Descriptive cancer epidemiology using cancer registry data An … · 2019. 8. 23. · Descriptive...
Transcript of Descriptive cancer epidemiology using cancer registry data An … · 2019. 8. 23. · Descriptive...
ANCR 2019 – Pre-symposium Workshop II:
Descriptive cancer epidemiology using cancer registry data
An overview of measures
Department of Medical Epidemiology and Biostatistics
Karolinska Institutet, Sweden
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Anna Johansson
Therese Andersson
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Aim of workshop
• To give an overview of concepts and terminology used within
descriptive cancer epidemiology
• The workshop will cover statistical measures used in cancer reporting,
such as incidence, mortality and survival, with a focus on survival
measures.
• The workshop aims to convey an understanding of commonly used measures on a conceptual
level.
• Traditional measures as well as more recently developed measures will be discussed.
• We will use examples from Nordic national cancer data to highlight concepts and terminology
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70% of those who
get cancer survives
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Format of workshop
• 13:00-14:30
• Part I: Incidence, prevalence, mortality, survival – traditional measures (Anna Johansson)
• Exercise in pairs/groups
• 14:30-14:50 Coffee break
• 14:50-16:30
• Part II: Survival measures – recent developments (Therese Andersson)
• Discussion
• Summing up of exercise
• We encourage active discussion and questions!
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Part I: Outline – traditional measures (Anna Johansson)
• The setting: Descriptive cancer epidemiology
• Basic concepts
• Time and place
• Traditional measures of descriptive cancer epidemiology
• Incidence
• Prevalence
• Mortality
• Survival (Overall survival, Net survival, Cause-specific survival, Relative survival)
• Summary
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The setting: Descriptive epidemiology
• Epidemiology is usually defined as ”the study of the distribution of disease in the population
over time and place”
• Descriptive epidemiology aims to ”describe and monitor disease patterns in the population”
• Time trends
• Geographical comparisons
• Ecological studies (correlation studies)
• Population-based cancer registry data are an essential component of descriptive cancer epidemiology
• Cover populations (persons) in geographically defined areas (place) over time (age, year)
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The setting: Descriptive epidemiology – is not clear-cut
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• Terminology is not always clear.
• The term ”descriptive” has different meanings.
• Epidemiology is in its essence ”descriptive and
observational”
• The measures presented today can be used
not only in ”descriptive epidemiology”, but in all
epidemiology
Descriptive”demographic”
exposure info only on group level
Analyticalindividual exposure info
to form groups
- Time trends- Geographical studies- Ecological studies
Descriptions
- Risk factor studies
Hypothesis testing
Epidemiology
Observational Experimental- Intervention studies
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The setting: Population-based cancer registry data
• Population-based cancer registry data are an essential component of descriptive cancer epidemiology
• Cover populations (persons) in geographically defined areas (place) over time (age, year)
• Population-based cancer registry data
• Collected systematically and routinely by health professionals and organisations
• Well-defined target population in a catchment area (e.g. country) over well-defined time period
• Clear inclusion/exclusion criteria
• Prospectively collected, high coverage, high completeness
• In contrast, non-population-based data
• Unclear underlying target population and catchment area
• Unclear which population the results can be generalised to
• E.g. hospital-based studies, single/multi-center studies, specific cohorts
• To describe incidence and survival in the population, we require population-based data7/59
The setting: Cancer reports and statistics are descriptive epidemiology
• National Cancer Reports
• Clinical Cancer Registry Reports
• Reports using cancer registry data
• NORDCAN (ANCR)
http://www-dep.iarc.fr/NORDCAN/English/frame.asp
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The setting: Cancer reports and statistics are descriptive epidemiology
• Cancer Incidence in 5 Continents (IARC)
• GLOBOCAN (IARC), international version of
NORDCAN; online Global Cancer Observatory
http://gco.iarc.fr/
• Large international studies on survival
• CONCORD
• EUROCARE
• ICBP
• SEER database
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Basic concepts: Time and place
• Cancer incidence (occurrence) and survival often varies
by time
• Cancer incidence increases with age
• Cancer incidence increases over calendar time
• Time is important in descriptive epidemiology, and
trends in cancer incidence and survival are usually
described by
• Age
• Calendar time (year)
• Important to monitor and assess what factors underlies
changes across period or age
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Lägg in figur på incidence över year
Rate/100,000
Rate/100,000 (ASW)
Source: NORDCAN
Basic concepts: Time and place
• Cancer incidence and survival also varies by geographical region (place)
• This indicates that cancer (and cancer death) is, to some extent, preventable
• By eliminating factors associated with geographical variation
• We need to consider
• Differences in lifestyle & environmental risk factors between populations
• Differences in genetics/ethnicity
• Differences in reporting, detection and classification of cancer
• Differences in healthcare, organisation and treatment
• Nordic countries are very similar, but also differences exist
• Think of such differences during today’s workshop!
11Source: NORDCAN /59
Traditional measures
• In descriptive cancer epidemiology we want to quantify/describe
• Occurrence of cancer (incidence, prevalence)
• Death of cancer (mortality, survival)
• Incidence, prevalence and mortality are defined entities within the general population
• Survival is the survival experience of patients and can be estimated with a range of different
measures (overall term: ”cancer patient survival”)
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Population x xCancer Cancer
• Survival• Incidence• Prevalence• Mortality /59
Incidence
• Incidence is a measure of the occurrence of (or risk of developing) a disease
• Among healthy individuals (disease-free), i.e. persons who yet do not have the disease
• In descriptive cancer epidemiology we usually define two measures:
• Incidence is the number (N) of persons diagnosed with cancer in a given time period
• Expressed as ”Number of new cases of cancer per year”
• Incidence rate is the number of persons diagnosed with cancer over population at risk in a
given time period, i.e. incidence (N) divided by population at risk
• Expressed as ”Number of new cases of cancer per 100,000 persons per year” (per 100,000 persyrs)
• ”Incidence” is sometimes used interchangeably for ”incidence” and ”incidence rate”
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Incidence
• Numbers of cancer (incidence, N)
• Absolute (actual size) burden of cancer
• Useful for care planning – how many patients will
need diagnosis and treatment each year
• Incidence depends on
• Risk of disease (the higher risk, the more cases)
o Presence of risk factors (lifestyle, genetics)
• Size of popul. (the larger popul., the more cases)
o Population sizes in Den, Fin, Ice, Nor, Swe?
• Is the occurrence different between countries?
• Are the numbers different?
• Is the risk different?
o Hard to say, we need to adjust for population size.
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Source: NORDCAN
N cases
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Incidence rate
• To make incidence numbers comparable
between countries and/or over time we need
to adjust for population size
• We divide numbers by population at risk each
year, which gives rates
• Incidence rates
• Incidence (N) divided by population size
• Reported as new cases per 100,000 per year
• This is also called ”crude rate”, since it is not
adjusted for any other factor
15Source: NORDCAN
Rate/100,000
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Incidence rate
• Time trends in incidence rates are used to
monitor cancer occurrence
• Incidence rates often change over period
• Reflects changes in risk factors over time
• Adjusted for varying population size over time
• Age effects play an important role
• Cancer risk (often) increases with age
• If ageing population over time, then more
persons will get cancer due to age
• Hence, incidence rates (and incidence)
depend on
• Age distribution of population
• Age-specific risks
16Source: NORDCAN
Rate/100,000
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Incidence rate (age-standardised)
• Age standardisation is a common way to adjust for
age
• Age-standardised rates (ASR) are weighted rates
according to a specific age distribution (the
standard)
• Each year, the crude rate is re-weighted so it
represents the rate ”had the cancer cases had the
age distribution of the standard”
• [Technical: The ASR is a weighted mean of age-specific rates
weighted by the standard age distribution within a year]
• The ASRs are comparable
• Over period, across countries
• Age differences between countries or over calendar
years do not explain the difference in ASRs
17Source: NORDCAN
Rate/100,000
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Incidence rate (age-standardised)
• Different age standards (age distributions) are used
for cancer incidence
• In NORDCAN
• World standard
• European standard
• Nordic standard
• Age-standardised rates are not ”real”, the ASRs are
”hypothetical” (if the population had had the same age
distribution as the standard)
• If you want the true rate in a country and in a year, then
report the crude rate
• ASRs are only used for age-adjusted comparisons
• Discussion: Why standardise only for age? More
factors?
