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Abstract: Background and Objective: Period analysis provides more up-to-date estimates of cancer patient survival than traditional methods, but there is a trade-off between up-to-dateness and precision. Our objective was to compare the performance of period and complete analysis in terms of up-to-dateness and precision of survival estimates. Study Design and Setting: Five-year relative survival data actually observed for patients diagnosed with 1 of 20 common forms of cancer in Finland in 36 overlapping 5-year periods between 1958–1962 and 1993–1997 were compared with period estimates and various variants of complete estimates of 5-year relative survival potentially available during these periods. Results: At comparable levels of up-to-dateness, survival estimates from period analysis were more precise than survival estimates from complete analysis. At comparable levels of precision, period analysis provided more up-to-date survival estimates than did complete analysis. Conclusion: These results further encourage more widespread use of period analysis as a standard tool for up-to-date monitoring of cancer patient survival by population-based cancer registries. [Copyright &y& Elsevier]

Relative survival rates are among the most commonly reported outcome measures of cancer patients. They are calculated as ratios of observed survival rates and the expected survival rates in the absence of cancer. Standard errors of relative survival rates are commonly calculated by dividing the standard error for absolute survival rates by the expected survival, without taking possible random variation of the latter into account. The aim of this study was to empirically assess the validity of these commonly reported standard errors. Using data from the nationwide Finnish Cancer Registry, the authors calculated 5- and 10-year absolute, expected, and relative survival rates for patients with 25 common forms of cancer in Finland in 1989. The authors used bootstrap analysis to empirically assess the random error of absolute and relative survival rates and then compared the results with conventionally derived estimates of standard errors. The conventional and bootstrap standard errors were closely similar for all estimates of absolute survival. By contrast, the conventional estimates of standard errors of 5- and 10-year relative survival exceeded the bootstrap estimates by up to 17% and 32%, respectively. The authors conclude that conventional derivation may substantially overestimate standard errors for relative survival. [ABSTRACT FROM PUBLISHER]

Timely monitoring of trends in long-term patient survival is an important task of cancer registries. Recently, a new method, denoted period analysis, has been proposed to enhance up-to-date monitoring of survival. The authors assessed the use of period analysis for advanced detection of time trends in long-term cancer patient survival based on data from the nationwide Finnish Cancer Registry by comparing estimates of 10-, 15-, and 20-year relative survival rates obtained by period analysis and by traditional (cohort) analysis of survival at various points of time between 1953 and 1997. Time trends are graphically displayed for the 15 most common forms of cancer. Long-term survival rates strongly improved over time for most forms of cancer. The slope and shape of trend curves obtained by period analysis are very similar to those obtained by traditional survival analysis. However, detection of progress in 10-, 15-, and 20-year survival rates of newly diagnosed patients could have been advanced by 5–10 years, 10–15 years, and 15–20 years, respectively, with the use of period analysis rather than traditional cohort survival analysis. The authors conclude that period analysis should be routinely used to advance detection of progress in long-term cancer patient survival. [ABSTRACT FROM PUBLISHER]

Background: Previous studies have reported lower colorectal cancer (CRC) survival in patients with low compared to high educational levels. We investigated the impact of education on CRC survival by using both individual and area-based information on education., Methods: Patients diagnosed with CRC in Finland in 2007-2016 were followed up for death until the end of 2016. Age-standardized relative survival and relative excess risk of death (RER) were estimated by sex using period approach. RERs were adjusted for age, stage at diagnosis, cancer site, urbanity, hospital district and municipality by using Bayesian piecewise constant excess hazard models. Analyses were conducted including individual (basic, secondary, high) and area-based (quartiles Q1-Q4 based on the proportion of population with basic education) education separately as well as both measures in one model., Results: We analysed in all 24 462 CRC patients. There was a clear gradient in 5-year relative survival across education groups (men: basic 62 %, secondary 64 %, high 69 %; women: basic 61 %, secondary 67 %, high 71 %). Compared to the basic education group, RER in the high education group was significantly lower. This association was still present after including area-based education in the models (men: RER 0.72, 95 % Confidence interval (CI) 0.64-0.81; women: RER 0.76, 95 % CI 0.59-0.96). Area-based education revealed smaller effect estimates than individual education in CRC survival and no association for men., Conclusion: Individual education information should be preferred over area-based when survival differences are studied by education. Educational differences in CRC survival are still present in Finland., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

