Bayesian calibration of a natural history model with application to a population model for colorectal cancer

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Maximizing the cost-effectiveness of cervical screening in the context of routine HPV vaccination by optimizing screening strategies with respect to vaccine uptake: a modeling analysis

BACKGROUND

Regarding primary and secondary cervical cancer prevention, the World Health Organization proposed the cervical cancer elimination strategy that requires countries to achieve 90% uptake of human papillomavirus (HPV) vaccines and 70% screening uptake. The optimal cervical screening strategy is likely different for unvaccinated and vaccinated cohorts upon national HPV immunization. However, health authorities typically only provide a one-size-fits-all recommendation for the general population. We aimed to evaluate the cost-effectiveness for determining the optimal screening strategies for vaccinated and unvaccinated cohorts.

METHODS

We considered the women population in Hong Kong which has a unique HPV infection and cervical cancer epidemiology compared to other regions in China and Asia. We used mathematical models which comprise a deterministic age-structured compartmental dynamic component and a stochastic individual-based cohort component to evaluate the cost-effectiveness of screening strategies for cervical screening. Following the recommendations in local guidelines in Hong Kong, we considered strategies that involved cytology, HPV testing, or co-testing as primary cervical screening. We also explored the impacts of adopting alternative de-intensified strategies for vaccinated cohorts. The 3-year cytology screening was used as the base comparator while no screening was also considered for vaccinated cohorts. Women's lifetime life years, quality-adjusted life years, and costs of screening and treatment were estimated from the societal perspective based on the year 2022 and were discounted by 3% annually. Incremental cost-effectiveness ratios (ICERs) were compared to a willingness to pay (WTP) threshold of one gross domestic product per capita (US $47,792). Probabilistic and one-way sensitivity analyses were conducted.

RESULTS

Among unvaccinated cohorts, the strategy that adds reflex HPV to triage mild cytology abnormality generated more life years saved than cytology-only screening and could be a cost-effective alternative. Among vaccinated cohorts, when vaccine uptake was 85% (based on the uptake in 2022), all guideline-based strategies (including the cytology-only screening) had ICERs above the WTP threshold when compared with no screening if the vaccine-induced protection duration was 20 years or longer. Under the same conditions, HPV testing with genotyping triage had ICERs (compared with no screening) below the WTP threshold if the routine screening interval was lengthened to 10 and 15 years or screening was initiated at ages 30 and 35 years.

CONCLUSIONS

HPV testing is a cost-effective alternative to cytology for vaccinated cohorts, and the associated optimal screening frequency depends on vaccine uptake. Health authorities should optimize screening recommendations by accounting for population vaccine uptake.

Modeling the Natural History and Screening Effects of Colorectal Cancer Using Both Adenoma and Serrated Neoplasia Pathways: The Development, Calibration, and Validation of a Discrete Event Simulation Model

Background. Existing colorectal cancer (CRC) screening models mostly focus on the adenoma pathway of CRC development, overlooking the serrated neoplasia pathway, which might result in overly optimistic screening predictions. In addition, Bayesian inference methods have not been widely used for model calibration. We aimed to develop a CRC screening model accounting for both pathways, calibrate it with approximate Bayesian computation (ABC) methods, and validate it with large CRC screening trials. Methods. A discrete event simulation (DES) of the CRC natural history (DECAS) was constructed using the adenoma and serrated pathways in R software. The model simulates CRC-related events in a specific birth cohort through various natural history states. Calibration took advantage of 74 prevalence data points from the German screening colonoscopy program of 5.2 million average-risk participants using an ABC method. CRC incidence outputs from DECAS were validated with the German national cancer registry data; screening effects were validated using 17-y data from the UK Flexible Sigmoidoscopy Screening sigmoidoscopy trial and a German screening colonoscopy cohort study. Results. The Bayesian calibration rendered 1,000 sets of posterior parameter samples. With the calibrated parameters, the observed age- and sex-specific CRC prevalences from the German registries were within the 95% DECAS-predicted intervals. Regarding screening effects, DECAS predicted a 41% (95% intervals 30%-51%) and 62% (95% intervals 55%-68%) reduction in 17-y cumulative CRC mortality for a single screening sigmoidoscopy and colonoscopy, respectively, falling within 95% confidence intervals reported in the 2 clinical studies used for validation. Conclusions. We presented DECAS, the first Bayesian-calibrated DES model for CRC natural history and screening, accounting for 2 CRC tumorigenesis pathways. The validated model can serve as a valid tool to evaluate the (cost-)effectiveness of CRC screening strategies.