• Age is the strongest risk factor for cancer18
Source: NORDCAN
Incidence rate (age-standardised)
• Different age standards will yield different ASRs
• The ASRs will depend on the age-specific crude
rates in the country
• The Nordic standard is older than the European
and World standards, hence more weight will be
given to older ages where the colon cancer rate is
high, and the weighted rate will thus be higher
19Source: NORDCAN
Rate/100,000
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Incidence rates
• Are the patterns in colon cancer
incidence between Nordic
countries similar before/after
age standardisation?
20Source: NORDCAN
Rate/100,000 Rate/100,000
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Issues with measuring incidence
• Incidence should only include primary cancer (not recurrence or regional/distant metastasis)
• Primary = where the cancer first grows in your body
• Secondary = when a primary cancer spreads to another area of your body (distant metastases)
• Multiple primary cancers at same site (i.e. persons with two new cancers at same site, at same
or different times) need to be handled
• Various rules (algorithms) for how to count multiple primaries exist
• IARC rules (NORDCAN uses IARC rules)
• SEER rules
• Important if you want to compare rates between countries
• Cancers at different sites should be counted
• If a person has two primary cancers at different sites, then both will be counted in the incidence as
two cases
• Incidence counts ”cases” of cancer, i.e. a person may contribute with more than one cancer per year
for different site-specific incidences 21/59
Issues with measuring incidence
• Incidence date (or diagnosis date) must be well-defined
• Different rules for how to define the date of diagnosis
• Different cancer registries may use slightly different definitions; check documentations
• In Cancer Incidence in 5 Continents (IARC publication), three available algorithms:
• IARC (Jensen et al., 1991)
• European Network of Cancer Registries (ENCR) (Pheby et al., 1997)
• SEER Program (Johnson et al., 2007)
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Issues with measuring incidence
• Incidence should be measured in population free of cancer (disease-free population)
• Incidence rates are reported as cases per 100,000 persons per year
• How many persons are at risk over a year?
• We usually calculate the population at risk as the mid-point population per year (in 1-year age groups)
from official population statistics
• Population can include persons who have cancer (usually a small fraction)
• Discussion: When may this not be the case, i.e. when is it a problem that the population contains
many cancer cases? Must the population at risk be disease-free?
• Anyone alive in the population is ”at risk” of having cancer, also if you previously have had cancer. So
ok to include persons with previous cancers in the at-risk-population. (If you study risk of first cancer,
then the at-risk-population should not include persons with previous cancer.)
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Prevalence
• Prevalence is a measure of the number of persons living with cancer at a given point in time
(a.k.a. point prevalence)
• Expressed as: How many persons were living with a cancer (or cured cancer) in a given year
• Prevalence proportion is the proportion of the population living with cancer at a given point in
time (i.e. prevalence divided by population)
• Expressed as: The percentage of the population living with a cancer (or cured cancer) in a given year
• In NORDCAN, prevalence for a given year is defined as the number of persons with cancer and
alive at the end of that year
• Prevalence includes all persons with the diagnosis, i.e. those on treatment, those who have
finished treatment, those cured, yet still alive
• An incident case is considered prevalent until death
• Prevalence will therefor also include cases which were diagnosed a long time ago24/59
Prevalence
• Prevalence is a measure of burden of disease
• Used to quantify and monitor current and longterm cancer burden
• Healthcare for cancer patients and cancer survivors
• Survivorship issues, longterm effects of cancer and cancer treatment
• Cure?
• Prevalence is useful for chronic diseases, i.e. you cannot be cured; cancer is sometimes considered a
chronic disease, which increases risks for other diseases throughout life
• However, often cancer is considered cured within 5 years of diagnosis.
• Partial prevalence: Restricted to patients diagnosed within up to 5 (or 10) years
• Partial prevalence may represent a better measure of current burden (currently ill persons) than total
prevalence (ever ill persons).
• Discussion:
• Is prevalence relevant for cancers with high or low survival (or cure)?
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The relationship between incidence, prevalence and survival
• Prevalence (i.e. persons living with a cancer) depends on the number who get cancer
(incidence) and for how long patients survive (survival)
• High incidence and high survival, gives a high prevalence
• Prevalence is the ”product of incidence and duration of disease” (if stable, constant trends)
• If incidence and/or survival vary across time,
then so will prevalence
• Prevalence can be age-standardised to enable
comparisons over time and countries
• Same age standards as for incidence (World, European, Nordic)
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Prevalence
• In end of 2016, approx. 260-450
persons/100,000 have colon cancer
• 0.26-0.45%
• Women have higher prevalence,
what could be the reason?
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0
50
100
150
200
250
300
350
400
450
500
Den Fin Ice Nor Swe
Prevalence proportion (per 100,000)Colon, 0-85+, end of 2016
Men Women
Source: NORDCAN/59
Mortality
• Mortality is the number of persons who die of cancer in a given time period
• Expressed as: Number of deaths due to cancer per year
• Mortality rate is the number of persons who die of cancer over population at risk in a given time
period
• Expressed as: Number of deaths per 100,000 per year (per 100,000 persyrs)
• Diagnosed when?
• At any time (any year) prior to death
• Mortality in a given year may thus reflect incidence from a long time ago
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Mortality
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• Mortality is (nearly) always lower than
incidence
• For incidence: ”Year” is year at diagnosis
• For mortality: ”Year” is year at death
• Hence, different patients in the incidence
and mortality curves in a given year
• Why is the gap between incidence and
mortality widening?
Source: Cancer in Finland, 2013 /59
Mortality
• Mortality versus survival
• In descriptive cancer epidemiology, the term ”mortality” denotes the ”number of persons who die from
cancer among the total population in a given time period”.
• Survival denotes ”the number of persons who die (or survive) from cancer among those with cancer in
a given time period”
• That is, mortality has ”population” as the denominator, while survival has ”patients” as the
denominator.
• Mortality is thus a measure of burden of cancer death in the population
• This measure is useful to assess the impact of cancer death in the population
• A common cancer may have a larger cancer death burden despite high survival, while a rare cancer
will have a lower cancer death burden regardless of (higher or lower) survival.
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Mortality
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The relationship between incidence, mortality and survival
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Source: Dickman et al, 2006 Journal of Internal Medicine, Review
• Mortality is directly related to incidence,
since the persons dying from cancer in the
population is a subset of those who got the
disease.
• Mortality also depends on the survival
among patients, since a lower survival will
give a larger number of persons dying from
cancer.
• Hence, (simplified) high incidence and low
survival, gives high mortality.
