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Cancer Patient Survival in Sweden - Predictions using period analysis

This is a short introduction to the article "Cancer patient survival in Sweden at the beginning of the third millennium - predictions using period analysis", which was published in Cancer Causes and Control in November 2004.

Introduction

Forty different forms of cancer, all sites combined, and all sites combined excluding breast and prostate cancer for females and males, respectively, have been analysed. For cancer of the small intestine, testis, and brain and nervous system, different histopathological groups within the same site were analysed separately. Some histopathological groups were excluded from the analyses due to low incidence and/or survival probabilities that differ from the predominant pattern for that particular site.

Tables for cumulative absolute (observed) and relative survival are presented for the period 2000-2002 and for cohorts of patients diagnosed in 1995-1997, 1990-1992, 1985-1987, and 1980-1982. The latter provide survival for the latest available 5-, 10-, 15-, and 20-year observed estimates, respectively. The survival estimates are presented for males and females as well as for both sexes combined, and for different age groups. Cumulative and interval-specific graphs are available for each combination of site, sex, and age group. The absolute survival is not included in the published article.

Tables and figures for

We recommend that the information on this web page in the first place is referred to by the article in Cancer Causes and Control, and to the web page only if the information is not included in the published article. However, be aware that the results will not be available on the web page indefinitely.

Recommended references:

Mats Talbäck, Måns Rosén, Magnus Stenbeck, Paul W. Dickman. Cancer patient survival in Sweden at the beginning of the third millennium - predictions using period analysis. Cancer Causes and Control 2004;15:967-976.

Cancer patient survival in Sweden 1980-2002. Centre for Epidemiology. National Board of Health and Welfare. Published at http://www.socialstyrelsen.se/english/statisticaldatabases/Sidor/cancersurvival-predictions2004.aspx  (Originally published December, 2004).

Background

Although the majority of the excess mortality due to cancer occurs during the first few years subsequent to diagnosis, excess mortality exists up to 20-years following diagnosis and even longer for some forms of cancer. It is therefore necessary to study both short-term and long-term survival in order to gain a complete picture of our progress in reducing cancer mortality. Traditional cohort-based estimates of, for example, 10-year survival are based on patients diagnosed during a period of at least 10 years. Long-term estimates of patient survival made using cohort-based methods can appear irrelevant to clinicians, their patients, and policy makers alike, since estimates are heavily influenced by patients diagnosed many years in the past who may have been treated with methods now considered obsolete. The time-lag between diagnosis and evaluation of survival can be reduced by applying period survival analysis, which was introduced into cancer survival analysis in 1996 (1). Period analysis has been shown to provide better predictions of survival for recently diagnosed patients and earlier detection of temporal survival trends than cohort-based analysis (2-5). Period analysis has previously been used in several countries to derive more up-to-date estimates of survival (6-9).

The aim of this study is to provide predictions of long-term survival for cancer patients recently diagnosed in Sweden. The predictions are made by period analysis and the latest estimates for cohorts with 5-, 10-, 15-, and 20-year survival are provided as a comparison.

Material and Methods

The Swedish Cancer Registry

Since 1958 every clinician, pathologist, and cytologist in Sweden is required by law to notify the Swedish Cancer Registry at the National Board of Health and Welfare of each new cancer diagnosed. The Swedish Cancer Registry is population based and covered in 2001 8.9 million people. From its inception the register has accumulated information on 1.8 million tumours for 1.6 million persons. The registry did not collect information on clinical stage until 2003 and does not register cases based on death certificates.

Patients

This study was based on cancer cases diagnosed in patients aged less than 90 years between 1980 and 2001. A total of 415,894 cancers in males and 403,092 cancers in females were included in the analysis. Ninety-seven percent of the tumours were histologically confirmed and an additional two percent were verified by X-ray, CT, NMR etc. Patients diagnosed incidentally at autopsy or without any information regarding follow-up were excluded from the analysis. Only the first primary cancer at each site was included in the analyses. Patients with multiple primary cancers diagnosed at different sites were included as independent entities. Patients with zero survival, but not formally registered as autopsy findings, were included.

The Cancer Register is linked annually by personal identification numbers to the Cause of Death Register, which is also maintained by the National Board of Health and Welfare, and to the Migration and Population registries at Statistics Sweden, to obtain dates of death or censoring and to confirm continued residency in Sweden. At the time of analysis the follow-up was completed up to and including 31 December 2002. Complete follow-up was available for 99.9% of the recorded cases.

