Tools for Cancer Survival Analysis
Cancer Research UK Cancer Survival Group
- strel and strel2 (estimation of excess hazard and relative survival)
- ewblft (creation and smoothing of life tables)
- Graphical tools for performance indicators (funnel plots and smoothed maps)
You can register for access to our cancer survival analysis tools. Both strel and ewblft are copyright to the London School of Hygiene & Tropical Medicine. You will be agreeing to accept the terms of the licences, so please read them carefully. These programs are free, but you will also need a licence for Stata to use them.
After registration, the login for registered users gives you access to the tools (strel, ewblft, and life tables).
Bernard Rachet and Milena Falcaro
The computer program strel is designed for relative survival analysis. It is based on the maximum likelihood approach to survival estimation using individual tumour records (Estève et al., 1990). You will need a licence for Stata®, in which the program is written, but our program strel is free. It has been widely used for cancer survival analyses in the UK and many other countries. You can download the most recent version of strel once you have registered. To do this, go to the ‘Register for Access’ page, complete and submit the form, and a username and password will be sent to you.
Relative survival is the ratio of the observed cumulative probability of survival in the study group and the survival that would have been expected if the group had only been subject to the background mortality in the general population (obtained from life tables).
We have consolidated and extended our statistical software for the analysis of cancer survival data, strel. The new version, strel2, incorporates a multivariable functionality, enabling estimation of covariable-specific excess hazards of death in large population-based datasets. This simple tool is convenient for users without strong statistical skills who want to analyse large datasets. A paper has been published in the Stata Journal. The new features of the software were presented at a methodological workshop in Paris in March 2012.
References and further reading
Estève J., Benhamou E., Croasdale M. and Raymond L. (1990). Relative survival and the estimation of net survival: elements for further discussion. Statistics in Medicine 9, 529-538.
Coleman M., Babb P. and Damiecki P. (1999). Cancer Survival Trends in England and Wales, 1971-1995: Deprivation and NHS Region. TSO.
Camille Maringe, Bernard Rachet and Libby Ellis
A suite of life tables is available to registered users (go to ‘Login for Registered Users’ and enter your username and password). You will have access to the life tables we have constructed for England and Wales containing age-sex-mortality rates for five deprivation groups based on various indices of deprivation, by geographic region and calendar year or period. Life tables for Scotland, Northern Ireland and Ireland are also available.
Sub-national life tables have been smoothed either by applying Ewbank's 4-parameter model life table system to the observed mortality rates with the English Life Table 1991 as standard (archive), or by a Poisson model (published in 2009). You can perform the Ewbank procedure with your own data using our Stata® command ewblft, which you can also download free, once you have registered.
Graphical tools for performance indicators in cancer survival research: funnel plots and smoothed cancer survival maps
Manuela Quaresma and Bernard Rachet
Funnel plots were developed to enable comparison of institutional performance whilst showing the precision of each measure. We first used them to show variation between surgeons and over time in the proportion of women referred for radiotherapy after breast cancer, for a Panorama documentary broadcast in 2006. We also used them to show differences in the excess hazard of death from breast cancer among women managed by different surgeons.
We have developed funnel plots to improve the visualisation of geographical and temporal patterns in cancer survival for health policy-makers. A tutorial paper (in review) describes the use of these plots for various measures, such as relative survival and the excess hazard of death. We also set out new statistical formulations of the control limits for each of these measures.
We have also developed smoothed maps to improve the presentation and interpretation of the results for national strategic purposes, as well as for local management.
References and further reading
Spiegelhalter DJ. Funnel plots for comparing institutional performance. Stat Med 2005; 24: 1185-202
Coleman MP, Rachet B, Quaresma M, Lepage C, Baum M, Sikora K. Bradford NHS Trust and Panorama [webappendix report, 119pp]. Lancet 2006, 368: 730-731, last accessed 6 July 2009.
Rachet B, Maringe C, Nur U, Quaresma M, Shah A, Woods LM, Ellis L, Walters S, Forman D, Steward JA, Coleman MP. Population-based cancer survival trends in England and Wales up to 2007: an assessment of the NHS cancer plan for England. Lancet Oncol 2009; 10 : 351-69
Coleman MP, Rachet B, Quaresma M, Lepage C, Baum M, Sikora K. Bradford NHS Trust and Panorama. Lancet 2006; 368: 730-1
Coleman MP, Quaresma M, Berrino F, Lutz J-M, De Angelis R, Capocaccia R, Baili P, Rachet B, Gatta G, Hakulinen T, Micheli A, Sant M, Weir HK, Elwood JM, Tsukuma H, Koifman S, Azevedo e Silva G, Francisci S, Santaquilani M, Verdecchia A, Storm HH, Young JL, CONCORD Working Group. Cancer survival in five continents: a worldwide population-based study (CONCORD). Lancet Oncol 2008; 9: 730-56
Ito Y, Ioka A, Tsukuma H, Ajiki W, Sugimoto T, Rachet B, Coleman MP. Regional differences in population-based cancer survival between six prefectures in Japan: application of relative survival models with funnel plots. Cancer Sci 2009; 100: 1306-11