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Incidence and mortality in Nordic Cancer Reports
• Included in all reports
• For all sites
• Shows longterm trends and
patterns
33Source: Cancer in Norway 2017 /59
Overview so far
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Incidence
Incidence = Ni number new cancer cases
Incidence rate = Ni/population and year
Prevalence
Prevalence = Np number alive with cancer
Prevalence prop = Np/population at end of year
Mortality
Mortality = Nd number deaths of cancer
Mortality rate = Nd /population and year
Survival
Among cancer patients
Overall survival (OS)
Net survival
Cause-specific survival (CSS)
Relative survival (RS)
IN POPULATION IN PATIENTS
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Survival
• In descriptive cancer epidemiology, survival refers to the occurrence of death (or duration of life)
among cancer patients
• Cancer patient survival can be measured in several ways
• Overall survival, OS (time from diagnosis to death of any cause)
• Cause-specific survival or cancer-specific survival, CSS (time from diagnosis to death of cancer)
• Relative survival, RS (something completely different altogether…)
• More terminology (and confusion…)
• Net survival
• Crude survival (see Part II)
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Survival varies by time
• In survival, time has an additional, important component: time-since-diagnosis
• Survival is highly dependent on time-since-diagnosis
• Cancer deaths often occur within 5 years of diagnosis, with some important exceptions (e.g. breast
cancer)
• Age and period are important too
• Age at diagnosis
• Year at diagnosis
• They can also (in some situations)
refer to
• Age at death
• Year at death
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Survival
• Survival is usually described by two main measures
• Survival proportion (which is often referred to as ”survival”*) is the proportion of patients still
alive at time t, denoted S(t)
• Mortality rate is the rate of dying at time t, denoted h(t), i.e. number of deaths divided by
persons (person-time) at risk at time t. This is the mortality rate among patients.
• From an intuitive point of view, survival and death are two sides of the same coin (”if you are
alive, then you are not dead”)
• At 5 years after diagnosis: 70% patients are alive (meaning 30% of patients are dead)
• However, the mortality rate is not a proportion, and the mathematical relationship between S(t)
and h(t) is a bit more complex S(t)=exp(-0𝑡ℎ 𝑥 𝑑𝑥)
• A proportion is confined to [0,1] while a rate can be any positive value ≥037
* Confusion! The overall area is called ”survival” and a specific measure is called ” survival”
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Survival
• Overall survival proportion (OS) is the proportion of patients still alive at certain point in time
after diagnosis.
• Often presented as a curve over time, or in table at 1, 5 or 10 years after diagnosis.
• Time to death due to any cause (all-cause survival, all-cause mortality)
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Estimation methods:
• Kaplan-Meier
• Actuarial method/Life table
• Model-based estimation
Alive
Dead
At 5 years after
diagnosis the proportion
surviving is around 70%
5-year survival is 70%
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Survival: Meaning of mortality
• In the ”patient survival” context, the term ”mortality rate” refers to the mortality rate among
patients.
• In contrast, in ”descriptive cancer epidemiology” context (”incidence, mortality, survival”),
”mortality” referred to the mortality rate among population – this sometimes causes confusion!
• Be aware of the use of term ”mortality”, always check which meaning is being used by checking
the denominator (among patients or among population).
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Survival: Net survival
• A person can only die once, and will die from one main cause
• Cancer patients will likely die from their cancer
• Cancer patients will also die of other causes than their cancer
• Net survival
• If we want to assess how well we are performing with cancer treatment and care, by comparing
survival across time and countries, then we want to remove death due to other causes, and
specifically look at cancer death only
• This is called net survival
• Net survival ignores other-cause mortality (which may vary over time and place)
• Net survival is defined as the probability of surviving if it was impossible to die from anything else,
so a patient will eventually die from the cancer if followed long enough
• Net survival estimates are not corresponding to the true survival in the patient group, where patients
may die from other causes
• Net survival is the most commonly used survival entity in cancer reports and in scientific
papers. Often not even mentioned as ”net survival” but as ”survival”.
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Survival: Net survival
• There are two main ways to estimate net survival
• Cause-specific survival, CSS
• Relative survival, RS
• Cause-specific survival and relative survival are fundamentally different in how they estimate
net survival
• CSS use cause of death information
• RS does not use cause of death information
• Most cancer reports present relative survival
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Survival: Cause-specific survival
• Cause-specific survival (CSS) uses information on cause of death for each patient (from a
cause of death registry or similar)
• Cause-specific survival proportion (cause-specific survival) is the proportion of patients not yet
dead of cancer at certain point in time after diagnosis
• Limitation: CSS relies on correct death certificate classification (if cause of death information is
incorrect, then CSS will be biased)
• Cancer patients tend to more often have ”cancer” written as cause of death, also when they die from
other causes
• If patient has many other diseases/comorbidities, e.g. older ages
• If cancer or cancer treatment gives rise to another disease/condition, which kills the patient
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Survival: Cause-specific survival
• Cause-specific survival assumes you cannot die from other causes
• Hypothetical situation where you either are alive or dead from cancer
43
Alive
Dead from cancer
At 5 years after
diagnosis the proportion
surviving due to cancer
is around 78%
5-year cancer-specific
survival is 78%
/59
Survival: Relative survival
• The second way to estimate net survival is relative survival.
• Relative survival uses the all-cause mortality rate in the patient group and compares it to the all-
cause mortality rate in the general population
• The difference in all-cause mortality between the patients and the general population is
considered to be due to cancer
• This is called excess mortality
• Having cancer (i.e. being a patient) is associated with an excess mortality that we estimate from
comparing patients to the general population
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Survival: Relative survival – a non-mathematical explanation
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Assumption is that we can capture non-cancer deaths in cancer patients correctly with the non-cancer
deaths in the general population.
KEY: We use expected mortality rates from general population to remove non-cancer deaths from the
cancer patients’ all-cause mortality to get cancer-associated deaths in the cancer patients.
Remember: We don’t use cause of death
information, so we don’t know what is a
cancer/non-cancer death.
Non-cancer deaths
Non-cancer deaths
Cancer associated
deaths
Cancer patients
Difference=excess (deaths assigned to having cancer), λ(t)
All-cause deathsh(t)
All-cause deathsh*(t)
General population
Excess mortalityλ(t) = h(t) – h*(t)
orh(t) = λ(t) + h*(t)
/59
Relative survival
• Survival and death are two sides of the same coin.
• Excess mortality corresponds to the relative survival ratio (RSR), which is often referred to as
relative survival (RS) (to increase the confusion!*)
• The RSR is the all-cause survival proportion (OS) among the patients divided by the all-cause
survival proportion (OS) among the general population
RS = RSR = 𝑎𝑙𝑙−𝑐𝑎𝑢𝑠𝑒 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 𝑝𝑟𝑜𝑝𝑜𝑡𝑖𝑜𝑛 𝑎𝑚𝑜𝑛𝑔 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠
𝑎𝑙𝑙−𝑐𝑎𝑢𝑠𝑒 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑎𝑚𝑜𝑛𝑔 𝑔𝑒𝑛𝑒𝑟𝑎𝑙 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
• RSR gives the proportion of all-cause survival that is due to the cancer patients, i.e. the survival
proportion due to cancer, or net survival…. Hmmm.
• This is not easy to understand, and requires some thought
• It is a rather backward way to get to cancer survival via the all-cause mortality, by comparing patients
to general population46
* Both the framework is called ”relative survival” and a specific measure is called ”relative survival”
/59
Relative survival
• The RSR is the ratio between the all-cause
survival curve in patients (red) divided by
the all-cause expected survival curve
(green) in a comparable general population
• Both RSR and cause-specific survival
measures net survival
• RSR is however not a proportion, it is a ratio
of two survival proportions
• It can (in rare situations) be >1 (if the
expected rates are not correct, and cancer
patients are healthier than the background
population)
47/59
Relative survival
• Relative survival is often reported at 1 year, 5 year and 10 year after diagnosis
48
Among men with regional stage pharynx cancer, 5-y RS has improved from 23.8%
(1978-1982) to 60.3% (2013-2017), assuming you cannot die from anything else!
/59
Relative survival (age-standardised)
• Age-standardisation of relative survival is important for comparisons between calendar periods
and countries, when age distributions vary over countries/time
• Age-standardisation includes weighting relative survival by the same age distribution (the
standard age distribution).
