Simulation Models to Inform Health Policy: Colorectal Cancer Screening Carolyn Rutter, PhD Group Health Research Institute Supported by NCI U01 CA

April, Models for Mammography On Nov 16, 2009 the United States Preventive Services Task Force (USPSTF) revised its recommendations for screening mammography, indicating that most women should start regular breast cancer screening at age 50, not 40, and women should test every other year instead of every year. The change was made in light of: New data, including a very large study of mammography initiated at 40 from England (~1.5 million) New focus on harms, including work-up following a false positive test and over diagnosis and treatment of cancer that would not otherwise have been detected in a woman’s lifetime. Results from disease models (CISNET) The models did not provide consistent results about over-diagnosis, with estimates that ranged from 6% to 50% of breast cancers. However, there was agreement that mammograms every two years give the nearly the same benefit as annual ones but confer half the risk of harms, and that there was little benefit to screening women in their 40’s. (Paraphrasing Don Berry) New York Times, 11/22/09: “Behind Cancer Guidelines, Quest for Data”, Gina Kolata

April, Models for Colorectal Cancer November 2008: The USPSTF used microsimulation models to guide recommendations for colorectal cancer screening. Both models supported a range of screening strategies, starting at age 50 up to age 75: colonoscopy every 10 years annual FOBT flexible sigmoidoscopy every 5 years with a mid-interval FOBT Zauber, Lansdorp-Vogelaar, Knudsen, et al 2008 January 2009: The Medicare Evidence & Discovery Coverage Advisory Committee considered coverage of CT colonography for colorectal cancer screening, based on: Operating characteristics & risks of different tests (systematic review) Expert Testimony Model Predictions Knudsen, Lansdorp-Vogelaar, Rutter, et al 2010 Recommendation: Insufficient evidence to support CTC for screening

April, Microsimulation Models Microsimulation models simulate outcomes in a population of interest by simulating individual event history trajectories. A natural history model refers to the mathematical formulae that describe these individual histories. The natural history model is combined with a screening or treatment model to project population-level effects of treatment. Existing models describe: Cancers: prostate, breast, lung, colorectal, esophogeal, cervical Cardiovascular disease, diabetes

April, Adenoma to Carcinoma Pathway Normal Epithelium Colorectal Cancer Small Adenoma Advanced Adenoma Thanks to Ann Ann Zauber!

April, Example: CRC-SPIN model for colorectal cancer Time-Dependent Poisson Process with intensity i ( t) i ( t ) a function of age gender patient-specific risk adenoma growth model + P(invasive) =  (size) no lesion adenoma invasive disease clinical disease t1t1 t2t2 t3t3 Lognormal sojourn time model) ColoRectal Cancer Simulated Population model for Incidence and Natural history Rutter & Savarino, Cancer Epidemiology Biomarkers & Prevention, 2010 DEATH

April, CRC-SPIN model for colorectal cancer Model Summary 1.Adenoma Risk Model (7 parameters): 2.Adenoma Growth Model: Time to 10mm (4 parameters) 3.Adenoma Transition Model (8 parameters) 4.Sojourn Time Model (4 parameters) 23 parameters

April, CRC-SPIN model for colorectal cancer Adenoma Location: Multinomial distribution, informed by 9 autopsy studies (and similar to a recent colonoscopy study) : 0 parameters P(cecum)=0.08 P(ascending colon)=0.23 P(transverse colon)=0.24 P(descending colon)=0.12 P(sigmoid colon)=0.24 P(rectum)=0.09 CRC Survival: Assign CRC survival using survival curves estimated SEER survival data from 1975 to 1979, stratified by location (colon or rectum) and AJCC stage with age and sex included as covariates. Other Cause Survival: Assign other-cause mortality using product-limit estimates for age and birth-year cohorts from the National Center for Health Statistics Databases.

Calibrating Models in Economic Evaluation: A Seven-Step Approach Vanni, Karnon, Madan, et al. Pharmacoepidemiology, Which parameters should be varied in the calibration process? 2. Which calibration targets should be used? 3. What measure of goodness of fit (GOF) should be used? 4. What parameter search strategy should be used? 5. What determines acceptable GOF parameter sets (convergence criteria)? 6. What determines the termination of the calibration process (stopping rule)? 7. How should the model calibration results and economic parameters be integrated? April,

10 Example: Calibration of CRC-SPIN no lesion adenoma invasive disease clinical disease t1t1 t2t2 t3t3 1 study of Adenoma Prevalence (3 age groups) 2 studies of cross- sectional size information (3 size categories) 23 Parameters 1 study of preclinical cancer incidence SEER cancer incidence rates by age, sex, & location (1978) + Expert opinion about t1, t2, and t3 and prior information, about adenoma prevalence 29 data points