Statistical analysis

Both cumulative and interval-specific absolute (observed) and relative survival were estimated using period analysis for the years 2000-2002 and cohort-based analysis for patients diagnosed in 1995-1997, 1990-1992, 1985-1987, and 1980-1982. The latter provide survival for the latest available 5-, 10-, 15-, and 20-year observed estimates, respectively (Figure 1). Relative survival is defined as the absolute survival among the cancer patients divided by the expected survival for a comparable group from the general population with respect to the main factors affecting survival, in this case, sex, age, and calendar year. The relative survival provides a measure of the excess mortality experienced by patients diagnosed with cancer, irrespective of whether mortality is directly or indirectly related to the cancer in question.

The calculations were performed with two publicly available SAS macros that can be used for both cohort and period analysis (10). One macro implements the Hakulinen method (11) and was used to estimate the cumulative survival (12). The other macro implements the Ederer II method (13) and was used to estimate the interval-specific survival (14). The latter macro was adapted to report interval-specific survival and both macros were updated to facilitate the use of annual population survival probabilities.

In period survival analysis only person-time at risk and events (death or censoring) occurring during one particular calendar period are considered. The estimates are obtained by left truncation of all observations at the beginning of the period and right censoring at the end of the period. Whereas cohort estimates represent the survival experience of a well-defined cohort of patients diagnosed during a specified calendar period, period estimates do not represent the survival of any real cohort of patients followed from diagnosis. Period estimates represent the survival that would be observed for a hypothetical cohort of patients who experienced the same interval-specific survival estimates of the patients who were actually at risk during the specified calendar period (2000-2002 in our study). If prognosis improves over time the period estimates are expected to be higher than those obtained by a corresponding cohort analysis. The opposite would be expected if survival was declining and no difference would be seen if survival was constant over time. Empirical studies comparing the two methods using historical data show that period estimates from a given time period in most cases predict, quite well, the long-term survival for cohorts of patients diagnosed during that particular period (2-5).

The cumulative relative survival ratio can be interpreted as the proportion of patients alive after a given time of follow-up in the hypothetical situation where the cancer in question is the only possible cause of death. An interval-specific relative survival of 100% indicates that, during this particular interval (year of follow-up), mortality in the patient group was equivalent to that of the general population. If this level is maintained during subsequent years of follow-up there is no longer evidence of an excess mortality due to cancer and the patients, as a group, can be considered “statistically cured“. The absolute survival proportion refers to the proportion of patients alive after a given time of follow-up and an interval-specific absolute survival of 100% indicates that none of the patients have died during the interval.

References

1) Brenner H, Gefeller O (1996) An alternative approach to monitoring cancer patient survival. Cancer 78: 2004-2010.

2) Talbäck M, Stenbeck M, Rosén M (2004). Up-to-date long-term survival of cancer patients - an evaluation of period analysis on Swedish Cancer Registry data. Eur J Cancer 40: 1361-1372.

3) Brenner H, Söderman B, Hakulinen T (2002) Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370 000 cancer patients in Finland. Int J Epidemiol 31: 456-462.

4) Brenner H, Hakulinen T (2002) Advanced detection of time trends in long-term cancer patient survival: Experience from 50 years of cancer registration in Finland. Am J Epidemiol 156: 566-577.

5) Brenner H, Hakulinen T (2002) Very long-term survival rates of patients with cancer. J Clin Oncol 20: 4405-4409.

6) Brenner H, Hakulinen T (2001) Long-term cancer patient survival achieved by the end of the 20th century: most up-to-date estimates from the nationwide Finnish cancer registry. Br J Cancer 85: 367-371.

7) Brenner H (2002) Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. The Lancet 360: 1131-1135.

8) Aareleid T, Brenner H (2002) Trends in cancer patient survival in Estonia before and after the transition from a Soviet republic to an open-market economy. Int J Cancer 102: 45-50.

9) Smith LK, Lambert PC, Jones DR (2003) Up-to-date estimates of long-term cancer survival in England and Wales. Br J Cancer 89: 74-76.

10) The macros can be downloaded from the statistical archive network at the University of Erlangen-Nürnberg http://www.imbe.med.uni-erlangen.de/issan/SAS/period/period.htm

11) Hakulinen T (1982) Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics 38: 933-942.

12) Brenner H, Hakulinen T, Gefeller O (2002) Computational realization of period analysis for monitoring cancer patient survival. Epidemiology 13: 611-612.

13) Ederer F, Heise H (1959) Instructions to IBM 650 programmers in processing survival computations. Methodological note No. 10. End Results Evaluation Section, National Cancer Institute, Bethesda MD.

14) Brenner H, Gefeller O, Hakulinen T (2002) A computer program for period analysis of cancer patient survival. Eur J Cancer 38: 690-695.