Highlights

This article presents a new discrete event simulation model, DECAS, which models both adenoma-carcinoma and serrated neoplasia pathways for colorectal cancer (CRC) development and CRC screening effects.DECAS is calibrated based on a Bayesian inference method using the data from German screening colonoscopy program, which consists of more than 5 million first-time average-risk participants aged 55 years and older in 2003 to 2014.DECAS is flexible for evaluating various CRC screening strategies and can differentiate screening effects in different parts of the colon.DECAS is validated with large screening sigmoidoscopy and colonoscopy clinical study data and can be further used to evaluate the (cost-)effectiveness of German colorectal cancer screening strategies.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO₂ meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO₂ generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO₂ readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO₂ generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO₂ levels. The occupancy schedule becomes critical when the CO₂ data are limited, whereas a 15-min measurement interval could capture dynamic CO₂ profiles well even without the occupancy information. Hourly CO₂ recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m³ and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m³ and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m³ and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m³ and 8-9 occupancies) for three consecutive days.

Optimizing the Design of a Repeated Fecal Immunochemical Test Bowel Cancer Screening Programme With a Limited Endoscopy Capacity From a Health Economic Perspective

OBJECTIVES

In 2016, it was announced that the fecal immunochemical test (FIT) would replace the guaiac fecal occult blood test in the UK Bowel Cancer Screening Programme. England has limited endoscopy capacity. This study informed decision making by determining the most cost-effective FIT screening strategy (age range, frequency, and FIT threshold) under a constrained endoscopy capacity.

METHODS

An economic model with a colorectal cancer natural history component was used to model 60 221 screening strategies with first screening at age 50 to 60 years, screening interval of 1 to 6 years, 3+ screening episodes, and FIT integer threshold of 20 to 180 μg hemoglobin/g feces. Screening strategies requiring the same endoscopy capacity were compared to determine the characteristics of the most cost-effective strategies.

RESULTS

With 50 000 annual screening referral colonoscopies, the 20 most cost-effective strategies had a starting age of 50 to 53 years, 2-yearly screening, 7 or 8 rounds of screening, and FIT threshold of 127 to 166. Compared with a 2-yearly screening interval, screening less frequently (3-, 4-, 5-, or 6-yearly) with a more sensitive FIT was less cost-effective.

CONCLUSIONS

The UK Bowel Cancer Screening Programme should use a 2-yearly FIT screening interval. When endoscopy capacity increases, the screening starting age should be reduced first followed by reducing the FIT threshold. These findings are relevant for other colorectal cancer screening programs with constrained endoscopy capacity.

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models

Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making.

Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework.

Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of −0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters’ posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold.

Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

Calibrating Natural History of Cancer Models in the Presence of Data Incompatibility: Problems and Solutions

The calibration of cancer natural history models is often challenged by a lack of representative calibration targets, forcing modellers to rely on potentially incompatible datasets. Using a microsimulation colorectal cancer model as an example, the purposes of this paper are to (1) highlight the reasons for uncertainty in calibration targets, (2) illustrate practical and generalisable approaches for dealing with incompatibility in calibration targets, and (3) discuss the importance of future research in the area of incorporating uncertainty in calibration. The low quality of data and differences in populations, outcome definitions, and healthcare systems may result in incompatibility between the model and the data. Acknowledging reasons for data incompatibility allows assessment of the risk of incompatibility before calibrating the model. Only a few approaches are available to address data incompatibility, for instance addressing biases in calibration targets and their adjustment, relaxing the goodness-of-fit metric, and validation of the calibration targets to the data not used in the calibration. However, these approaches lack explicit comparison and validation, and so more research is needed to describe the nature and causes of indirect uncertainty (i.e. uncertainty that cannot be expressed in absolute quantitative forms) and identify methods for managing this uncertainty in healthcare modelling.

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

BACKGROUND

Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive.

METHODS

This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low.

RESULTS

The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity.

