Net survival vs relative survival

Net cancer-specific survival and crude probability of death have two methods in which they can be estimated: using cause of death information or expected survival tables. When using cause of death information, there has been much debate over what is the right endpoint. If death certification were perfect, one would just use the specific form of cancer as the endpoint.

Many people want to know their chance of surviving after a diagnosis of cancer. Your doctor is the best person to ask. Prognostic and predictive factors are used to help develop a treatment plan and predict the outcome. A prognostic factor is a feature of the cancer like the size of the tumour or a characteristic of the person like their age that may affect the outcome. A predictive factor can help predict if a cancer will respond to a certain treatment. Some drugs only work if molecules such as proteins are on cancer cells or inside them.

Net survival vs relative survival

Federal government websites often end in. The site is secure. Survival statistics are of great interest to patients, clinicians, researchers, and policy makers. Although seemingly simple, survival can be confusing: there are many different survival measures with a plethora of names and statistical methods developed to answer different questions. This paper aims to describe and disseminate different survival measures and their interpretation in less technical language. In addition, we introduce templates to summarize cancer survival statistic organized by their specific purpose: research and policy versus prognosis and clinical decision making. Although a seemingly simple concept, survival can be confusing: there are many different survival measures with a plethora of names and statistical methods developed to answer different questions. Because most of the work has been published in technical journals, clinicians and members of the public may not appreciate the many cancer survival statistics available and how to interpret them. However, relative survival—also called net survival—represents the net effect of a cancer diagnosis, that is, the chances of surviving assuming that cancer is the only possible cause of death. This paper has two main objectives.

Instead, population-based survival refers to survival of all cancer patients diagnosed in a defined population area as opposed to survival of the usually highly selected and often unrepresentative cancer patients who participated in randomized trial. Verification of the cause of death in the trial of early detection of breast cancer.

Metrics details. Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias. We compare lifetable-based estimates Ederer II , a new unbiased method based on inverse probability of censoring weights Pohar Perme and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias. Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up 10 and 15 years.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Cause-specific and relative survival estimates differ. We aimed to examine these differences in common cancers where by possible identifying the most plausible sources of error in each estimate.

Net survival vs relative survival

Federal government websites often end in. The site is secure. Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias. We compare lifetable-based estimates Ederer II , a new unbiased method based on inverse probability of censoring weights Pohar Perme and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias. Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up 10 and 15 years.

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We aimed to examine these differences in common cancers where by possible identifying the most plausible sources of error in each estimate. All methods described below require these assumptions to be true. Table 2 Classification of cause of death on ten-year cause-specific survival a. There must therefore be other covariates which might account for the difference, bringing cause-specific and relative survival closer together. Additional information Competing interests The authors declare that they have no competing interests. Reprints and permissions. We have deliberately chosen two scenarios where there is substantial variation by age and, in practice, most cancer sites have far less variation. These patterns were observed within each age group investigated, except for breast and prostate cancer where relative survival was very slightly lower than cause-specific for the youngest age group 15—44 years. J Clin Epidemiol. See topics What is cancer? Table 3 shows the bias, MSE and coverage for scenario 1. Bias is difference in percentage points Full size table. The estimates are derived from subtraction of the population hazard of death from that in the cancer patients see Supplementary Information 1. For five-year survival, most of the methods provide very similar relative survival estimates. A prognostic factor is a feature of the cancer like the size of the tumour or a characteristic of the person like their age that may affect the outcome.

Federal government websites often end in. The site is secure.

The purpose of the simulation study is to quantify any bias in the methods in situations when one would expect the traditional non-parametric lifetable based methods to be biased, i. Flexible parametric models for relative survival, with application in coronary heart disease. Due to small sample size, CSS and RS were not calculated in children for cancer at the following sites: lung and bronchus, breast, and prostate. For colorectal cancer, this was driven by persons aged 65—74 years. Estimates of long-term survival for newly diagnosed cancer patients - a projection approach. Analysis of data: C. It illustrates three key issues, i the methods give different estimates of net survival, particularly for long term follow up; ii the Pohar-Pohar estimate is more variable than the other methods; iii the confidence intervals for the Pohar-Perme estimate are wider. The information is for your general use, so be sure to talk to a qualified healthcare professional before making medical decisions or if you have questions about your health. All authors read and approved the final manuscript. Health Rep. Ederer II standardized obtains age group specific estimates and uses internal age-standardization using Eq. Actual Prognosis Crude Probabilities : Survival Measures That Include Competing Causes of Death Cancer prognosis communicates the net effect of a cancer diagnosis: the chance of surviving assuming the cancer was the only possible cause of death. Also, statistics are based on numbers from several years ago and may not show the impact of recent advances in treating a certain cancer. Bright, C.