague and misleading. The present article attempts to remedy these basic problems. The population quantities of the traditional estimators are carefully described and their interpretation discussed. We then propose a new estimator of net survival probability that enables the desired comparability between countries. The new estimator requires no modeling and is accompanied with a straightforward variance estimate. The methods are described on real as well as simulated data.
COBISS.SI-ID: 28569561
Often the data units are described with discrete distributions (work describedwith citation distribution over time, population pyramid described asage-sex distribution etc.).When the set of such units is very large, appropriate clustering methods can reveal the typical patterns hidden in the data. In this paper we present an adapted leaders method combined with a compatible adapted agglomerative hierarchical method that are based on relative error measure between a unit and the corresponding cluster representative-leader. The proposed approach is illustrated on citation distributions derived from the data set of US patents from 1980 to 1999. Thesenew methods were developed because clustering of units, described with distributions, with classical k-means method reveals patterns with single highpeaks which correspond to a single year. These patterns prevail over otherdistribution shapes also present in the data. Compared with centers in k-means method, clustersć representatives obtained with the proposed new methods better detect typical distribution shapes of units. The obtained main cluster types for different sets of units show three main patterns: patents with early or late peak of importance to the community, and patents where the importance is slowly increasing throughout the time period.
COBISS.SI-ID: 28528345
Abstract Background. Population-based relative survival is widely used as a method of monitoring the success of cancer control. This success may not be relevant only for an entire country but also regional developments over time are of interest. It would not only be important that the relative survival improved but also that the differences between regions decreased over time. Methods. In this paper the authors show how relative survival methods can be used to study such differences. In addition to standard methods, some more recently introduced approaches are used, most notably a method for checking the goodness of fit of the relative survival model. This gives confidence in the obtained results and provides additional insight when assumptions are not met. Results. An analysis of cancers of the colon and ovary by cancer control region in Finland in 1953-2003 shows an overall improvement in relative survival, accompanied in colon cancer also by a decrease of differences in relative survival between the regions. Thus, the desired course was observed in colon cancer but not in cancer of the ovary. Conclusions. These results, applied to further sites, should lead to investigation of differences in cancer control policies between regions.
COBISS.SI-ID: 29064665