CONCLUSIONS

A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals

State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). Estimating TPs presents numerous issues, including missing data for specific transitions, data incongruence and uncertainty around extrapolation. Inappropriately estimated TPs could result in biased models. There is limited guidance on how to address common issues associated with TP estimation. To assess current methods for estimating TPs and to identify issues that may introduce bias, we reviewed National Institute for Health and Care Excellence Technology Appraisals published from 1 January, 2019 to 27 May, 2020. Twenty-eight models (from 26 Technology Appraisals) were included in the review. Several methods for estimating TPs were identified: survival analysis (n = 11); count method (n = 9); multi-state modelling (n = 7); logistic regression (n = 2); negative binomial regression (n = 2); Poisson regression (n = 1); and calibration (n = 1). Evidence Review Groups identified several issues relating to TP estimation within these models, including important transitions being excluded (n = 5); potential selection bias when estimating TPs for post-randomisation health states (n = 2); issues concerning the use of multiple data sources (n = 4); potential biases resulting from the use of data from different populations (n = 2), and inappropriate assumptions around extrapolation (n = 3). These issues remained unresolved in almost every instance. Failing to address these issues may bias model results and lead to sub-optimal decision making. Further research is recommended to address these methodological problems.

CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models

Introduction

Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.

Methods

Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.

Results

We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.

Conclusions

We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.

Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review

OBJECTIVE

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

MATERIALS AND METHODS

We searched 10 electronic databases for English-language articles published between January 1, 2008, and March 1, 2019, that described the development of a simulation model with a focus on average-risk CRC screening delivery. Included articles were reviewed for evidence that the model was validated, and provided real or potential contribution to informed decision making using the GRADE EtD (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision) framework.

RESULTS

A total of 43 studies met criteria. The majority used Markov modeling (n = 31 [72%]) and sought to determine cost-effectiveness, compare screening modalities, or assess effectiveness of screening. No study reported full model validation and only (58%) reported conducting any validation. Majority of models were developed to address a specific health systems or policy question; few articles report the model's impact on this decision (n = 39 [91%] vs. n = 5 [12%]). Overall, models provided evidence relevant to every element important to decision makers as outlined in the GRADE EtD framework.

DISCUSSION AND CONCLUSION

Simulation modeling contributes evidence that is considered valuable to decision making in CRC screening delivery, particularly in assessing cost-effectiveness and comparing screening modalities. However, the actual impact on decisions and validity of models is lacking in the literature. Greater validity testing, impact assessment, and standardized reporting of both is needed to understand and demonstrate the reliability and utility of simulation modeling.

External Validation of a Colorectal Cancer Model Against Screening Trial Long-Term Follow-Up Data

BACKGROUND

The University of Sheffield School of Health and Related Research (ScHARR) Bowel Cancer Screening Model has been used previously to make decisions about colorectal cancer screening strategies in England.

OBJECTIVES

The objective of this study was to perform an external validation of the ScHARR model against long-term follow-up data about colorectal cancer (CRC) incidence and mortality reductions due to screening, from the Nottingham trial of guaiac faecal occult blood testing for CRC, and the UK Flexible Sigmoidoscopy Screening Trial.

METHODS

The ScHARR model was adapted prior to validation to reflect the setting of each trial in terms of population characteristics, details of screening and surveillance programs, uptake of screening, and further investigations and study follow-up. The impact of using current versus historical CRC incidence and mortality data in the validation was also examined by carrying out a series of analyses in which historical data from different years was included in the model.

RESULTS

The ScHARR model was able to predict CRC incidence and mortality rate/hazard ratios from both trials to well within the 95% confidence intervals in the observed data. While it was less accurate in predicting absolute incidence and mortality rates, modeling historical incidence and mortality data enabled these predictions to be improved considerably.

CONCLUSION

The ScHARR model is able to replicate the long-term relative benefit from screening observed in 2 large-scale UK-based screening trials and can therefore be considered to be an appropriate tool to facilitate decision making around the English bowel cancer screening program.

Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes

Background. Developing efficient procedures of model calibration, which entails matching model predictions to observed outcomes, has gained increasing attention. With faithful but complex simulation models established for cancer diseases, key parameters of cancer natural history can be investigated for possible fits, which can subsequently inform optimal prevention and treatment strategies. When multiple calibration targets exist, one approach to identifying optimal parameters relies on the Pareto frontier. However, computational burdens associated with higher-dimensional parameter spaces require a metamodeling approach. The goal of this work is to explore multiobjective calibration using Gaussian process regression (GPR) with an eye toward how multiple goodness-of-fit (GOF) criteria identify Pareto-optimal parameters. Methods. We applied GPR, a metamodeling technique, to estimate colorectal cancer (CRC)-related prevalence rates simulated from a microsimulation model of CRC natural history, known as the Colon Modeling Open Source Tool (CMOST). We embedded GPR metamodels within a Pareto optimization framework to identify best-fitting parameters for age-, adenoma-, and adenoma staging-dependent transition probabilities and risk factors. The Pareto frontier approach is demonstrated using genetic algorithms with both sum-of-squared errors (SSEs) and Poisson deviance GOF criteria. Results. The GPR metamodel is able to approximate CMOST outputs accurately on 2 separate parameter sets. Both GOF criteria are able to identify different best-fitting parameter sets on the Pareto frontier. The SSE criterion emphasizes the importance of age-specific adenoma progression parameters, while the Poisson criterion prioritizes adenoma-specific progression parameters. Conclusion. Different GOF criteria assert different components of the CRC natural history. The combination of multiobjective optimization and nonparametric regression, along with diverse GOF criteria, can advance the calibration process by identifying optimal regions of the underlying parameter landscape.

Development of an Empirically Calibrated Model of Esophageal Squamous Cell Carcinoma in High-Risk Regions

Objective

This study constructs, calibrates, and verifies a mathematical simulation model designed to project the natural history of ESCC and is intended to serve as a platform for testing the benefits and cost-effectiveness of primary and secondary ESCC prevention alternatives.

Methods

The mathematical model illustrates the natural history of ESCC as a sequence of transitions among health states, including the primary health states (e.g., normal mucosa, precancerous lesions, and undetected and detected cancer). Using established calibration approaches, the parameter sets related to progression rates between health states were optimized to lead the model outputs to match the observed data (specifically, the prevalence of precancerous lesions and incidence of ESCC from the published literature in Chinese high-risk regions). As illustrative examples of clinical and policy application, the calibrated and validated model retrospectively simulate the potential benefit of two reported ESCC screening programs.

Results

Nearly 1,000 good-fitting parameter sets were identified from 1,000,000 simulated sets. Model outcomes had sufficient calibration fit to the calibration targets. Additionally, the verification analyses showed reasonable external consistency between the model-predicted effectiveness of ESCC screening and the reported data from clinical trials.

Conclusions

This parameterized mathematical model offers a tool for future research investigating benefits, costs, and cost-effectiveness related to ESCC prevention and treatment.

Cost-utility analysis of a potential lung cancer screening program for a high-risk population in Germany: A modelling approach

BACKGROUND

Lung cancer is the leading cause of cancer death in Germany. Although several randomized trials in Europe have evaluated the effectiveness of lung cancer screening programs, evidence on the cost-effectiveness of lung cancer screening is scarce.

OBJECTIVE

To evaluate the cost-effectiveness of a population-based lung cancer screening program from the perspective of a German payer.

METHODS

We conducted a cost-effectiveness analysis from the public payer perspective for a high-risk population defined as heavy former and current smokers (≥20 cigarettes per day) between 55 and 75 years of age. The underlying model consisted of two Markov models. We differentiated between a population-based annual screening program and standard clinical care. Depending on stage at diagnosis, simulated patients were assigned to one of five treatment paths according to the German clinical guideline for the diagnosis and treatment of lung cancer. Costs, life years saved, and quality adjusted life years (QALYs) were used as outcomes. Values for input parameters were taken from the literature. The model was run for 60 cycles with a cycle length of three months. Deterministic and probabilistic sensitivity analyses were conducted.

RESULTS

In the base case, annual lung cancer screening led to an increase in incremental costs (€ 1,153 per person) compared to standard clinical care. However, the screening approach was associated with an incremental gain in life years (0.06 per person) and QALYs (0.04 per person). Thus, the incremental cost-effectiveness ratio (ICER) was € 19,302 per life year saved and € 30,291 per QALY. A probabilistic sensitivity analysis with 10,000 draws resulted in average ICERs of € 22,118 per life year and € 34,841 per QALY.