April, Microsimulation Model Calibration: Published Approaches One-at-a-time parameter perturbation, with subjective judgment One-at-a-time parameter perturbation, with chi-square or deviance statistics Grid Search using objective functions (e.g., likelihood) Undirected: evaluate objective function at each node or at a random set of parameter values. Not computationally feasible for highly parameterized models, dense grids can miss maxima. Directed: move through the parameter space toward improved goodness of fit to calibration data. Fewer evaluations of the likelihood, but can converge to local maxima. A key challenge is numeric approximation of derivatives. Likelihood approaches Bayesian estimation (MCMC, other approaches are possible) Simplify the likelihood to allow usual estimation approaches. The simplified model needs to be flexibly enough to be useful for prediction Active area of research

April, Microsimulation Model Calibration Bayesian calibration approach: Place priors on model parameters (allows explicit incorporation of expert opinion) Use simulation-based estimation (Markov Chain Monte Carlo or Sampling Importance Resampling)

JSM Vancouver, Microsimulation Model Calibration Bayesian model: data distribution: Y |    (y;  ) prior distribution:  ~  (·) posterior distribution  | y ~ h(  ;y) =  (y;  )  (·)/  (y) Microsimulation model: data distribution: Y |    (y;g(  )) prior distribution:  ~  (·) posterior distribution  | y ~ h(  ;y) =  (y;g(  ))  (·)/  (y) Bayesian Estimation: Closed form posterior Simulation-based estimation: * Markov Chain Monte Carlo (MCMC) * Importance sampling Likelihood is not closed form The data distribution is parameterized by g(  ), an unknown function of model parameters. Data sets do not have a 1-1 correspondence with model parameters. Solution: Simulate data given θ, and use this to simulate g(θ)

April, Microsimulation Model Calibration Advantages of Bayesian Calibration (simulated draws from the posterior distribution): Interval estimation Posterior predicted values for calibration data (Goodness of Fit) Can compare prior and posterior distributions to gain insight about parameter identifiability (Garrett & Zeger, Biometrics 2000)

JSM Vancouver, Bayesian MSM Calibration Overlap Statistic Compare prior and posterior distributions Garret & Zeger `Latent Class Model Diagnostics', Biometrics,

April, Microsimulation Model Calibration A 2009 review by Stout and colleagues (Pharmacoeconomics) found: Most modeling analyses did not describe calibration approaches Those that did generally used either undirected or directed grid searches. Big problem: lack of information about precision. Grid searches generally provide point estimates, with no measures of uncertainty for either estimated parameters or resulting model predictions. A few ad hoc approaches have been proposed, for example: ‘uncertainty intervals’ based on sampling parameters over a specified range Provide a range of predictions based on parameters that provide equally good fit to observed data.

April, Example of a Modeling Analysis Table 8C. Undiscounted costs by type, number of life-years gained, and number of cases of CRC per year-olds, by screening scenario – SIMCRC Scenario Screening Costs Total Costs LYG Symptomatic CRC Screen-Det CRC No Screening0$3.5M0600 SENSA$122K$2.8M FIT$248K$2.6M FSIG+SENSA$444K$2.7M FSIG+FIT$638K$2.8M CSPY$783K$2.7M CTC(1)$1.1M$3.3M CTC(2)$1.2M$3.3M160712

April,

April, Simpler models, similar answers Comparative Effectiveness Studies of CT colonography (Rutter, Knudsen, Pandharipande, Aca Radiol, 2011) Literature review: 1decision tree model 6 cohort models 3microsimulation models Similar overall disease processes (adenoma-carcinoma), similar ‘calibration’ data, but different levels of detail. Microsimulation models: multiple adenomas, of different sizes and in different (& detailed) locations in the colorectum Cohort / decision tree models: most with one adenoma, one with two in either the proximal or distal colon.

April, Simpler models, similar answers First Author YearModel TypeMost Effective Least Costly Most Cost-Effective Heitman 2005Decision TreeCSPY Sonnenberg 1999CohortCSPYCTCCSPY Ladabaum 2004CohortCSPY Vijan 2007CohortCSPY Hassan 2007CohortCSPYCTC Lee 2010CohortCSPYCTCCTC & CSPY Telford 2010CohortCSPY Knudsen 2010MicrosimCSPY

April, colonoscopy CT C Costs: CTC vs colonoscopy with and without polypectomy Hassan et al Lee et al

Accuracy of CTC v Colonoscopy April, Assumptions made by Hassan et al Assumptions made by Lee et al Small lesions not modelled

April, What next?  Simulation models are increasingly being used to inform health policy  Useful tool, with some shortcomings oMany are complex (when do simper models make sense?) oFew methods for estimating the precision of model predictions oFew (no?) models are ‘public use’  Potential for other applications oStudy design