International comparison of population-based cancer survival is a key component of monitoring progress against cancer. Its validity depends to an unknown degree on completeness of ascertainment of deaths in the cancer registries involved which may vary according to legal and administrative circumstances. The aim of this study was to assess the impact of incomplete registration of deaths through various mechanisms on the validity of long-term absolute and relative survival estimates. For that purpose, we simulated underascertainment of deaths through linkage failure of registry data with death certificates with probabilities between 0.1 and 5%, and underascertainment of deaths by unregistered annual emigration with probabilities between 0.05 and 2%, using data from the Finnish Cancer Registry. The expected impact on estimates of 5-, 10- and 15-year absolute and relative survival was assessed. We demonstrate that even modest levels of under-registration of deaths may lead to severe overestimation of long-term survival estimates, ranging from 0 to 31 percent units in the scenarios assessed. In general, relative survival is much more affected than absolute survival, and potential problems are much larger for relative survival estimates in older compared with younger patients. Potential overestimation strongly increases with length of follow-up, and this increase is particularly pronounced for under-registration of deaths because of unrecorded emigration. Every effort should be made in cancer registry based survival analyses to ascertain deaths with close to 100% completeness. When such completeness cannot be achieved, long-term relative survival estimates and their comparison across populations must be interpreted with much caution., (Copyright 2009 UICC.)

Since its introduction in 1996, period analysis has been shown to be useful for deriving more up-to-date cancer survival estimates, and the method is now increasingly used for that purpose in national and international cancer survival studies. However, period analysis, like other commonly employed methods, is just a special case from a broad class of design options in the analysis of cancer survival data. Here, we explore a broader range of design options, including 2 model-based approaches, for deriving up-to-date estimates of 5- and 10-year relative survival for patients diagnosed in the most recent 5-year interval for which data are available. The performance of the various designs is evaluated empirically for 20 common forms of cancer using more than 50-year long time series of data from the Finnish Cancer Registry. Period analysis as well as the 2 model-based approaches, one using a "cohort-type model" and another using a "period-type model", all performed better than traditional cohort or complete analysis. Compared with "standard period analysis", the cohort-type model further increased up-to-dateness of survival estimates, whereas the period-type model increased their precision. While our analysis confirms advantages of period analysis over traditional methods in terms of up-to-dateness of cancer survival data, further improvements are possible by flexible use of model-based approaches.

Recently, 2 modeling strategies have been proposed and shown to be useful to increase precision of up-to-date cancer survival estimates and to predict cancer patient survival: modeled period analysis and modeled cohort analysis. We aimed to compare the performance of both types of modeling for providing up-to-date and precise cancer survival estimates. Data from the nationwide Finnish Cancer Registry were used to assess how well both approaches would have been able to predict 5-year relative survival of concurrently diagnosed patients if they had been applied for that purpose throughout the past decades. Analyses were carried out for 20 common forms of cancer. For each cancer, 5-year relative survival was modeled with either approach for each single calendar year from 1962 to 1997. Mean differences and mean squared differences from 5-year relative survival later observed for patients diagnosed in the 5-year period around those calendar years were calculated. Survival estimates obtained by period modeling had much lower standard errors than those obtained by cohort modeling. Furthermore, for a clear majority of cancers, period modeling on average also provided better prediction of 5-year relative survival than cohort modeling. We conclude that, although both modeling strategies have their merits and specific indications, period modeling of survival has distinct advantages for up-to-date and precise estimation of cancer survival in population-based cancer survival studies., ((c) 2007 Wiley-Liss, Inc.)

Period analysis has been shown to provide more up-to-date estimates of cancer survival than traditional methods of survival analysis. There is, however, a tradeoff between up-to-dateness and precision of period survival estimates: increasing up-to-dateness by restricting the analysis to a relatively short period, such as the most recent calendar year, goes along with loss of precision. Recently, a model-based approach was proposed, in which more precise period survival estimates for the most recent year can be obtained through modeling of survival trends within a recent 5-year period. We assess possibilities to extend the time window used for modeling to come up with even more precise, but equally accurate and up-to-date estimates of prognosis. Empirical evaluation using data from the Finnish Cancer Registry shows that extension of the time window to about 10 years provides, in most cases, as accurate results as using a 5-year time window (whereas further extension may lead to considerably less accurate results in some cases). Using 10-year time windows for modeling, SEs of survival estimates can be approximately halved compared with conventional period survival estimates for the most recent calendar year. Furthermore, we present a modification of the modeling approach, which allows extension to 10-year time windows to be achieved without the need to include additional cohorts of patients diagnosed longer time ago and which provides similarly accurate survival estimates at comparable levels of precision in most cases. Our analyses indicate opportunities to further maximize benefits of model-based period analysis of cancer survival.