• Cancer patients are different than the general population, hence different age standards are
used for survival than for incidence (mortality)
• International Cancer Survival Standard (ICSS) – see next slide
• The weighted (age-standardised) relative survival is not the true relative survival, and has no
direct meaning for the population it refers to. It can only be used for comparisons to other
populations (or across time).
49/59
Relative survival (age-standardised)
• Age standards for relative survival
• ICSS 1 - For cancer sites with
increasing incidence by age (most
cancer sites)
• ICSS 2 - For cancer sites with
broadly constant incidence by age
• ICSS 3 - For cancer sites that
mainly affect young adults
50
Source: https://seer.cancer.gov/stdpopulations/survival.htmlCorazziari et al. Eur J Cancer 2004. /59
Relative survival (age-standardised)
• The ICSS standards are usually applied in broader
age groups
51
Source: Corazziari et al, Eur J Cancer 2004
/59
Issues with relative survival
• Because we use all-cause survival, we do not require information on cause of death among
the patients
• RS will capture not only deaths reported as ”cancer deaths” but also increased mortality from
other causes which may be increased due to cancer (i.e. treatment-related or ”late effect”
deaths occuring after treatment)
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Issues with relative survival
• Background (expected) mortality rates are obtained from official national statistics.
• The all-cause mortality rates in patients is compared to all-cause mortality rates in the general
population in categories of similar age, sex and calendar period as the patients
• When we estimate RS, we use expected mortality rates in the general population in 1 year age
groups and calendar year groups, and by sex
• A strong assumption in RS is that the expected mortality rates in these categories of age, period
and sex, are representative of what the cancer patient group would have had had they NOT
had cancer
• See next slide for more details
53/59
Issues with relative survival
• When might the background rates not be representative for the patients?
1. For smoking-related cancers. Smokers are more likely to die of other causes (disease related to
smoking). Hence, the patients of smoking-related cancers are generally sicker than the general
population. In such situations, the excess mortality would be higher than in reality, because the sicker
patient group is compared to a healthier population group.
• A way around this problem is to create expected mortality rates not only by age, year and sex, but
also by smoking status (smokers/non-smokers), and estimate excess mortality within these finer
groups. However, expected mortality rates from the general population are typically not available by
smoking, as they rely on official national statistics. (Blakely et al, Int J Cancer 2012)
2. If the background rates come from the general population including cancer cases, i.e. the general
population is not cancer-free. This may occur for common cancers in older age groups, e.g. breast and
prostate cancer. The assumption is that the background population should be free of the cancer under
study, not free of any other cancer.
• There are methods to adjust for such mix of cancers among general population (Talbäck et al. Eur J
Cancer 2011)
54/59
Part I: Summary – traditional measures
• The setting: Descriptive cancer epidemiology
• Basic concepts
• Time and place
• Traditional measures of descriptive cancer epidemiology
• Incidence
• Prevalence
• Mortality
• Survival (Overall survival, Net survival, Cause-specific survival, Relative survival)
• Age-standardisation
• Exercise55/59
• Pattern Recognition Exercise – interpret trends of incidence, prevalence, mortality and survival
• 18 graphs of time trends – connect statements with graphs
• ”which graph represents decreasing survival”
• Work in pairs or small groups
• Discuss!
• There is sometimes more than one correct answer
• Work until the coffee break
• We will discuss some questions (not all) in full group
at the end of today
• Solutions will be provided
Exercise in groups
56/59
References
• NORDCAN:
• Danckert B, Ferlay J, Engholm G , Hansen HL, Johannesen TB, Khan S, Køtlum JE, Ólafsdóttir E, Schmidt LKH,
Virtanen A and Storm HH. NORDCAN: Cancer Incidence, Mortality, Prevalence and Survival in the Nordic Countries,
Version 8.2 (26.03.2019). Association of the Nordic Cancer Registries. Danish Cancer Society. Available from
http://www.ancr.nu, accessed on 15/08/2019.
• Engholm G, Ferlay J, Christensen N, Bray F, Gjerstorff ML, Klint A, Køtlum JE, Olafsdóttir E, Pukkala E, Storm HH
(2010). NORDCAN-a Nordic tool for cancer information, planning, quality control and research. Acta Oncol. 2010
Jun;49(5):725-36.
• Be aware that there will be minor differences between the national cancer reports of each Nordic country and what is
used in NORDCAN. Read about it in "The NORDCAN database"
57/59
References
• Blakely et al. Bias in relative survival methods when using incorrect life-tables: Lung and bladder cancer by smoking status and ethnicity
in New Zealand. Int. J. Cancer: 131, E974–E982 (2012)
• Corazziari I, Quinn M, Capocaccia R. Standard cancer patient population for age standardising survival ratios. Eur J Cancer. 2004
Oct;40(15):2307-16.
• Dickman PW, Adami HO. Interpreting trends in cancer patient survival. J Intern Med. 2006 Aug;260(2):103-17.
• Ellis L, Belot A, Rachet B, Coleman MP. The Mortality-to-Incidence Ratio Is Not a Valid Proxy for Cancer Survival. J Glob Oncol. 2019
May;5:1-9. doi: 10.1200/JGO.19.00038.
• Ellis L, Woods LM, Estève J, Eloranta S, Coleman MP, Rachet B. Cancer incidence, survival and mortality: explaining the concepts. Int J
Cancer. 2014 Oct 15;135(8):1774-82. doi: 10.1002/ijc.28990. Epub 2014 Jun 19.
• Estéve J, Benhamou E, Raymond L (1994). Descriptive Epidemiology, Statistical methods in cancer research, volume IV, IARC
Scientific Publication No. 128. Lyon: IARC Press.
• Jensen OM, Parkin DM, MacLennan R, Muir CS, Skeet RG, editors (1991). Cancer Registration: Principles and Methods. IARC Scientific
Publication No. 95. Lyon: IARC Press.
• Johnson CH, Peace S, Adamo P, Fritz A, Percy-Laurry A, Edwards BK (2007). The 2007 Multiple Primary and Histology Coding Rules.
Bethesda (MD): National Cancer Institute, Surveillance, Epidemiology and End Results Program. Available from:
http://seer.cancer.gov/tools/mphrules/, accessed 20 November 2014.
• Parkin DM & Bray F. Descriptive Studies pp 187-258, Handbook of Epidemiology, 2nd ed (Edited by Wolfgang Ahrens & Iris
Pigeot), Springer
• Pheby D, Martínez C, Roumagnac M, Schouten LJ (1997). Recommendations for Coding Incidence Date. Ispra, Italy: European Network
of Cancer Registries.
• Talbäck M, Dickman PW. Estimating expected survival probabilities for relative survival analysis--exploring the impact of including cancer
patient mortality in the calculations. Eur J Cancer. 2011 Nov;47(17):2626-3258
Acknowledgements
• Paul Dickman, Karolinska Institutet, Sweden
• Gerda Engholm, Danish Cancer Society
• Paul Lambert, University of Leicester, UK
• Mark Rutherford, University of Leicester, UK
• Max Parkin, University of Oxford, UK, and Freddie Bray, IARC for graciously allowing us to use
and adapt their exercise on Pattern Recognition
59/59
ANCR, Stockholm 2019
Descriptive cancer epidemiology usingcancer registry data – an overview ofmeasures. Part II
LINE
Therese M-L Andersson
LINE
Department of Medical Epidemiology and Biostatistics,Karolinska Institutet, Stockholm
Acknowledgements: Paul Dickman, Paul Lambert, Hannah Bower,Caroline Weibull, Mark Rutherford & Sandra Eloranta
Outline
Relative survival
approaches for estimationage-standardisation
Statistical cure
Crude and net survival
Loss in expectation of life
Period analysis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 2 / 53
Relative Survival
Relative Survival = Observed Survival
Expected Survival
R(t) = S(t)/S∗(t)
Cancer patient survival is often measured using relativesurvival, and summarised by the 1- 5- and/or 10-year RSR.
