Cancer Incidence Predictions

(Finnish Experience)

Tadeusz DybaTadeusz Dyba

Joint Research Center

EPAAC Workshop, January 22-23 2014, Ispra

Rational for making cancer incidence predictions

Administrative:

to plan the allocation of the resources

(then predictions should be as accurate as possible)

Scientific:

to evaluate the success of disease control

(then predictions that do not come true are useful)

Example of administrative prediction: Updating cancer registry data

(Annual Report of Finnish Cancer Registry 2006/2007)

Example of scientific prediction:

"Predictions are often given as point forecasts with no guidance as to

their likely accuracy."

"Given their importance, it is perhaps surprising and rather regrettable,

Precision of prediction

"Given their importance, it is perhaps surprising and rather regrettable,

that many […] do not regularly produce prediction intervals, and that

most predictions are still given as a single value."

Chris Chatfield, ”Time-Series Forecasting”,

Chapman&Chall, 2001.

Why use predictions intervals in incidence predictions?

• Monitoring a range of possible future outcomes

• Evaluation of cancer prevention actions

• Some predictions more accurate than others• Some predictions more accurate than others

• Elimination of absolutely inaccurate predictions

Calculating prediction intervals

Using the formula for calculating the variance of conditional distribution

var(ciT) = var( E(ciT | θiT ) ) + E(var(ciT | θiT ) )

can be shown for any model:

var(cT) = var(θT) + E(θT)

where θiT is the estimator of predicted number of cases ciT

uncertainty about = uncertainty about + uncertainty about

the predicted the parameters of the future

number of cases model distribution

------------------------

Confidence Interval

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Prediction Interval

Why use simple models for cancer incidence prediction?

• Rule of parsimony("status quo" assumption lying behind any prediction)

• Complicated models are not likely to hold in the future

• Lack of information or of reliable information about causes

of cancers

• Short prediction interval; more precise prediction

(if model holds)

• Clear interpretation

Decreasing trends of cancer incidence

lnE(Iit) = αi + βt Iit = cit / nit

↑ cit = number of cases

βi nit = number of person-years

i = age, t = period

AGE – PERIOD MODELS

i = age, t = period

Increasing trends of cancer incidence

E( Iit ) = αi + βit

E( Iit ) = αi ( 1+ βt ) ← no non-identifiability property

Assumption about:

cit ∼ Poisson

Iit, age-adjusted ∼ Normal

Empirical coverage error of ex post predictionsFinnish data (1953-2003)

Site Females Males

Lip 95 95

Oesophagus 95 95

Stomach 84 100

Colon 97 73

Rectum 89 97

Liver 78 65

Gallbladder 76 65

Pancreas 89 97Pancreas 89 97

Lung 84 31

Corpus uteri 86 ---

Ovary 86 ---

Kidney 84 78

Bladder 76 70

Skin melanoma 89 86

Skin non-melanoma 68 81

Nervous system 76 78

Thyroid 68 86

Non-Hodgkin 85 86

Leukaemia 81 92

cancer sites without screening activities, horizon of prediction = 5 years, 32 ex post predictions per site

Site specific predictions

- Lung Cancer

Hakulinen T and Pukkala E, Int J Epidemiol, 1981

a simulation model to predict lung cancer incidence in Finland based on historical

smoking habits and possible future scenarios of starting and quiting smoking

- Breast Cancer- Breast Cancer

Seppanen et al., Cancer Cause Control, 2006predicting breast cancer incidence under historical and possible future scenarios

of screening practices in Finland

Examples of predictive models for Finland based

on age-period-region specific data

- cancer control is by region in Finland

- stratification by region increases homogenity of data eliminating extra-Poisson

variation for the more common cancers

No. Model D.F. Pearson’s X2 Deviance Dev. + 2*NP

1 αi 702 991.2 1044.6 1070.6

2 αi ( 1 + βt ) 701 983.2 1037.8 1065.8

3 αi ( 1 + γ r + βt ) 697 701.6 724.6 760.6

4 αi ( 1 + γ r + βr t ) 693 695.9 718.9 762.9

5 αi + βi t 689 966.6 1024.5 1076.5

6 αi r 650 644.7 677.9 807.9

7 αi r ( 1 + βt ) 649 637.0 671.1 803.1

8 αi r ( 1 + βr t ) 645 634.2 667.0 807.0

9 αi r + βi t 638 619.8 657.4 811.4

10 αi r + βi r t 588 577.6 609.9 863.9

The models for cancer incidence specific to age(αi), period(β) and to region(γr) applied for cancer sites

with increasing (or stable) incidence pattern.

