This report presents one-year and five-year age-standardised net survival estimates (see background note 3) for the 28 Cancer Networks of England, for patients who were diagnosed with a cancer of the oesophagus, stomach, colon, lung, breast (women) or cervix during 1996–2009 and followed up to 31 December 2010. One-year survival estimates are reported for patients diagnosed in 1996, 2001, 2005 and 2009, and five-year survival for those diagnosed in 1996, 2001 and 2005.
Further information about the cancer survival estimates published by the Office for National Statistics (ONS) can be found in the Summary Quality Report for cancer survival.
Summary quality reports are overview notes which pull together key qualitative information on the various dimensions of the quality of statistics as well as providing a summary of the methods used to compile the output. Information about key users of these statistics are also provided.
Danieli, C, Remontet, L, Bossard, N, Roche, L, and Belot, A Estimating net survival: the importance of allowing for informative censoring Stat Med (in press)
Royston P, Parmar MKB (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 21: 2175-2197
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Lambert PC, Royston P (2009) Further development of flexible parametric models for survival analysis. Stata Journal 9: 265-290
Statacorp (2011) STATA statistical software. College Station, TX: Stata Corporation
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Ellis L, Rachet B, Coleman MP (2007) Cancer survival indicators by Cancer Network: a methodological perspective. Health Stat Q Winter: 36-41
Ula Nur,a Michel P Coleman,a Emma Gordon,b Natalie Jakomis,b Chris Carriganc and Bernard Racheta
This report was produced by the Cancer Research UK Cancer Survival Group at London School of Hygiene and Tropical Medicinea (LSHTM), the National Cancer Registry at the Office for National Statisticsb (ONS) and the National Cancer Intelligence Networkc (NCIN).
The National Cancer Intelligence Centre at the Office for National Statistics, the National Cancer Intelligence Network and the London School of Hygiene and Tropical Medicine wish to thank the GIS Team at the West Midlands Intelligence Unit for providing the digital boundary data. They also acknowledge the work of the regional cancer registries in England, which provide the raw data for these analyses.
1. National cancer registration data for England were received from the National Cancer Registry at the Office for National Statistics (ONS), which collates cancer registrations submitted by regional cancer registries in England. The National Health Service Information Centre (NHS IC) updates these records with the registration of death or emigration. The data used in these analyses were extracted from the live database at ONS on 11 July 2011 and provided to the Cancer Research UK Cancer Survival Group at the London School of Hygiene and Tropical Medicine on 22 July 2011.
2. All adults (15–99 years) who were diagnosed with a first, invasive, primary, malignancy during the period 1996–2009 were eligible for inclusion in the analyses. We excluded patients who were diagnosed with a tumour that was benign (behaviour code 0), in situ (2) or of uncertain behaviour (1). Patients were excluded if their cancer was only registered from a death certificate. Patients with zero recorded survival time were included in the analyses with one day added to their survival. Table 2 (29 Kb Excel sheet) shows the number of patients excluded and Table 3 (25.5 Kb Excel sheet) shows the final number of patients in each Cancer Network who were included in the analyses.
3. Net survival in a population of cancer patients is their survival from the cancer of interest in the absence of other causes of death. It was estimated at one and five years after diagnosis for each cancer, sex and year of diagnosis. Net survival was estimated with an excess hazard model in which the all-cause mortality is modelled as the sum of the excess (cancer-related) mortality hazard and the expected (or background) mortality. The background mortality is defined by life tables from the general population. This approach enables population-level cancer survival to be estimated in the absence of detailed data on the cause of death. To obtain an unbiased estimation of net survival, age needs to be carefully modelled to account for the informative censoring associated with age (Danieli et al., 2011). We used flexible parametric models (Royston and Parmar, 2002) with age and year of diagnosis as main effects and an interaction between age and year of diagnosis. We also examined interactions between year and follow-up time and between age and follow-up time to deal with potential non-proportionality of the excess hazards over time since diagnosis. The Akaike Information Criterion (AIC) (Akaike, 1974) was used to select the best-fitting statistical model using the relative goodness of fit. The publicly available program, stpm2, was used to estimate net survival (Lambert and Royston, 2009). Analyses were performed in Stata 12 (Statacorp, 2011).
4. Life tables were constructed for the years 1996, 2001, and 2005 using the mid-year population estimates and the mean annual number of deaths in the three years centred on those index years (Cancer Research UK Cancer Survival Group 2004). Life tables for each year from 1996 to 2005 were created by linear interpolation. The life table for 2005 was used for 2006–09. Background mortality changes with time and varies by sex, age, socio-economic status and region, so life tables were created by single year of age, sex, Government Office Region (GOR) and deprivation quintile for each calendar year of death. National life tables were used for the very small number of patients with missing GOR, and regional life tables for those with missing deprivation category.
5. The age distribution of cancer patients at diagnosis changes with time and varies between Cancer Networks. Since survival also varies with age at diagnosis, robust summary comparisons of survival require control for these differences. The directly standardised overall survival figure for each cancer is a weighted average of the age-specific survival estimates, with standard weights taken from the proportionate distribution by age and sex of patients diagnosed in England and Wales during 1996–99. Age-standardisation requires a set of survival estimates for each age group. It is not always possible to obtain an estimate for each combination of cancer, age group, sex and calendar year of diagnosis in geographic units with small populations because of the limited number of cases. In this situation, the missing estimate was replaced by the equivalent age-specific estimate for England.
6. A 95 per cent confidence interval is a measure of the uncertainty around an estimate. It provides a range of values which contains the true population value with a 95 per cent level of confidence.
7. Cancer Networks were formed in 2001 following the recommendations of the Calman–Hine report (Expert Advisory Group on Cancer, 1995) and the NHS Cancer Plan (Department of Health, 2000). The most recent Cancer Network boundaries were applied to the whole period 1991–2006, enabling geographic trends to be charted over time (Figure 7). Cancer Network populations range from 0.6 to 3.0 million (2002 figures), making them more suitable for detailed statistical comparison of survival than smaller health geographies such as Primary Care Trusts. The role of Cancer Networks in the organisation of cancer services, and in improving regional equity in cancer management, makes them a meaningful unit with which to describe the geography of cancer survival in England (Ellis et al., 2007).
8. Cancers were defined by codes in the International Classification of Diseases, Tenth Revision (ICD-10) and International Classification of Diseases for Oncology, Second Edition (ICD-O-2). See Table 2 (29 Kb Excel sheet)
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