Estimate of mortality associated with a disease withoutrequiring information on cause of death.
ExcessMortality Rate
=Observed
Mortality Rate–
ExpectedMortality Rate
λ(t) = h(t) − h∗(t)
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 3 / 53
Relative survival
All-cause mortality for males with colon cancer and generalpopulation
0
10000
20000
30000
40000
50000
60000
70000
80000
Mor
talit
y R
ate
(per
100
,000
per
son
year
s)
20 40 60 80 100Age
General populationColon cancer patients
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 4 / 53
Relative survival
Assumptions needed to interpret relative survival as a measure ofnet survival
The time to death from the cancer in question is conditionallyindependent of the time to death from other causes (are thereany factors that influence both cancer and non-cancermortality other than those factors that have been controlledfor in the analysis?).
The mortality from other causes, or expected survival, can becorrectly estimated for the group of cancer patients (wouldthey have the same mortality as the general population hadthey not been diagnosed with cancer?).
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 5 / 53
Relative survival
Approaches to estimating relative (expected) survival
Three widely used methods for estimating expected survivalfor groups of patients are commonly known as the Ederer I,Ederer II, and Hakulinen methods.
An alternative is the The Pohar Perme approach, which doesnot explicitly estimate expected survival. It estimates netsurvival in a relative survival framework without estimatingobserved and expected survival.
A model-based approach, where the excess mortality ismodelled and estimated using the log-likelihood, can also beused.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 6 / 53
Relative Survival, example
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10
0 2 4 6 8 10 0 2 4 6 8 10
1973−1979 1980−1986 1987−1993
1994−2000 2001−2008
< 50 50−59 60−69 70−79 > 79
Rel
ativ
e S
urvi
val R
atio
Time Since Diagnosis (Years)
Published in: Success Story of Targeted Therapy in Chronic Myeloid Leukemia: A Population-Based Study ofPatients Diagnosed in Sweden From 1973 to 2008. Bjorkholm M, Ohm L, Eloranta S, Derolf A, Hultcrantz M,Sjoberg J, Andersson T, Hoglund M, Richter J, Landgren O, Kristinsson SY, and Dickman PW. J Clin Oncol. 2011.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 7 / 53
Age-standardisation of relative survival
Standardisation is very common in epidemiology to try andensure comparisons are fair, we should compare“like-with-like”.
When we compare incidence, mortality or survival betweendifferent populations we always need to think about adjustingfor age differences.
If the age distribution varies between the groups beingcompared, then we are likely to have confounding by age.
Thus we usually use age-standardised estimates whenpresenting incidence and mortality. The same ideas carry overto (relative) survival
When we standardize, we summarise survival in a populationby a single number. For example, relative survival at 5 years.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 8 / 53
Age-standardisation of relative survival
0.560.530.51
0.47
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Rel
ativ
e Su
rviv
al
0 1 2 3 4 5Years since diagnosis
<4545-5960-7475+
An age standardized estimate is a weighted average of theseage group specific curves.
We will use different weights depending on whether we useinternal or external standardization.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 9 / 53
Age-standardisation of relative survival
Internal Age standardisation
If one is interested in a summary estimate of relative survivalfor a population and not concerned with comparisons betweenpopulations then internal age-standardisation can beperformed.
Note this is not the same as ignoring age completely (seelater).
External Age standardisation
If one is just interested in comparisons between populationsthen external age-standardisation should be performed.
We force a common age distribution on the two populations.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 10 / 53
Age-standardisation of relative survival
Relative survival is estimated separately in each of S agegroups.
The age standardised estimate is a weighted average of therelative survival in each age group, Rj(t).
Rs(t) =S
∑j=1
wjRj(t)
The weights, wj are based on the age distribution within thestudy population (for internal age standardisation).
It is important to realise that there may be huge variation inrelative survival between age groups, but this can be ‘lost’when only presenting age standardised estimates.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 11 / 53
Age-standardisation of relative survival
Why not just ignore age?
It is sometimes questioned why we need to internallyage-standardise when interested in the population as a whole,i.e. why not just analyse all ages as one group?
However, older people are more likely to be lost to follow-updue to a higher mortality rate due to other causes.
The problem relates to the fact that we are interested in themarginal survival function (hypothetical world), but only havereal world data with informative censoring.
In other words, older people often have worse relative survivaland are more likely to be censored (due to deaths from othercauses).
Analysis by age group removes most (but not all) of thisproblem.
Still some theoetical bias with Ederer II within age groups, butthis will be extremely small.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 12 / 53
Age-standardisation of relative survival
For external age standardisation we just change the weights.
In doing this we are forcing a different age distribution ontoour study population to that they actually have.
This means that we are estimating survival in a hypotheticalworld where you can only die of the cancer under study and ifthe population had a different age distribution to what theyactually have!
We should be very cautious about putting a real worldinterpretation on this and remember that we are standardisingin order to make fair comparisons.
The analyst has the choice of what age distribution to use.This could be
An agreed standard age distribution (See part I)The age distribution in a particular calendar period whencomparing survival between different calendar periods.The age distribution in a particular subgroup.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 13 / 53
Summary so far
The concept of age-standardization is fairly simple - it is justa weighted average of different relative survival estimates.
For life tables, we would recommend traditional agestandardisation (both internal and external) using Ederer II.Will be slightly biased, however the bias is small as long asage-standardisation is performed.
Pohar-Perme method is a further approach and now is oftenthe method of choice (issue with longer term estimates),doesn’t require internal age-standardisation however externalneeded if aim is comparison across groups.
Can also use a model-based approach.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 14 / 53
Statistical cure
Medical cure occurs when all signs of cancer have beenremoved in a patient; this is an individual-level definition ofcure.
Population or statistical cure occurs when mortality amongpatients returns to the same level as the general population.
Equivalently the excess mortality rate approaches zero.
This is a population-level definition of cure.
When the excess mortality reaches (and stays) at zero, therelative survival curve is seen to reach a plateau.
Cure models can be used to estimate the cure proprotion andthe survival of the uncured. The median survival time ofuncured is often reported as a summary measure of thesurvival of uncured.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 15 / 53
Statistical Cure
When the mortality rate observed in the patients eventually returnsto the same level as that in the general population
0.0
0.2
0.4
0.6
0.8
1.0
Rel
ativ
e S
urvi
val
0 2 4 6 8 10Years from Diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 16 / 53
Statistical Cure
Cancer of the stomach in Finland 1985–1994 Interval-specificrelative survival
Cancer of the stomachr
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
Annual follow-up interval
1 2 3 4 5 6 7 8 9 10
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 17 / 53
Statistical Cure
Cancer of the breast in Finland 1985–1994 Interval-specific relativesurvival
Cancer of the breastr
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
Annual follow-up interval
1 2 3 4 5 6 7 8 9 10
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 18 / 53
Statistical CureS
urvi
val o
f Unc
ured
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure
(a)
Sur
viva
l of U
ncur
ed
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure
(a)
(b)
Sur
viva
l of U
ncur
ed
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure
(a)
(b)
(c)
Sur
viva
l of U
ncur
ed
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure
(a)
(b)
(c)
(d)Sur
viva
l of U
ncur
ed
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure
(a)
(b)
(c)
(d)Sur
viva
l of U
ncur
ed
Cure FractionAdapted from Verdeccia (1998)
(a) GeneralImprovement
(b) SelectiveImprovement
(c) Improved palliativecare or lead time
(d) Inclusion ofsubjects with noexcess risk
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 19 / 53
Statistical Cure, example
0.5
11.