The example of fit is for lung cancer in females in Finland

Predictions for males in Finland as age-adjusted incidence rates

based on age-period-region models

Other approaches to prediction

Age-period-cohort models

Moller B, et al. Stat in Med, 2003

(empirical evaluation of using Age-Period-Cohort models for prediction, applying different

methods using data from Nordic countries, no evaluation of the precision of the performed

predictions by means of prediction interval)

Rutherford M et al. Int J Biost 2012, Phd Thesis 2011

(in the framework of flexible parametric modelling forces period and cohort cubic spline

functions to be linear beyond the boundary knot in order to predict the future incidence)

Bayesian age-period-cohort modelsBashir G and Esteve J, J of Epidemiol and Biostat, 2001

Bray I, Appl Stat, 2002

Baker A and Bray I, Am. J Epidemiol, 2005

Cleries R et al., Stat Med, 2012(choice of smoothing priors is crucial, long credible intervals)

Generalized additive modelsClements et al., Biostatistics, 2005(prediction interval more precise than those from Bayesian approach)

Software for incidence prediction

- Published papers are sometimes accompanied by computer code to perform prediction

or the code is available upon request, many Bayesian predictions use Winbugs software

- Nordpred package developed in Norway, written in R software

http://www.kreftregisteret.no/en/Research/Projects/Nordpred/Nordpred-software/

Moller B, et al. Stat in Med, 2003

Engholm et al., Association of Nordic Cancer Registries. Danish Cancer Society, 2009

- A four presented here age-period models can be applied using Stata macros- A four presented here age-period models can be applied using Stata macros

http://www.cancer.fi/syoparekisteri/en/general/links/,

Hakulienen T and Dyba, Stat Med, 1994; Dyba T and Hakulinen T, Stat Med, 1997; 2000

- Prediction based on APC models using restricted cubic splines uses Stata macros

Rutherford M et al., Int J Biost 2012; Stata Jour 2012; Rutherford M, Phd Thesis 2011

- On line analysis, allowing to perform predictions for certain data sets

Nordic countries: http://www.kreftregisteret.no/en/Research/Projects/Nordpred/Nordpred-software/

Other countries: http://www-dep.iarc.fr/WHOdb/predictions_sel.htm

Closing remarks

• Predictive methods should clearly state assumptions used during prediction process

• One method of fitting all cancer sites doesn't exist

• Performed cancer incidence predictions often lack necessary measure of precision

• Mathematically advanced predictive methods/models are hard to interpret• Mathematically advanced predictive methods/models are hard to interpret

• The need of collecting software used by different prediction methods at one place (website?)

• Without external information on cancer etiology, latency time, screening activities

performing cancer incidence predictions will remain a challenge

THANK YOU

Long-term Bayesian predictions for Finland:

numbers of new cases in females 1990-1994

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Site Observed Projected 90% CI

___________________________________________

Oesophagus 442 440 130; 1552 Oesophagus 442 440 130; 1552

Lung 2208 2152 706; 6539

Melanoma 1240 1675 431; 6216

Breast 13930 15032 4952; 46354

Brain 1948 2117 643; 8285

___________________________________________ Bashir G and Esteve J, J of Epidemiol and Biostat, 2001

Posterior estimates of the precision parameters

of the predictive model

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Timescale Posterior median 90% Credible Interval

_______________________________________________

Age 18.6 6.9; 52.5 Age 18.6 6.9; 52.5

Period 674.2 54.9; 2993.4

Cohort 513.2 61.6; 2724.3

Bray I, Appl Stat, 2002

Moller B, et al. Stat in Med, 2003

Moller B, et al. Stat in Med, 2003

Moller B, et al. Stat in Med, 2003

Moller B, et al. Stat in Med, 2003