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Analysis of Population-Based Cancer Survival Data
Published in Yingwei Peng, Binbing Yu, Cure Models, 2021
There are two types of measures for net survival: cause-specific survival and relative survival (Cronin and Feuer, 2000; Schaffar et al., 2015, 2017). Cause-specific survival is calculated by specifying the cause of death, where patients who die of causes other than cancer of interest are considered to be censored. Although detailed medical records may be available to ascertain the cause of death information in clinical trials, the cause of death in population-based survival data is primarily based the death certificate, thus, is often unreliable or not available. Therefore, cause-specific survival is much less appealing in population-based cancer survival analysis. The other measure of net survival, i.e., relative survival, is often used as a better alternative. Relative survival is calculated as the ratio of the observed overall survival of the cancer patients to the expected survival from the comparable cancer-free population. The expected survival is estimated from the general population life tables defined by sex, single calendar year and single year of age, for each country. Even though relative survival might not be directly relevant from a patient’s perspective, it is particularly useful for studying temporal trends in cancer patient survival and comparing populations where expected survival may vary, which is important from a public health perspective.
Real-World Evidence from Population-Based Cancer Registry Data
Published in Harry Yang, Binbing Yu, Real-World Evidence in Drug Development and Evaluation, 2021
If the information on cause of death is reliable or recorded accurately. Then cause-specific survival analysis can be used to estimate the survival rate of cancer patients. The cancer-specific survival function S(t) can be calculated by treating death due to other causes as censoring. However, in population-based cancer studies, cause of death may be either incorrectly identified or obtained from death certificates that are often inaccurately recorded [12]. For instance, it is not clear how to handle “autopsy only” cases and cases with unknown cause of death. As an alternative, relative survival [13] is often used as a measure of net survival (excess mortality) due to cancer under study. The relative survival is calculated as , where t is the survival time after the diagnosis of cancer; is the observed OS rate for the cancer patient group; and is the expected survival rate of a comparable group from the general population who are assumed to be practically free of the cancer of interest. The relative survival definition implies an additive hazards model, where the hazard of OS,
Relative Survival Analysis
Published in Atanu Bhattacharjee, Bayesian Approaches in Oncology Using R and OpenBUGS, 2020
The individual expected survival of a cohort could be obtained through Relative Survival. The individual expected survival is required to make toward comparison on age and sex composition. The R package relsurv is useful to obtain relative survival based on population table. The population contains expected death rates from the calendar year, age, and sex.
Lung cancer registries in Denmark, Finland, Norway and Sweden: a comparison and proposal for harmonization
Published in Acta Oncologica, 2023
A. Gouliaev, T. R. Rasmussen, N. Malila, L. Fjellbirkeland, L. Löfling, E. Jakobsen, S. O. Dalton, N. L. Christensen
The DLCR differs from the other national registries as does its purpose. It has a greater level of detailed clinical information including treatment complications registered by the treating clinicians (Table 1). Based on data from available registries, the DLCR and FCR reports the lowest 5-year survival (Table 2). DLCR reports actually observed overall survival based on yearly cohorts of patients and not relative survival or survival based on the period method for patient cohorts that have not yet reached the intended observation period, for example, 5 years as in the DCR [21], the FCR [28] and the NCR [33] which is why the reported survival rates for the more recent years cannot be directly compared when survival rates are steadily improving. Relative survival is defined as the ratio of the observed survival in the group of patients to the survival expected in a group of people in the general population, who are similar to the patients with respect to sex, age and calendar time at the time of diagnosis [46]. It can be interpreted as the probability of patient survival in the absence of other causes of death or as an estimation of cancer related death. Thus, when comparing survival rates between these countries, the Finnish and Norwegian will tend to be higher due to this adjustment. Comparative relative survival rates for all Nordic countries are reported in NORDCAN [2]; however, it has no information regarding stage distribution and stage specific survival, which limits the mutual learning potential.
Watch out for sticky diagnosis bias in older men with prostate cancer
Published in Scandinavian Journal of Urology, 2022
Oskar Bergengren, Marcus Westerberg
Additional evidence of sticky diagnosis bias has been found in studies with other designs. Substantial differences were observed between cause-specific survival estimates and relative survival estimates. Both methods measure net-survival, i.e. survival where one can only die of prostate cancer. Cause-specific survival is based on the classification of the underlying cause of death whereas relative survival is obtained by comparing the survival of men with prostate cancer to a disease-free comparable background population. In a recent nationwide population-based Swedish study, substantially higher relative survival estimates compared to cause-specific mortality were found for men with low-and intermediate-risk prostate cancer and for men over 80 [5]. A Norwegian study of similar design also found that these estimates differed, in particular for men above age 85 [6]. It is important to note that both cause-specific and relative survival have limitations: cause-specific survival is sensitive to misclassification of the cause of death and relative survival requires comparability between the two populations, which is challenging to obtain.
Trends in cancer survival in the Nordic countries 1990–2016: the NORDCAN survival studies
Published in Acta Oncologica, 2020
Frida E. Lundberg, Therese M.-L. Andersson, Mats Lambe, Gerda Engholm, Lina Steinrud Mørch, Tom Børge Johannesen, Anni Virtanen, David Pettersson, Elínborg J. Ólafsdóttir, Helgi Birgisson, Anna L. V. Johansson, Paul C. Lambert
We estimated marginal relative survival (RS) to quantify survival in the absence of death from other causes. We present 1-year and 5-year RS, and 5-year RS conditional on survival to 1-year post-diagnosis for women and men, across countries and calendar time. We adopted a modeling approach to estimate RS using flexible parametric RS models fitting separate models to each cancer site for each country. The models incorporated age at diagnosis, calendar year and sex (for relevant sites). After fitting the model, age-standardized estimates of RS were obtained using regression standardization stratified by calendar year and sex [9]. We used an adapted version of the International Cancer Survival Standard 1 (ICSS1) age-standard weights for all cancer sites by 10-year age groups, except for melanoma where the adapted ICSS2 weights were used (Supplementary Table 2).