use of cure fraction model for the survival analysis of uterine cancer patients noori akhtar-danesh,...

$: Use of cure fraction model for the survival analysis of Uterine Cancer patients Noori Akhtar-Danesh, PhD Alice Lytwyn, MD, FRCPC Laurie Elit, MD, FRCPC$
Use of cure fraction model for the survival analysis of Uterine

Cancer patients

Noori Akhtar-Danesh, PhDAlice Lytwyn, MD, FRCPC Laurie Elit, MD, FRCPCMcMaster UniversityHamilton, [email protected]

Stata Conference, Chicago 2011

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

By courtesy of Mats Talbäck Stata Conference, Chicago 2011 5


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 1992-2005.

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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 non-linear 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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MethodsInclusion 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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MethodsExclusion 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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Cure Fraction Model

The cure fraction is identified by the portion of the curve that plateaus over time.

0.1

.2.3

.4.5

.6.7

.8.9

1rs

_all

0 5 10 15Years from Diagnosis

< 40 years 40- 49 year50- 59 year 60- 69 year70- 79 year

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Cure Fraction Model0

.1.2

.3.4

.5.6

.7.8

.91

rs_

all

0 5 10 15Years from Diagnosis

Ontario British ColumbiaEastern Canada Central-West & Northern Canada

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Cure Fraction Model.7

.75

.8.8

5.9

.95

Cure

fra

ctio

n r

ate

1990 1995 2000 2005Year of diagnosis


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Cure Fraction Model1

.81

.92

2.1

2.2

Me

dia

n s

urv

iva

l (ye

ar)

1990 1995 2000 2005Year of diagnosis


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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 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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use of cure fraction model for the survival analysis of uterine cancer patients noori akhtar-danesh,...

Documents

uterine cancer uterine

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proportion of cancer

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mixture cure fraction

expected survival

median survival

observed survival