InterPreT cancer survival: A dynamic web interactive prediction cancer survival tool for health-care professionals and cancer epidemiologists.

BACKGROUND: There are a variety of ways for quantifying cancer survival with each measure having advantages and disadvantages. Distinguishing these measures and how they should be interpreted has led to confusion among scientists, the media, health care professionals and patients. This motivates the development of tools to facilitate communication and interpretation of these statistics. METHODS: "InterPreT Cancer Survival" is a newly developed, publicly available, online interactive cancer survival tool targeted towards health-care professionals and epidemiologists (http://interpret.le.ac.uk). It focuses on the correct interpretation of commonly reported cancer survival measures facilitated through the use of dynamic interactive graphics. Statistics presented are based on parameter estimates obtained from flexible parametric relative survival models using large population-based English registry data containing information on survival across 6 cancer sites; Breast, Colon, Rectum, Stomach, Melanoma and Lung. RESULTS: Through interactivity, the tool improves understanding of various measures and how survival or mortality may vary by age and sex. Routine measures of cancer survival are reported, however, individualised estimates using crude probabilities are advocated, which is more appropriate for patients or health care professionals. The results are presented in various interactive formats facilitating understanding of individual risk and differences between various measures. CONCLUSIONS: "InterPreT Cancer Survival" is presented as an educational tool which engages the user through interactive features to improve the understanding of commonly reported cancer survival statistics. The tool has received positive feedback from a Cancer Research UK patient sounding board and there are further plans to incorporate more disease characteristics, e.g. stage.

Direct likelihood inference on the cause-specific cumulative incidence function: A flexible parametric regression modelling approach.

In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) that can be calculated by either (1) transforming on the cause-specific hazard or (2) through its direct relationship with the subdistribution hazard. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when we look to questions on prognosis. We also extend cure models using a similar approach described by Andersson et al for flexible parametric relative survival models. Using SEER public use colorectal data, we compare and contrast our approach with standard methods such as the Fine & Gray model and show that many useful out-of-sample predictions can be made after modelling the cause-specific CIFs using an FPM approach. Alternative link functions may also be incorporated such as the logit link. Models can also be easily extended for time-dependent effects.

stpm2cr: A flexible parametric competing risks model using a direct likelihood approach for the cause-specific cumulative incidence function.

In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) which is usually obtained in a modelling framework by either (1) transforming on all of the cause-specific hazard (CSH) or (2) through its direct relationship with the subdistribution hazard (SDH) function. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when prognostic related questions are to be answered. We propose the direct FPM approach for the cause-specific CIF which models the (log-cumulative) baseline hazard without the requirement of numerical integration leading to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and alternative link functions can be incorporated, for example, the logit link. To implement the methods, a new estimation command, stpm2cr, is introduced and useful predictions from the model are demonstrated through an illustrative Melanoma dataset.