CONCLUSION

We provide evidence that lung cancer screening for a high-risk population may be more effective, but also more costly, than standard clinical care from the perspective of a German payer.

MODEL-BASED COST-EFFECTIVENESS OF CONVENTIONAL AND INNOVATIVE CHEMO-RADIATION IN LUNG CANCER

INTRODUCTION

Optimizing radiotherapy with or without chemotherapy through advanced imaging and accelerated radiation schemes shows promising results in locally advanced non-small-cell lung cancer (NSCLC). This study compared the cost-effectiveness of positron emission tomography-computed tomography based isotoxic accelerated sequential chemo-radiation (SRT2) and concurrent chemo-radiation with daily low-dose cisplatin (CRT2) with standard sequential (SRT1) and concurrent chemo-radiation (CRT1).

METHODS

We used an externally validated mathematical model to simulate the four treatment strategies. The model was built using data from 200 NSCLC patients treated with curative sequential chemo-radiation. For concurrent strategies, data from a meta-analysis and a single study were included in the model. Costs, utilities, and resource use estimates were obtained from literature. Primary outcomes were the incremental cost-effectiveness and cost-utility ratio (ICUR) of each strategy. Scenario analyses were carried out to investigate the impact of uncertainty.

RESULTS

Total undiscounted costs and quality-adjusted life-years (QALYs) for SRT1, CRT1, SRT2, and CRT2 were EUR 17,288, EUR 18,756, EUR 19,072, EUR 17,360 and QALYs 1.10, 1.15, 1.40, and 1.40, respectively. Compared with SRT1, the ICURs were EUR 38,024/QALY for CRT1, EUR 6,249/QALY for SRT2, and EUR 346/QALY for CRT2. CRT2 was highly cost-effective compared with SRT1. Moreover, CRT2 was more effective and less costly than CRT1 and SRT2. Therefore, these strategies were dominated by CRT2.

CONCLUSION

Optimized sequential and concurrent chemo-radiation strategies are more effective and cost-effective than the current conventional sequential and concurrent strategies. Concurrent chemo-radiation with a daily low dose cisplatin regimen is the most cost-effective treatment option for locally advanced inoperable NSCLC patients.

Bayesian Methods for Calibrating Health Policy Models: A Tutorial

Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes' theorem. This article provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique theoretically optimal summary of the evidence.

Validation and calibration of structural models that combine information from multiple sources

INTRODUCTION

Mathematical models that attempt to capture structural relationships between their components and combine information from multiple sources are increasingly used in medicine. Areas covered: We provide an overview of methods for model validation and calibration and survey studies comparing alternative approaches. Expert commentary: Model validation entails a confrontation of models with data, background knowledge, and other models, and can inform judgments about model credibility. Calibration involves selecting parameter values to improve the agreement of model outputs with data. When the goal of modeling is quantitative inference on the effects of interventions or forecasting, calibration can be viewed as estimation. This view clarifies issues related to parameter identifiability and facilitates formal model validation and the examination of consistency among different sources of information. In contrast, when the goal of modeling is the generation of qualitative insights about the modeled phenomenon, calibration is a rather informal process for selecting inputs that result in model behavior that roughly reproduces select aspects of the modeled phenomenon and cannot be equated to an estimation procedure. Current empirical research on validation and calibration methods consists primarily of methodological appraisals or case-studies of alternative techniques and cannot address the numerous complex and multifaceted methodological decisions that modelers must make. Further research is needed on different approaches for developing and validating complex models that combine evidence from multiple sources.

Improving early diagnosis of symptomatic cancer

Much time, effort and investment goes into the diagnosis of symptomatic cancer, with the expectation that this approach brings clinical benefits. This investment of resources has been particularly noticeable in the UK, which has, for several years, appeared near the bottom of international league tables for cancer survival in economically developed countries. In this Review, we examine expedited diagnosis of cancer from four perspectives. The first relates to the potential for clinical benefits of expedited diagnosis of symptomatic cancer. Limited evidence from clinical trials is available, but the considerable observational evidence suggests benefits can be obtained from this approach. The second perspective considers how expedited diagnosis can be achieved. We concentrate on data from the UK, where extensive awareness campaigns have been conducted, and initiatives in the primary-care setting, including clinical decision support, have all occurred during a period of considerable national policy change. The third section considers the most appropriate patients for cancer investigations, and the possible community settings for identification of such patients; UK national guidance for selection of patients for investigation is discussed. Finally, the health economics of expedited diagnosis are reviewed, although few studies provide definitive evidence on this topic.