52
2.5
Med
ian
Sur
viva
l Tim
e
0.2
.4.6
.8
Cur
e P
ropo
rtio
n
1970 1980 1990 2000Year of Diagnosis
Aged 19−40 at diagnosis
0.5
11.
52
2.5
Med
ian
Sur
viva
l Tim
e
0.2
.4.6
.8
Cur
e P
ropo
rtio
n
1970 1980 1990 2000Year of Diagnosis
Aged 41−60 at diagnosis
0.5
11.
52
2.5
Med
ian
Sur
viva
l Tim
e
0.2
.4.6
.8
Cur
e P
ropo
rtio
n
1970 1980 1990 2000Year of Diagnosis
Aged 61−70 at diagnosis
0.5
11.
52
2.5
Med
ian
Sur
viva
l Tim
e
0.2
.4.6
.8
Cur
e P
ropo
rtio
n1970 1980 1990 2000
Year of Diagnosis
Aged 71−80 at diagnosis
Cure proportion Median survival time 95% CI
Published in: Temporal trends in the proportion cured among adults diagnosed with acute myeloid leukaemia inSweden 1973-2001, a population-based study. Andersson TM, Lambert PC, Derolf AR, Kristinsson SY, Eloranta S,Landgren O, Bjorkholm M, Dickman PW. Br J Haematol. 2010.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 20 / 53
Crude (and net) survival
In cancer patient survival we typically choose to eliminate thecompeting events.
That is, we aim to estimate net survival (usually in a relativesurvival framework).
It is important to recognise that net survival is interpreted in ahypothetical world where competing risks are assumed to beeliminated.
It is useful as a measure of cancer-patient survival that isindependent of background mortality so we can makecomparisons across time, across different age-groups in ourpopulation and across different countries.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 21 / 53
Crude (and net) survival
Patient survival is the most important single measure of theeffectiveness of health systems in managing cancer patientsand is of interest to clinicians, patients, researchers,politicians, health administrators, and public healthprofessionals.
However, relatively little attention has been paid to the factthat each of these consumers of survival statistics havedifferent needs.
Net survival is useful for comparing populations, but notnecessarily relevant to individual patients.
We do not live in a hypothetical world where cancer is theonly possible cause of death.
Estimates of the proportion of patients who will die of cancerin the presence of competing risks can also be made (crudeprobabilities of death).
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 22 / 53
Crude (and net) survival
Net probabilityof death
due to cancer=
Probability of death in ahypothetical world where the
cancer under study is the onlypossible cause of death
Crude probabilityof death
due to cancer=
Probability of death in thereal world where you may die
of other causes before thecancer kills you
Net probability also known as the marginal probability.
Crude probability also known as cumulative incidence function.
‘Crude’ is, unfortunately, also used to mean ‘unadjusted’.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 23 / 53
Crude (and net) survival
What does a net survival of 50% mean?10-year probabilities of death
Measure Age 40 Age 60 Age 80
Net prob. of death (1-net surv) 0.50 0.50 0.50Crude (actual): cancer death 0.49 0.48 0.42Crude (actual): non-cancer death 0.02 0.08 0.42Crude (actual): any cause death 0.51 0.57 0.84
Taken from:Rutherford MJ. Care needed in interpretation of cancer survival measures. The Lancet 2014.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 24 / 53
Crude (and net) survival
Among men 75 years at diagnosis, the probability of dying ofprostate cancer within 10 years of diagnosis is 27% in thehypothetical world where it is not possible to die of othercauses.
However, in the real world where it is possible to die of othercauses the probability of dying of prostate cancer within 10years of diagnosis is 18%.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 25 / 53
Crude (and net) survival
Survival curves that accommodate competing causes of death(crude survival) are used to quantify survival in the real worldthat the patients’ live in (instead of the hypothetical world).
The cancer-specific survival in the presence of competing risksis often higher than cancer-specific survival in the absence ofcompeting risks, since some patients will die due to other.
Conversely, the crude probability of death from cancer is lowerthan the net probability of death due to cancer.
Crude Prob of Death (cancer) = ∫t
0S∗(u)R(u)λ(u)du
Crude Prob of Death (other causes) = ∫t
0S∗(u)R(u)h∗(u)du
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 26 / 53
Crude (and net survival), example
Published in: How can we make cancer survival statistics more useful for patients and clinicians: an illustrationusing localized prostate cancer in Sweden. Eloranta S, Adolfsson J, Lambert PC, Stattin P, Akre O, Andersson TM,Dickman PW. Cancer Causes Control. 2013.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 27 / 53
Loss in expectation of life
Loss in life expectancy is calculated as the difference between thelife expectancy in the general population (if not diagnosed withcancer) and the life expectancy in the cancer population.
LEL(z) = ∫tmax
0S∗(t, z′)dt − ∫
tmax
0S(t, z)dt
Interpretation: the change in the life expectancy due to a cancerdiagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 28 / 53
Loss in expectation of life
Cancer cohortall-cause survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 29 / 53
Loss in expectation of life
Life expectancy of cancer population 10.6 years
Cancer cohortall-cause survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 29 / 53
Loss in expectation of life
Life expectancy of cancer population 10.6 years
Cancer cohortall-cause survival Population survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 29 / 53
Loss in expectation of life
Life expectancy of cancer population 10.6 yearsLife expectancy of general population 15.3 years
Cancer cohortall-cause survival Population survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 29 / 53
Loss in expectation of life
Life expectancy of cancer population 10.6 yearsLife expectancy of general population 15.3 years
Loss in Expectationof Life = 4.7 years
Cancer cohortall-cause survival Population survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 29 / 53
Why use loss in expectation of life?
Common reported metrics are relevant at a particular point infollow-up time after diagnosis e.g. 1-year relative survival.
Relative survival is not an easy to understand measure andmakes communication of results difficult.
Can fit a complex model but still get a simple interpretation.
Real-world interpretation instead of a netmeasure.
Useful questions
Quantify disease burden in the society “how manylife-years are lost due to the disease?”
Quantify differences between socio-economic groups orcountries “how many life-years would be gained if Englandhad the same cancer patient survival as Sweden?”
Quantify the impact a cancer diagnosis has on apatient’s life expectancy.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 30 / 53
Loss in expectation of life
Cancer cohortall-cause survival
Population survival
0.0
0.2
0.4
0.6
0.8
1.0
Sur
viva
l pro
porti
on
0 5 10 15 20 25 30Years since diagnosis
How do we extrapolate observed survival?
