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

1

Anna Johansson

[email protected]

Therese Andersson

[email protected]

/59

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

2

70% of those who

get cancer survives

/59

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!

3/59

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

4/59

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)

5/59

The setting: Descriptive epidemiology – is not clear-cut

6

• 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

/59

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

8/59

http://www-dep.iarc.fr/NORDCAN/English/frame.asp

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

9/59

http://gco.iarc.fr/

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

10

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”)

12

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”

13/59

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.

14

Source: NORDCAN

N cases

/59

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

/59

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

/59

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

/59


• 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


• 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

/59

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

/59

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


• 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)

22/59


• 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.)

23/59

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)?

25/59

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)

26/59

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?

27

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

28/59

Mortality

29

• 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.

30/59

Mortality

31

The relationship between incidence, mortality and survival

32

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.

/59

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

34

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

/59

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)

35/59

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

36/59

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”

/59

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)

38

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%

/59

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).

39/59

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”.

40/59

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

41/59

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

42/59

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

44/59

Survival: Relative survival – a non-mathematical explanation

45

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


• 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


• 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)

52/59


• 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


• 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

http://www.ancr.nu/

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)


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


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?).


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.


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.


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.



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.



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.



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.



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.



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.


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.


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.


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


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


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


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


Statistical Cure

(a)

Sur

viva

l of U

ncur

ed







Statistical Cure

(a)

(b)

Sur

viva

l of U

ncur

ed







Statistical Cure

(a)

(b)

(c)

Sur

viva

l of U

ncur

ed







Statistical Cure

(a)

(b)

(c)

(d)Sur

viva

l of U

ncur

ed







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



0.5

11.

52

2.5

Med

ian

Sur

viva

l Tim

e

0.2

.4.6

.8

Cur

e P

ropo

rtio

n



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


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.


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.



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).



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’.



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.



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%.



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


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.



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



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



Life expectancy of cancer population 10.6 years


0.0

0.2

0.4

0.6

0.8

1.0S

urvi

val p

ropo

rtion




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




Life expectancy of cancer population 10.6 yearsLife expectancy of general population 15.3 years


0.0

0.2

0.4

0.6

0.8

1.0S

urvi

val p

ropo

rtion




Life expectancy of cancer population 10.6 yearsLife expectancy of general population 15.3 years

Loss in Expectationof Life = 4.7 years


0.0

0.2

0.4

0.6

0.8

1.0S

urvi

val p

ropo

rtion



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.




Population survival

0.0

0.2

0.4

0.6

0.8

1.0

Sur

viva

l pro

porti

on


How do we extrapolate observed survival?



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




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


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


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.



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


Age 75

0

5

10

15

20

25

30

35

40


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.



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.



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.


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.


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


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


Period analysis


Period of Interest


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



Period analysis


Period of Interest


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



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.


Period analysis


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/).


http://www.mortality.org/


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 |



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).



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.


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


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.


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.


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.


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.


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.


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.


Descriptive cancer epidemiology using cancer registry data An … · 2019. 8. 23. · Descriptive...

Documents

Transcript of Descriptive cancer epidemiology using cancer registry data An … · 2019. 8. 23. · Descriptive...