In many cancer registries, registration of new cases is somewhat less up-to-date than mortality follow-up. In such situations, hybrid analysis, a combination of cohort and period analysis, rather than 'pure' period analysis has been proposed to derive up-to-date survival estimates. We evaluate application and adaptation of a modelling strategy that has recently been introduced to enhance precision of period survival estimates, to 'hybrid type of data'. Using data from the Finnish Cancer Registry, we show that modelling again strongly increases precision of survival estimates. Furthermore, special models adapted to the hybrid type of data are shown to provide even more precise and, in a clear majority of cases, also more valid predictions of survival of recently diagnosed patients than models ignoring the hybrid type of data. Finally, we show that model-based estimation of and testing for recent trends may give different answers if period rather than hybrid modelling is used for hybrid type of registry data. We conclude that modelling is useful for both hybrid and period analyses of cancer survival, but the different data structure needs to be taken into account in the set-up of models.

Monitoring of progress in cancer patient survival by cancer registries should be as up-to-date as possible. Period analysis has been shown to provide more up-to-date survival estimates than do traditional methods of survival analysis. However, there is a trade-off between up-to-dateness and the precision of period estimates, in that increasing the up-to-dateness of survival estimates by restricting the analysis to a relatively short, recent time period, such as the most recent calendar year for which cancer registry data are available, goes along with a loss of precision. The authors propose a model-based approach to maximize the up-to-dateness of period estimates at minimal loss of precision. The approach is illustrated for monitoring of 5-year relative survival of patients diagnosed with one of 20 common forms of cancer in Finland between 1953 and 2002 by use of data from the nationwide Finnish Cancer Registry. It is shown that the model-based approach provides survival estimates that are as up-to-date as the most up-to-date conventional period estimates and at the same time much more precise than the latter. The modeling approach may further enhance the use of period analysis for deriving up-to-date cancer survival rates.

Purpose: Breast cancer diagnosed before the age of 50 years has become a common disease in many developed countries. Although average remaining life expectancy in the affected age groups is usually several decades, data regarding survival perspectives beyond 10 to 20 years after diagnosis are sparse. The aim of this study was to assess long-term survival in a large population-based sample of patients diagnosed with breast cancer before age 50 years., Patients and Methods: Relative survival within up to 40 years after diagnosis was assessed for cohorts of women diagnosed with breast cancer before age 50 years and notified to the nationwide Finnish Cancer Registry within various time intervals since 1953. In addition, up-to-date estimates of 40-year relative survival were obtained by exclusively looking at the survival experience of breast cancer patients in recent years (1993 to 1999) using period analysis, a new method of survival analysis., Results: Prognosis of patients diagnosed with breast cancer before age 50 years has considerably improved during the past decades. According to the latest estimates from period analysis, cumulative 40-year relative survival is now approximately 43% for all cancers combined, 57% for localized cancers, and 24% for cancers with regional tumor spread. Nevertheless, patients diagnosed with breast cancer before age 50 years continue to have increased mortality throughout at least four decades after diagnosis. This applies even if breast cancer is diagnosed in a localized stage and in the absence of a second primary breast cancer., Conclusion: Despite major improvement in prognosis over time, breast cancer occurring among patients who are younger than 50 years remains a chronic disease that affects prognosis for decades.

Relative survival rates, such as 5- or 10-year relative survival rates, which quantify "net survival" of cancer patients, are the most commonly reported measures of cancer outcome by cancer registries. Because relative survival rates vary with age for many forms of cancer, and because the age distribution of cancer patients varies between different populations or within one population over time, age adjustment of relative survival rates is often employed in international comparisons or in time series analyses of cancer patient survival. In this article, we show that derivation of crude and of age-adjusted relative survival rates in the traditional way is conceptually inconsistent, and that this inconsistency has important practical implications. We show ways to overcome this inconsistency in the derivation and interpretation of crude and age-adjusted relative survival rates.

Background: Providing up-to-date estimates of cancer patient survival rates is an important task of cancer registries. A few years ago, a new method of survival analysis, denoted period analysis, was proposed to enhance the recency of long-term survival estimates. The aim of this paper is to provide a comprehensive empirical evaluation of the use of this method., Methods: Using data from the nationwide Finnish Cancer Registry, we compare 5-year and 10-year relative survival rates of 371 849 patients diagnosed with one of the 16 most common forms of cancer in Finland at various time intervals between 1953 and 1992 with the most up-to-date estimates of 5-year or 10-year relative survival that might have been obtained in those time intervals by traditional methods of survival analysis and by period analysis of survival., Results: Survival rates strongly increased over time for most forms of cancer. For these cancers, traditional estimates of 5- and 10-year survival rates would have severely lagged behind the survival rates later observed for newly diagnosed patients, and period analysis would consistently have provided much more up-to-date estimates of survival rates., Conclusions: We conclude that period analysis should be implemented as a standard tool for providing up-to-date estimates of long-term survival rates by cancer registries.