Survival rates for cancer

DEFINITION: Survival rate is a statistic that states the percentage of people with a disease who live a specified length of time (one year, five years, or ten years) after diagnosis.

ALSO KNOWN AS: Mortality rates

Types of survival rates: The overall or observed survival rate is the percentage of people diagnosed with a disease who are alive after a specified period, often five years. Overall survival rates do not distinguish between different causes of death—deaths from cancer and car accidents are both included. The net survival rate filters out deaths from causes other than the disease of interest by calculating relative or cancer-specific survival rates. The relative survival rate compares the overall survival rate of a group after diagnosis of a disease to the survival rate of a similar (age- and sex-matched) group without the disease. The cancer-specific survival rate includes only deaths by cancer, usually determined using death certificates.

Overall and relative survival rates do not always give a clear picture. For example, there is no way to tell whether cancer survivors are still undergoing treatment at five years or if they have gone into remission. Two other types of survival rates can give more information—progression-free and disease-free survival. The progression-free survival rate is the percentage of people who survive without their cancer spreading, including people who have had partial treatment success; their diseases are not in remission, but the tumors are not growing. Disease-free survival represents the proportion of patients who achieve remission or are completely cancer-free during a specified period.

One of the newer methods of calculating survival is period survival, which uses only the most recent information available. This method more effectively accounts for newer treatment regimens and modalities than other methods. However, restricting the analysis to a relatively short, recent period, such as the most recent calendar year for which cancer registry data are available, results in a loss of precision in the estimate.

Although surviving is the opposite of dying, mortality rates are not always the inverse of survival rates. The mortality rate is often defined as the proportion of the entire population that died from a disease in a specified period, often reported as deaths per 1,000 or 100,000 (for example, 89 per 100,000 people rather than 0.089 percent). Population-based mortality rates do not help measure advances in treating cancers because they reflect changes in incidence. For example, death rates from lung cancer have declined since 1990, but that is more related to the reduction in tobacco use since that time than to any improvement in treatments for lung cancer. However, when the mortality rate is defined as the proportion of people diagnosed with a disease who died from that disease during a specific period, then its value is simply the inverse of the survival rate.

How survival rates are used: Survival rates are important in determining prognosis, or the prediction of the course of the disease. Survival rate estimates are used in health services research to study trends, such as the effect of a new therapy or the relationship of survival to characteristics such as socioeconomic status or stage at diagnosis. Survival statistics are also important in clinical research to measure cancer treatments' efficacy. On an individual patient level, survival rates can give patients a better understanding of their disease and help them evaluate different treatment plans in consultation with their medical providers.

How survival rates are calculated and reported: Cancer survival rates are usually calculated based on observations of hundreds or thousands of people, often using cancer registries or other databases. Survival rates can be reported in simple percentages, or they may be reported using life tables, survival curves, or the Kaplan-Meier estimator. Life tables show the actual observed survival over time, and although they are one of the oldest techniques in biostatistics, they are no longer seen much outside government reports. Survival curves are graphical presentations of the probabilities for surviving different lengths of time, often derived from life tables. In the actuarial method, they present the probabilities of surviving to set specific intervals, such as one year or month.

The Kaplan-Meier estimator also graphically represents survival probabilities, but unlike the actuarial survival curve, the Kaplan-Meier method (named for its originators, Edward Kaplan and Paul Meier) identifies the exact point in time when each death occurred and uses those points as the intervals. Considering all available information, the goal is to produce the most accurate survival curve possible. The Kaplan-Meier method allows researchers to analyze complex data, such as data from a study in which patients have been diagnosed or started treatment at different times and have varying lengths of follow-up. This method is so widely used and well-known that the literature often refers to survival curves as Kaplan-Meier curves.

Limitations of survival rate statistics: Statistics can be both accurate in general and completely wrong in particular. Survival rates are no different from other statistics in this regard. Statistics cannot predict the course of an individual’s disease. Because statistics are based on many different people and every person is different, they cannot give the exact chances of remission or progression in a particular case. Even when survival rates can be calculated for smaller groups—even very specific groups, such as fifty-five- to sixty-four-year-old White women in Texas with stage 2 breast cancer—the differences in genetics, body type, lifestyle, and socioeconomic factors between people in that subpopulation mean that the survival rates can vary considerably. It is always important to remember that survival rates represent an average and cannot reflect a person’s experience. Because of this limitation, many cancer patients choose to disregard survival rate statistics.