Understanding the Effects of Competition for Constrained Colonoscopy Services with the Introduction of Population-level Colorectal Cancer Screening

BACKGROUND

Median wait times for gastroenterology services in Canada exceed consensus-recommended targets and have worsened substantially over the past decade. Meanwhile, efforts to control colorectal cancer have shifted their focus to screening asymptomatic, average-risk individuals. Along with increasing prevalence of colorectal cancer due to an aging population, screening programs are expected to add substantially to the existing burden on colonoscopy services, and create competition for limited services among individuals of varying risk. Failure to understand the effects of operational programmatic screening decisions may cause unintended harm to both screening participants and higher-risk patients, make inefficient use of limited health care resources, and ultimately hinder a program's success.

METHODS

We present a new simulation model (Simulation of Cancer Outcomes for Planning Exercises, or SCOPE) for colorectal cancer screening which, unlike many other colorectal cancer screening models, reflects the effects of competition for limited colonoscopy services between patient groups and can be used to guide planning to ensure adequate resource allocation. We include verification and validation results for the SCOPE model.

RESULTS

A discrete event simulation model was developed based on an epidemiological representation of colorectal cancer in a sample population. Colonoscopy service and screening modules were added to allow observation of screening scenarios and resource considerations. The model reproduces population-based data on prevalence of colorectal cancer by stage, and mortality by cause of death, age, and sex, and attendant demand and wait times for colonoscopy services.

CONCLUSIONS

The study model differs from existing screening models in that it explicitly considers the colonoscopy resource implications of screening activities and the impact of constrained resources on screening effectiveness.

Modeling the Cost-Effectiveness of Alternative Upper Age Limits for Breast Cancer Screening in England and Wales

BACKGROUND

Currently in the United Kingdom, the National Health Service (NHS) Breast Screening Programme invites all women for triennial mammography between the ages of 47 and 73 years (the extension to 47-50 and 70-73 years is currently examined as part of a randomized controlled trial). The benefits and harms of screening in women 70 years and older, however, are less well documented.

OBJECTIVES

The aim of this study was to examine whether extending screening to women older than 70 years would represent a cost-effective use of NHS resources and to identify the upper age limit at which screening mammography should be extended in England and Wales.

METHODS

A mathematical model that allows the impact of screening policies on cancer diagnosis and subsequent management to be assessed was built. The model has two parts: a natural history model of the progression of breast cancer up to discovery and a postdiagnosis model of treatment, recurrence, and survival. The natural history model was calibrated to available data and compared against published literature. The management of breast cancer at diagnosis was taken from registry data and valued using official UK tariffs.

RESULTS

The model estimated that screening would lead to overdiagnosis in 6.2% of screen-detected women at the age of 72 years, increasing up to 37.9% at the age of 90 years. Under commonly quoted willingness-to-pay thresholds in the United Kingdom, our study suggests that an extension to screening up to the age of 78 years represents a cost-effective strategy.

CONCLUSIONS

This study provides encouraging findings to support the extension of the screening program to older ages and suggests that further extension of the UK NHS Breast Screening Programme up to age 78 years beyond the current upper age limit of 73 years could be potentially cost-effective according to current NHS willingness-to-pay thresholds.

Cost Effectiveness of Colorectal Cancer Screening Interventions with Their Effects on Health Disparity Being Considered

Purpose

The purpose of this study was to evaluate the cost effectiveness of colorectal cancer screening interventions with their effects on health disparity being considered.

Materials and Methods

Markov cohort simulation was conducted with the cycle/duration of 1/40 year(s). Data came from the results of randomized trials and others. Participants were hypothetical cohorts aged 50 years as of year 2013 in 16 Korean provinces. The interventions until the age of 80 were annual organized fecal occult blood test (FOBT) (standard screening), annual FOBT with basic reminders for provinces with higher mortalities than the national average (targeted reminder) and annual FOBT with basic/enhanced reminders for all provinces (universal reminder 1 and 2). The comparison was non-screening, the outcome was quality-adjusted life years, and only medical costs for screening and treatment were considered from a societal perspective. The Atkinson incremental cost effectiveness ratio (Atkinson ICER), the incremental cost effectiveness ratio adjusted by the Atkinson Inequality Index, was used to evaluate the cost effectiveness of the four interventions with their impacts on regional health disparity being considered.