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 31 / 53
Loss in expectation of life
Relativesurvival
All-cause survival
in cancer = Relative
survival x Expected
(population) survival
0.0
0.2
0.4
0.6
0.8
1.0S
urvi
val p
ropo
rtion
0 5 10 15 20 25 30Years since diagnosis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 32 / 53
Loss in expectation of life
LEL = ∫tmax
0S∗(t)dt − ∫
tmax
0S(t)
LEL = ∫tmax
0S∗(t)dt − ∫
tmax
0S∗(t) ×R(t)dt
Can make different assumptions
1 Cure: no excess mortality after a certain point in time
2 Constant excess mortality after a certain point in time
3 Excess mortality estimated from the model
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 33 / 53
Loss in expectation of life, example
General population
CML patients
0
5
10
15
20
25
30
35
40
Life
exp
ecta
ncy
1970 1980 1990 2000 2010
Age 55
0
5
10
15
20
25
30
35
40
1970 1980 1990 2000 2010
Age 65
0
5
10
15
20
25
30
35
40
Life
exp
ecta
ncy
1970 1980 1990 2000 2010Year of diagnosis
Age 75
0
5
10
15
20
25
30
35
40
1970 1980 1990 2000 2010Year of diagnosis
Age 85
Published in: Life Expectancy of Patients With Chronic Myeloid Leukemia Approaches the Life Expectancy ofthe General Population. Bower H, Bjorkholm M, Dickman PW, Hoglund M, Lambert PC, Andersson TM-L. J ClinOncol. 2016.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 34 / 53
Loss in expectation of life, example
General population
CML patients
LEL
0
5
10
15
20
25
30
35
40
Life
exp
ecta
ncy
1970 1980 1990 2000 2010
Age 55
0
5
10
15
20
25
30
35
40
1970 1980 1990 2000 2010
Age 65
0
5
10
15
20
25
30
35
40
Life
exp
ecta
ncy
1970 1980 1990 2000 2010Year of diagnosis
Age 75
0
5
10
15
20
25
30
35
40
1970 1980 1990 2000 2010Year of diagnosis
Age 85
Published in: Life Expectancy of Patients With Chronic Myeloid Leukemia Approaches the Life Expectancy ofthe General Population. Bower H, Bjorkholm M, Dickman PW, Hoglund M, Lambert PC, Andersson TM-L. J ClinOncol. 2016.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 34 / 53
Loss in expectation of life
Other reporting measures
Proportion of life lost
Loss in expectation of life is a highly age dependent measure.
Using the proportional, rather than absolute scale, for theimpact on life expectancy improves comparability across ageand deprivation groups.
proportion of life lost = loss in expectation of life
life expectancy for general population
Total life years lost in the whole population
Total population life years lost due to a cancer diagnosis isestimated by multiplying the number of patients diagnosed withcancer in a specific year by the loss in expectation of life.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 35 / 53
Loss in expectation of life, example
Total Years lost for females diagnosed in England in 2013
0 2,000 4,000 6,000 8,000 10,000People diagnosed in 2013
Lung
Colon
Rectum
Melanoma
Bladder
Stomach
Breast
Ovarian
0 5 10 15Average years of life lost
Lung
Colon
Rectum
Melanoma
Bladder
Stomach
Breast
Ovarian
0 50,000 100,000 150,000 200,000 250,000Total years lost for 2013 diagnoses
LungColon
RectumMelanoma
BladderStomach
BreastOvarian
Least deprived
Dep 2
Dep 3
Dep 4
Most deprived
Published in: Estimating the impact of a cancer diagnosis on life expectancy by socio-economic group for arange of cancer types in England. Syriopoulou E, Bower H, Andersson TM-L, Lambert PC, Rutherford MJ. Br JCancer. 2017.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 36 / 53
Period analysis
When interest lies in survival for recently diagnosed patients,we have not yet observed the follow-up of these patients. Sohow can we get a prediction of their survival?
This can be done using period analysis.
Time at risk is left truncated at the start of the periodwindow and right censored at the end.
This suggestion was initially met with scepticism althoughstudies based on historical data have shown that
period analysis provides very good predictions of the prognosisof newly diagnosed patients; andhighlights temporal trends in patient survival sooner thancohort methods.
The approach can be used with all measures I’ve presented.
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Period analysis
68,628 cases registered by the Finnish Cancer Registry.10 Mortalityfollow-up, which is performed by annual record linkage of registry datawith death records, is very efficient because of the existence of personalidentification numbers. At the time of this analysis, both registration ofnew cases and mortality follow-up had been complete until the end of1997. Patients with the 15 most common forms of cancer were includedin this analysis, and results are presented separately for each of theseforms of cancer.
Methods of Analysis
The principle of period analysis, which has been described in detailelsewhere,7,8 is simple. Briefly, period estimates of survival for a recenttime period are obtained by left truncation of observations at thebeginning of that period in addition to right censoring at its end. In ouranalysis, we aimed to derive the most up-to-date 10-year survivalcurves of cancer patients that could be obtained with the database of theFinnish Cancer Registry, which at that time was complete up to andincluding the year 1997. Using traditional cohort-wise survival analy-sis, we could have derived 10-year survival curves from cohorts ofpatients who have been under observation for 10 years or more by theend of 1997, ie, patients who have been diagnosed in 1987 or earlieryears. To minimize the role of random variation, we might haveincluded patients diagnosed in the 5-year interval 1983 to 1987 in thatanalysis. This approach is illustrated by the grey solid frame in Fig 1.
However, survival curves obtained in that way that pertain to patientsdiagnosed many years ago might be quite outdated in case of recentimprovement in survival. Therefore, we aimed to obtain more up-to-date survival curves by exclusive consideration of the survival experi-ence of patients in a recent time period (for example, the 1993 to 1997period as indicated by the black squares frame in Fig 1) by periodanalysis. This analysis includes all patients diagnosed in 1983 to 1997,but the analysis is restricted to the 1993 to 1997 period by lefttruncation of observations at the beginning of 1993 in addition to right
censoring at the end of 1997. With that approach, survival experienceduring the first year after diagnosis is provided by patients diagnosedbetween 1992 and 1997, survival experience during the second year afterdiagnosis is provided by patients diagnosed between 1991 and 1996 and soon, until survival experience during the tenth year of diagnosis, which isprovided by patients diagnosed between 1983 and 1988.
Of course, it would be of great interest to know how well the 10-yearrelative curves obtained by period analysis for the 1993 to 1997 periodactually predict the 10-year relative survival curves of patients diag-nosed in that time period. A definite answer to this question will onlybe possible 10 years from now. It is possible, however, to evaluate howwell period analysis would have predicted meanwhile known 10-yearsurvival curves of patients diagnosed in the past.
To evaluate the performance of period analysis for deriving up-to-date survival curves we, therefore, proceeded as follows: we comparedthe 10-year survival curves actually observed for patients diagnosed in1983 to 1997 (the most recent cohort of patients who had completed10-year follow-up by the end of 1997, see Fig 1, solid grey frame) withthe most up-to-date estimates of the 10-year survival function thatmight have been derived at the time of diagnosis of these patients (ie,in 1983 to 1987) using either traditional cohort-wise analysis or periodanalysis. For simplicity, we neglected any delay in cancer registration,mortality follow-up, and data analysis. In this ideal situation, thetraditional cohort estimates available in 1983 to 1987 would havereflected the survival experience in 1973 to 1987 of the cohorts ofpatients diagnosed in 1973 to 1977, who have completed 10-yearfollow-up in 1983 to 1987 (see Fig 1, solid black frame). By contrast,the period estimates available in 1983 to 1987 would have exclusivelyreflected the survival experience in 1983 to 1987 of patients diagnosedbetween 1973 and 1987 (see Fig 1, dashed black frame). This wouldhave been achieved by inclusion of this whole cohort in the analysisalong with left truncation of all observations at the beginning of 1983(in addition to right censoring at the end of 1987). This type of
Fig 1. Database for the 10-year survival curves available in1993 to 1997 by cohort analysis(grey frame) and period analysis(black squares frame), and in1983 to 1987 by cohort analysis(solid black frame) and periodanalysis (dashed frame). Figureswithin cells indicate years of fol-low-up since diagnosis.
827LONG-TERM SURVIVAL CURVES
Copyright © 2002 by the American Society of Clinical Oncology. All rights reserved. 143.210.152.102.