Additionally, survival rates usually do not reflect the latest treatment options. Because there is a lag between when cancer patients die and when this information is reported, the data are usually at least several years old. This means the effects of new treatments are not seen in the statistics for several years after the treatment becomes commonplace.

Another limitation of survival rates has to do with improvements in screening. When screening is widespread, a higher five-year survival rate may be observed not because people live longer but only because an earlier diagnosis has been made.

Survival rate trends: The five-year survival rate is commonly used to report progress in the so-called war against cancer. Experts disagree as to whether cancer patients are living longer than cancer patients did in the past. Although survival rates have increased for many cancers since the 1970s, this could be because of many factors or combinations of factors, including improvements in surgical treatment or chemotherapy, changes in definitions of diseases, diagnoses of cases that would have been undetectable in years past, improvements in access to care, and earlier diagnoses through advances in screening.

Many cases are diagnosed earlier than they would have been, even in the late 1990s, because of improvements in imaging technology, advancements in genetic screening, and increasing awareness among patients and physicians. However, earlier diagnosis does not necessarily imply longer survival. If one person is diagnosed when symptoms appear in 2015 and survives until 2017, while another is diagnosed while still asymptomatic in 2013 and survives until 2017, the survival statistics will seem to be improved even though both people died at the same time. From the point of view of the cancer patient, there may even be negative consequences of earlier diagnosis. The patient’s emotional well-being may worsen, or there may be longer exposure to the side effects of treatment.

Survival rates by cancer site and stage: The most up-to-date survival rates for different cancers are available through the National Cancer Institute (NCI). According to the NCI’s Surveillance, Epidemiology, and End Results Program (SEER), the general five-year relative survival rate was 69.2 percent, from data recorded between 2014 and 2020. The highest relative five-year survival rates typically occur in ductal carcinoma in situ (also known as stage 0 breast cancer), testicular cancer, and prostate cancer. The lowest survival rates occur in lung, esophageal, and pancreatic cancer.

Survival rates can vary greatly based on how early the cancer is diagnosed. For example, for patients diagnosed with stage 1 (localized) lung or bronchus cancer in the United States, the five-year relative survival rate for 2007–13 was 55.6 percent, while for those with stage 4 (distant) cancer, the rate was only 4.5 percent. Only 16 percent of lung cancers were diagnosed at stage 1 during the same period, and the overall relative survival rate was very low, at just 18.1 percent. Even so, it is unlikely that the survival rate would increase significantly if all cases of lung cancer were diagnosed at stage 1 or 2 since treatment options are limited and the disease spreads rapidly. Such is not the case for cancers that can be cured with surgery if caught early, such as melanoma. Conversely, prostate cancer has a high survival rate because it is generally a very slow form of cancer that is typically diagnosed late in life, and most men with prostate cancer will die of other causes before cancer can spread.

Survival rates in different populations: Survival rates can vary greatly between different groups of people. For instance, women have better relative survival rates than men, both overall and in most of the major types of cancer, although these differences are generally not large. In addition, age at diagnosis has been shown to influence survival rate, since older people are less likely to be treated aggressively. This may be related to their perceived or actual lowered capability of withstanding severe side effects from treatment, or it may be an example of age-related bias on the part of medical providers.

Compared with Hispanic and non-Hispanic Whites, American Indians, and Asian Americans, Black Americans have poorer cancer survival rates both overall and for nearly every type of cancer. This is because they are more likely to be diagnosed with advanced cancer as a result of disparities in access to and receipt of healthcare services. Some studies suggest that most racial and ethnic disparities in survival rates are socioeconomic disparities and that when non-White people receive cancer treatment and medical care similar to that received by White individuals, their survival rates are similar. This is supported by research indicating that higher income predicts better survival.

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