Cancer Incidence Predictions (Finnish Experience)

Transcription

1 Cancer Incidence Predictions (Finnish Experience) Tadeusz Dyba Joint Research Center EPAAC Workshop, January , Ispra

2 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)

3 Example of administrative prediction: Updating cancer registry data (Annual Report of Finnish Cancer Registry 2006/2007)

4 Example of scientific prediction:

5 Precision of 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, 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.

6 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 Elimination of absolutely inaccurate predictions

7 Calculating prediction intervals Using the formula for calculating the variance of conditional distribution var(c it ) = var( E(c it θ it ) ) + E(var(c it θ it ) ) can be shown for any model: var(c T ) = var(θ T ) + E(θ T ) where θ it is the estimator of predicted number of cases c it uncertainty about = uncertainty about + uncertainty about the predicted the parameters of the future number of cases model distribution Confidence Interval Prediction Interval

8 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

9 AGE PERIOD MODELS Decreasing trends of cancer incidence lne(i it ) = α i + βt β i Increasing trends of cancer incidence I it = c it / n it c it = number of cases n it = number of person-years i = age, t = period E( I it ) = α i + β i t E( I it ) = α i ( 1+ βt ) no non-identifiability property Assumption about: c it Poisson I it, age-adjusted Normal

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11 Empirical coverage error of ex post predictions Finnish data ( ) Site Females Males Lip Oesophagus Stomach Colon Rectum Liver Gallbladder Pancreas Lung Corpus uteri Ovary Kidney Bladder Skin melanoma Skin non-melanoma Nervous system Thyroid Non-Hodgkin Leukaemia cancer sites without screening activities, horizon of prediction = 5 years, 32 ex post predictions per site

12 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 Seppanen et al., Cancer Cause Control, 2006 predicting breast cancer incidence under historical and possible future scenarios of screening practices in Finland

13 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 X 2 Deviance Dev. + 2*NP 1 α i α i ( 1 + βt ) α i ( 1 + γ r + βt ) α i ( 1 + γ r + β r t ) α i + β i t α i r α i r ( 1 + βt ) α i r ( 1 + β r t ) α i r + β i t α i r + β i r t The models for cancer incidence specific to age(α i ), period(β) and to region(γ r ) applied for cancer sites with increasing (or stable) incidence pattern. The example of fit is for lung cancer in females in Finland

14 Predictions for males in Finland as age-adjusted incidence rates based on age-period-region models

15 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 models Bashir 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 models Clements et al., Biostatistics, 2005 (prediction interval more precise than those from Bayesian approach)

16 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 Moller B, et al. Stat in Med, 2003 Engholm et al., Association of Nordic Cancer Registries. Danish Cancer Society, A four presented here age-period models can be applied using Stata macros Hakulienen T and Dyba, Stat Med, 1994; Dyba T and Hakulinen T, Stat Med, 1997; 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 On line analysis, allowing to perform predictions for certain data sets Nordic countries: Other countries:

17 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 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

18 THANK YOU

19 Long-term Bayesian predictions for Finland: numbers of new cases in females Site Observed Projected 90% CI Oesophagus ; 1552 Lung ; 6539 Melanoma ; 6216 Breast ; Brain ; 8285 Bashir G and Esteve J, J of Epidemiol and Biostat, 2001

20 Posterior estimates of the precision parameters of the predictive model Timescale Posterior median 90% Credible Interval Age ; 52.5 Period ; Cohort ; Bray I, Appl Stat, 2002

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22 Moller B, et al. Stat in Med, 2003

23 Moller B, et al. Stat in Med, 2003

24 Moller B, et al. Stat in Med, 2003

25 Moller B, et al. Stat in Med, 2003