Information downloaded from jco.ascopubs.org and provided by University of Leicester Library on December 2, 2008 from
Year of follow-up
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 38 / 53
Period analysis
Illustration of period analysis for 4 patients
Start and Stopat Risk Times
Standard Period
(0, 2)
(0, 4)
(0, 6)
(0, 3)Subject 4
Subject 3
Subject 2
Subject 1
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
DiagnosisDeath or Censoring
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 39 / 53
Period analysis
Illustration of period analysis for 4 patients
Period of Interest
Start and Stopat Risk Times
Standard Period
(0, 2)
(0, 4)
(0, 6)
(0, 3)Subject 4
Subject 3
Subject 2
Subject 1
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
DiagnosisDeath or Censoring
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 39 / 53
Period analysis
Illustration of period analysis for 4 patients
Period of Interest
Start and Stopat Risk Times
Standard Period
(0, 2)
(0, 4)
(0, 6)
(0, 3)
(0, 2)
(2, 4)
(5, 6)
(−, −)Subject 4
Subject 3
Subject 2
Subject 1
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
DiagnosisDeath or Censoring
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 39 / 53
Period analysis
Talback et al. (2004) evaluated period predictions usinghistorical data from Sweden.
The authors estimated cohort, complete, and period estimatesusing data that would have been available in each of the years1970–1992.
Compared these predictions with the survival of patientsactually diagnosed in those years (with follow-up to 2002).
Forty different forms of cancer and all sites combined werestudied for males and females aged 0–89 at diagnosis.
The analyses showed that period relative survival provides, ingeneral, a better prediction of the subsequent survival ofnewly diagnosed patients than cohort or complete estimates.
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Period analysis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 41 / 53
Period analysis
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 42 / 53
Estimating expected survival
To estimate the RSR for a group of cancer patients, werequire an estimate of the expected survival proportion for acomparable group from the general population who areassumed to be practically free of the cancer of interest,matched by age, sex, and calendar time.
The estimates of the expected survival proportions are basedon tables of annual probabilities of death in the generalpopulation.
Expected survival obtained from national population mortalityfiles stratified by age, sex, calendar year, other covariates.
This file is sometimes referred to as the ‘popmort file’ and thefilename often contains the word ‘popmort’.
The probabilities can generally be obtained from the office ofnational statistics (or http://www.mortality.org/).
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 43 / 53
Estimating expected survival
First rows of popmort file
. use popmort, clear
. list
+----------------------------------------+
| sex _year _age prob rate |
|----------------------------------------|
1. | 1 1951 0 .96429 .0363632 |
2. | 1 1951 1 .99639 .0036165 |
3. | 1 1951 2 .99783 .0021724 |
4. | 1 1951 3 .99842 .0015812 |
5. | 1 1951 4 .99882 .0011807 |
|----------------------------------------|
6. | 1 1951 5 .99893 .0010706 |
7. | 1 1951 6 .99913 .0008704 |
8. | 1 1951 7 .99905 .0009504 |
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 44 / 53
Estimating expected survival
Expected survival for a 79 year-old man in 1989
Tabell: Interval-specific (p∗i (h)) and cumulative (1p∗
i (h)) expectedsurvival probabilities for a 79 year-old man in 1989.
age (i) 79 80 81 82 83
year 1989 1990 1991 1992 1993p∗i (h) 0.9062 0.8954 0.8936 0.8863 0.8719
1p∗
i (h) 0.9062 0.8114 0.7250 0.6426 0.5602
This is the so-called Ederer I approach for estimating expectedsurvival. Other approaches differ in which intervals each individualwill contribute to the estimation of expected survival (see nextslide).
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 45 / 53
Estimating expected survival
Expected survival can be thought of as being calculated for acohort of patients from the general population matched byage, sex, and period. The methods differ regarding how longeach individual is considered to be ‘at risk’ for the purpose ofestimating expected survival.
Ederer I: the matched individuals are considered to be at riskindefinitely (even beyond the closing date of the study). Thetime at which a cancer patient dies or is censored has noeffect on the expected survival.
Ederer II: the matched individuals are considered to be at riskuntil the corresponding cancer patient dies or is censored.
Hakulinen: if the survival time of a cancer patient is censoredthen so is the survival time of the matched individual.However, if a cancer patient dies the matched individual isassumed to be ‘at risk’ until the closing date of the study.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 46 / 53
Summary
There are a lot of different measures of cancer patient survival
All measures are of interest, and they show different aspectsof survival
Which method to use depends on the researchquestion/objective/target audience
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 47 / 53
Discussion points
Which of the survival measures presented do you find incancer registry reports?
What is the target audience of the cancer registry reports? Arethe measures included appropriate for the target audience?
Which measures do you think would be vauable to add?
Which measures are of interest to:
patients?clinicians?researchers?health care policy makers?
Are there any cancer sites for which any of the measures arenot of interest/appropriate? E.g. cure, loss in expectation oflife, crude probability of death.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 48 / 53
References
Dickman PW, Adami HO. Interpreting trends in cancerpatient survival. J Intern Med 2006.
Rutherford MJ, Dickman PW, Lambert PC. Comparison ofmethods for calculating relative survival in population-basedstudies. Cancer Epidemiology 2012.
Pohar Perme M, Stare J, Esteve J. On estimation in relativesurvival. Biometrics 2012.
Bjorkholm M, Ohm L, Eloranta S, Derolf A, Hultcrantz M,Sjoberg J, Andersson T, Hoglund M, Richter J, Landgren O,Kristinsson SY, and Dickman PW. Success Story of TargetedTherapy in Chronic Myeloid Leukemia: A Population-BasedStudy of Patients Diagnosed in Sweden From 1973 to 2008. JClin Oncol 2011.
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References
Verdecchia A, De Angelis R, Capocaccia R, Sant M, MicheliA, Gatta G, Berrino F. The cure for colon cancer: results fromthe EUROCARE study. Int J Cancer 1998.
Andersson TM, Dickman PW, Eloranta S, Lambert PC. BMCMed Res Methodol. Estimating and modelling cure inpopulation-based cancer studies within the framework offlexible parametric survival models 2011.
Andersson TML, Lambert PC, Derolf AR, Kristinsson SY,Eloranta S, Landgren O, Bjorkholm M, Dickman PW.Temporal trends in the proportion cured among adultsdiagnosed with acute myeloid leukaemia in Sweden1973-2001, a population-based study. Br J Haematol 2010.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 50 / 53
References
Lambert PC, Dickman PW, Nelson CP, Royston P. Estimatingthe crude probability of death due to cancer and other causesusing relative survival models. Stat Med 2010.
Rutherford MJ. Care needed in interpretation of cancersurvival measures. The Lancet 2014.
Eloranta S, Adolfsson J, Lambert PC, Stattin P, Akre O,Andersson TM, Dickman PW. How can we make cancersurvival statistics more useful for patients and clinicians: anillustration using localized prostate cancer in Sweden. CancerCauses Control 2013.
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References
Andersson TML, Dickman PW, Eloranta S, Lambe M,Lambert PC. Estimating the loss in expectation of life due tocancer using flexible parametric survival models. Stat Med2013.
Bower H, Bjorkholm M, Dickman PW, Hoglund M, LambertPC, Andersson TM-L. Life Expectancy of Patients WithChronic Myeloid Leukemia Approaches the Life Expectancy ofthe General Population. J Clin Ocol 2016.
Syriopoulou E, Bower H, Andersson TM-L, Lambert PC,Rutherford MJ. Estimating the impact of a cancer diagnosison life expectancy by socio-economic group for a range ofcancer types in England. Br J Cancer 2017.
Descriptive Cancer Epidemiology Andersson & Johansson ANCR 2019 52 / 53
References
Brenner H, Gefeller O. An alternative approach to monitoringcancer patient suvival. Cancer 1996.
Brenner H, Hakulinen T. Up-to-date cancer survival: periodanalysis and beyond. Int J Cancer 2009.
Talback M, Stenbeck M, Rosen M. Up-to-date long-termsurvival of cancer patients: an evaluation of period analysis onSwedish Cancer Registry data. Eur J Cancer 2004.
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