Period analysis for ‘up-to-date’ cancer survival data: theory, empirical evaluation, computational realisation and applications

doi:10.1016/j.ejca.2003.10.013

European Journal of Cancer

Volume 40, Issue 3, February 2004, Pages 326-335

https://doi.org/10.1016/j.ejca.2003.10.013 Get rights and content

Abstract

Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort-based types of analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are therefore often severely outdated at the time they become available. A couple of years ago, a new method of survival analysis, denoted period analysis, has been introduced to derive more ‘up-to-date’ estimates of long-term survival rates. We give a comprehensive review of the new methodology, its statistical background, empirical evaluation, computational realisation and applications. We conclude that period analysis is a powerful tool to provide more ‘up-to-date’ cancer survival rates. More widespread use by cancer registries should help to increase the use of cancer survival statistics for patients, clinicians, and public health authorities.

Introduction

Long-term survival rates are the most commonly used outcome measures for patients with cancer. They are widely used to monitor progress in cancer care over time, or to compare quality of cancer care between different populations (e.g. 1, 2, 3, 4). Furthermore, cancer survival statistics are increasingly accessible through the Internet to clinicians and cancer patients, and their knowledge has a strong impact on both clinicians’ management of the disease as well as patients’ coping strategies.

However, traditional long-term survival rates, which have been derived by cohort-based types of analysis 5, 6, 7, have essentially reflected the survival expectations of patients diagnosed many years ago. They have often been severely outdated at the time they became available as they failed to account for ongoing improvements in survival over time. A few years ago, a new method of survival analysis, denoted period analysis, has been introduced to derive more up-to-date estimates of long-term survival rates 8, 9. Meanwhile, this methodology has undergone extensive empirical evaluation 10, 11, 12, 13, 14, which showed that the method provides much more up-to-date estimates of long-term survival rates than traditional methods of survival analysis indeed. Furthermore, software has been developed which allows easy implementation of this new analytical tool for both absolute and relative survival rates [15]. The method is now applied to derive more up-to-date long-term survival rates in an increasing number of countries 16, 17, 18, 19, 20, 21, 22, 23, 24. These analyses suggest that long-term survival rates achieved by the end of the 20th century are much higher than previously suggested by traditional cohort-based analysis. For example, a recent period analysis of cancer patient survival in the United States [22] indicated that 20-year relative survival rates for all cancers combined are now approximately 51% rather than 40% as suggested by traditional cohort-based analysis (see Fig. 1). Even larger differences are seen for many common forms of cancer, such as breast cancer (65% versus 52%) or ovarian cancer (50% versus 35%).

In this review, a comprehensive presentation of the new methodology, its statistical background, empirical evaluation, computational realisation and applications is given. We thereby hope to expedite widespread availability of more up-to-date cancer survival statistics.

Section snippets

Theory

The methodological principle of period analysis, which has been described in detail by Brenner and Gefeller 8, 9 is very simple. In order to provide up-to-date estimates of long-term survival for some recent time period, all observations included in the analysis are left-truncated at the beginning of the period of interest in addition to being right censored at its end. This is illustrated in Fig. 2 for 5-, 10-, 15-, and 20-year survival estimates that might be obtained for a recent time

Empirical evaluation

Two major avenues have been followed for empirical evaluation of the performance of period analysis.

In one approach, it was evaluated how well survival estimates obtained by period analysis (compared with estimates obtained by traditional survival analysis) within some calendar period actually agree with the survival rates later observed for patients diagnosed with cancer in that period. This approach is of particular relevance in the clinical setting, where the prognosis of newly-diagnosed

Computational realisation

The previously available special software for relative survival analysis used by most cancer registries has not included options to perform period analysis 29, 30. Recently, easy to use SAS macros have been developed and made publicly available, by which both traditional analysis as well as period analysis of both absolute and relative survival rates can be performed. Two macro versions are currently available. They provide identical results for absolute survival rates, but they differ in the

Applications

In previous work, both cohort and complete analyses have been applied to derive population-based cancer survival rates. Pure cohort analysis has been very popular. For example, the previous report of the EUROCARE project, an international collaborative study of European cancer registries, has included cohort estimates of 5-year survival of patients diagnosed in 1985–1989 and followed until 1994 [2]. Pure forms of complete analysis have been rare (e.g. [34]), but ‘close to complete analysis’ has

Discussion

Since the first publication suggesting the use of period analysis for cancer patient survival appeared in the literature in 1996, a thorough empirical evaluation has disclosed that

•
this method provides more ‘up-to-date’ estimates of long-term cancer patient survival than traditional methods of survival analysis
•
period estimates of long-term cancer patient survival within some recent time period quite closely predict long-term survival rates observed later for patients diagnosed in that period
•

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ReviewPeriod analysis for ‘up-to-date’ cancer survival data: theory, empirical evaluation, computational realisation and applications

Abstract

Introduction

Section snippets

Theory

Empirical evaluation

Computational realisation

Applications

Discussion

J. Chron. Dis.

J. Clin. Epidemiol.

Eur. J. Cancer

Lancet

Eur. Urol.

Toward a comparison of survival in American and European cancer patients

Cancer

Childhood cancer survival in Europe and the United States

Cancer

Nonparametric estimation from incomplete observations

J. Am. Stat. Assoc.

The relative survival ratea statistical methodology

Natl. Cancer Inst. Monogr.

An alternative approach to monitoring cancer patient survival

Cancer

Up-to-date survival curves of patients with cancer by period analysis

J. Clin. Oncol.

Use of period analysis for providing more up-to-date estimates of long-term survival ratesempirical evaluation among 370 000 cancer patients in Finland

Int. J. Epidemiol.

Advanced detection of time trends in long-term cancer patient survivalexperience from 50 years of cancer registration in Finland

Am. J. Epidemiol.

Very long-term survival rates of patients with cancer

J. Clin. Oncol.

Up-to-date survival curves of children with cancer by period analysis

Br. J. Cancer

Recent improvement in survival of breast cancer patients in Saarland, Germany

Br. J. Cancer

Trends in survival of patients with ovarian cancer in Saarland, Germany, 1976–1995

J. Cancer Res. Clin. Oncol.

More up-to-date monitoring of long-term survival rates by cancer registriesan empirical example

Methods Inf. Med.

Long-term survival of children with leukemia achieved by the end of the second millenium

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Review
Period analysis for ‘up-to-date’ cancer survival data: theory, empirical evaluation, computational realisation and applications