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Use of cure fraction model for
the survival analysis of Uterine
Cancer patients
Noori Akhtar-Danesh, PhD
Alice Lytwyn, MD, FRCPC
Laurie Elit, MD, FRCPC
McMaster University
Hamilton, Canada
[email protected]
Relative Survival Analysis
 Relative survival (RS) is defined as the
observed survival among cancer patients
divided by the expected survival in the
general population.
 It has become the standard method of
analysis for population-based cancer
registry datasets (Dickman et al., 2004;
Dickman & Adami, 2006).
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Cure fraction
 On the other hand, in population-based
cancer studies patients may be classified
into those who survive the disease and
those who encounter excess mortality risk
compared to the general population.
 Cure fraction is defined as the proportion
of patients who survived the disease and
no longer experience the excess mortality
rate (Lambert et al., 2007).
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Cure fraction
 The cure fraction estimates the proportion
of cancer patients who are statistically
cured (rather than medically cured), i.e.
they experience the same rate of mortality
as the general population.
 Therefore, it assumes that a proportion of
the cancer patients, , will be statistically
cured and the other proportion, 1-, will
experience excess mortality rate.
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The concept of relative survival
- Cumulative survival
Cumulative RSR
110
100
90
80
70
60
50
40
30
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10
0
Observed
Expected
Relative
Excess mortality
Excess mortality
0
By courtesy of Mats Talbäck
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10
Years since diagnosis
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Cure fraction
 In this approach there is no need to know
the actual cause of death.
 Indeed, it includes all causes of death
whether or not it is directly or indirectly
associated with the diagnosis of cancer
(Ederer, Axtell, & Cutler, 1961).
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Cure fraction model
 To use a cure fraction model, the
background mortality rate for the general
population needs to be incorporated in the
model.
 We used a cure fraction model to estimate
both the cure fraction rate and the relative
survival for patients diagnosed with uterine
cancer in Canada over the period of 19922005.
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Uterine cancer
 Uterine cancer is the most common type of
gynaecological cancer.
 The Canadian Cancer Society estimates
that cancer of the body of the uterus
affects about 4500 women across Canada
annually and about 790 women are
expected to die each year (2010) .
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Objectives
 To estimate effects of age and
geographical region on the survival of
uterine cancer patients.
 To estimate long-term trends in the
survival of uterine cancer patients in
Canada.
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Methods
Statistical Analysis
 We used a non-mixture cure fraction model
with Weibull distribution for relative survival
analysis.
 We used restricted cubic splines with 5 knots
to model the effects of year of diagnosis
which provides more flexibility to model nonlinear trends (Durrleman & Simon, 1989).
 Then, we predicted the cure fraction rate and
median survival for each age group based on
the year of diagnosis.
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Methods
Inclusion criteria
 Women were included if they:
• had an new diagnosis of uterine cancer from
1992- 2005
• were between 16-79 years of age at the time of
diagnosis.
 The age range of 16-79 years was selected
to include women who were in or had
completed their reproductive age.
 Follow-up information was retained until the
end of 2006.
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Methods
Exclusion criteria
 Women were excluded if they were 80
years or older because the cure fraction
model is less reliable for this age group
(Lambert et al., 2007).
 Patients were also excluded if the diagnosis
was only based on the death certificate or
autopsy.
 Data from the province of Quebec were also
excluded because the death could not be
confirmed by CCR.
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Methods
 For the analysis by age, women were
grouped into strata given their age at
diagnosis (16- 39, 40- 49, 50- 59, 60- 69,
and 70- 79).
 Because health care in Canada is funded
by the province, women were grouped
based on the province.
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Methods
 Due to small sample sizes provinces were
collapsed into geographically cohesive
regions as:
• British Columbia,
• Central-west and Northern Canada: Alberta,
•
•
Saskatchewan, Manitoba, Yukon, Nunavut,
Northwest Territories,
Ontario,
Eastern Canada: New Brunswick, Nova Scotia,
Prince Edward Island and Newfoundland.
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Results
 A total of 32,485 women were identified
with uterine cancer.
• Mean age at diagnosis= 61.5 (SD=10.7) year
• Median age at diagnosis= 62 year.
 87.0% of them were 50+ years old.
 Over half of the uterine cancer cases were
diagnosed in Ontario.
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Results
 The highest rate of death (26.4%) was
noted in Eastern Canada compared to:
• Ontario (24.6%),
• British Columbia (23.0%; the lowest rate),
• and Central-West & Northern Canada (23.5%).
 In total 7880 patients (24.3%) diagnosed
with uterine cancer died by the end of
2006.
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.1 .2 .3 .4 .5 .6 .7 .8 .9
0
rs_all
The cure fraction
is identified by the
portion of the
curve that plateaus
over time.
1
Cure Fraction Model
0
5
10
15
Years from Diagnosis
< 40 years
50- 59 year
70- 79 year
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40- 49 year
60- 69 year
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.1 .2 .3 .4 .5 .6 .7 .8 .9
0
rs_all
1
Cure Fraction Model
0
5
10
15
Years from Diagnosis
Ontario
Eastern Canada
British Columbia
Central-West & Northern Canada
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.85
.8
.75
.7
Cure fraction rate
.9
.95
Cure Fraction Model
1990
1995
2000
2005
Year of diagnosis
< 40 years
50- 59 year
70- 79 year
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40- 49 year
60- 69 year
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1.8
1.9
2
2.1
2.2
Cure Fraction Model
1990
1995
2000
2005
Year of diagnosis
< 40 years
50- 59 year
70- 79 year
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40- 49 year
60- 69 year
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Discussion
 We found that cure fraction rate is highly
dependent on the age of diagnosis.
 This may in part be related to:
• higher rate of co-morbidities in older women,
• earlier diagnosis of uterine cancer in younger
•
women because of indicators such as changes
in menstrual cycle
increased self awareness (i.e., body image).
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Discussion
 Over the period of 1992-2006 there has
been a general drift toward improving
median survival time over all age groups.
 This analysis indicates that both cure
fraction rate and median survival have
slightly improved over the this period.
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Discussion
 This change may reflect access to better
diagnostic techniques to:
• define at risk for uterine cancer and thus down
•
•
•
•
staging,
improved anaesthesiology and postoperative care,
improved therapies for uterine cancer,
access to several lines of adjuvant chemotherapy
and biologic agents,
and access to palliative care.
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Limitations & Strengths
 One potential limitation of the cure fraction
model is that it estimates a cured
proportion even when statistical cure is not
reached.
 We can estimate survival for the uncured
group which provides more insight to the
survival pattern of uncured patients.
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Alternative approach
 Flexible parametric model (stpm2 code for
Stata) which introduces more flexibility into
the model and can be used with or without
cure fraction assumption (Royston &
Parmar 2002; Lambert & Royston 2009).
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References
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Dickman, P.W. & Adami, H.O. 2006. Interpreting trends in cancer patient survival.
J.Intern.Med., 260, (2) 103-117.
Dickman, P.W., Sloggett, A., Hills, M., & Hakulinen, T. 2004. Regression models for relative
survival. Stat.Med., 23, (1) 51-64.
Durrleman, S. & Simon, R. 1989. Flexible regression models with cubic splines. Stat.Med., 8,
(5) 551-561.
Lambert, P.C. 2007. Modeling of the cure fraction in survival studies. Stata Journal, 7, (3) 1-25
Lambert, P.C., Thompson, J.R., Weston, C.L., & Dickman, P.W. 2007. Estimating and modeling
the cure fraction in population-based cancer survival analysis. Biostatistics, 8, (3) 576-594.
Lambert P.C., Royston P. 2009. Further development of flexible parametric models for survival
analysis. The Stata Journal ;9:265-90.
Royston P., Parmar M.K. 2002. Flexible parametric proportional-hazards and proportional-odds
models for censored survival data, with application to prognostic modelling and estimation of
treatment effects. Stat Med ;21:2175-97.
Statistics Canada 2010, Table 102-0504: Deaths and mortality rates, by age group and sex,
Canada, provinces and territories, annual (2112 series), Statistics Canada.
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