Results

Health disparity was smallest (or greatest) in non-screening (or the standard screening). The targeted reminder had smaller health disparity, and smaller Atkinson ICER with respect to standard screening, than did the universal reminder 1 and 2.

Conclusion

The targeted reminder might be more cost effective than the universal reminders with their effects on health disparity being considered. This study helps to develop promotional effort for colorectal cancer screening with both the greatest cost effectiveness and the smallest health disparity

Cancer care coordinators in stage III colon cancer: a cost-utility analysis

BACKGROUND

There is momentum internationally to improve coordination of complex care pathways. Robust evaluations of such interventions are scarce. This paper evaluates the cost-utility of cancer care coordinators for stage III colon cancer patients, who generally require surgery followed by chemotherapy.

METHODS

We compared a hospital-based nurse cancer care coordinator (CCC) with 'business-as-usual' (no dedicated coordination service) in stage III colon cancer patients in New Zealand. A discrete event microsimulation model was constructed to estimate quality-adjusted life-years (QALYs) and costs from a health system perspective. We used New Zealand data on colon cancer incidence, survival, and mortality as baseline input parameters for the model. We specified intervention input parameters using available literature and expert estimates. For example, that a CCC would improve the coverage of chemotherapy by 33% (ranging from 9 to 65%), reduce the time to surgery by 20% (3 to 48%), reduce the time to chemotherapy by 20% (3 to 48%), and reduce patient anxiety (reduction in disability weight of 33%, ranging from 0 to 55%).

RESULTS

Much of the direct cost of a nurse CCC was balanced by savings in business-as-usual care coordination. Much of the health gain was through increased coverage of chemotherapy with a CCC (especially older patients), and reduced time to chemotherapy. Compared to 'business-as-usual', the cost per QALY of the CCC programme was $NZ 18,900 (≈ $US 15,600; 95% UI: $NZ 13,400 to 24,600). By age, the CCC intervention was more cost-effective for colon cancer patients < 65 years ($NZ 9,400 per QALY). By ethnicity, the health gains were larger for Māori, but so too were the costs, meaning the cost-effectiveness was roughly comparable between ethnic groups.

CONCLUSIONS

Such a nurse-led CCC intervention in New Zealand has acceptable cost-effectiveness for stage III colon cancer, meaning it probably merits funding. Each CCC programme will differ in its likely health gains and costs, making generalisation from this evaluation to other CCC interventions difficult. However, this evaluation suggests that CCC interventions that increase coverage of, and reduce time to, effective treatments may be cost-effective.

Distributional cost-effectiveness analysis of health care programmes--a methodological case study of the UK Bowel Cancer Screening Programme

This paper presents an application of a new methodological framework for undertaking distributional cost-effectiveness analysis to combine the objectives of maximising health and minimising unfair variation in health when evaluating population health interventions. The National Health Service bowel cancer screening programme introduced in 2006 is expected to improve population health on average and to worsen population health inequalities associated with deprivation and ethnicity--a classic case of 'intervention-generated inequality'. We demonstrate the distributional cost-effectiveness analysis framework by examining two redesign options for the bowel cancer screening programme: (i) the introduction of an enhanced targeted reminder aimed at increasing screening uptake in deprived and ethnically diverse neighbourhoods and (ii) the introduction of a basic universal reminder aimed at increasing screening uptake across the whole population. Our analysis indicates that the universal reminder is the strategy that maximises population health, while the targeted reminder is the screening strategy that minimises unfair variation in health. The framework is used to demonstrate how these two objectives can be traded off against each other, and how alternative social value judgements influence the assessment of which strategy is best, including judgements about which dimensions of health variation are considered unfair and judgements about societal levels of inequality aversion.

Modelling the cost-effectiveness of public awareness campaigns for the early detection of non-small-cell lung cancer

BACKGROUND

Survival rates in lung cancer in England are significantly lower than in many similar countries. A range of Be Clear on Cancer (BCOC) campaigns have been conducted targeting lung cancer and found to improve the proportion of diagnoses at the early stage of disease. This paper considers the cost-effectiveness of such campaigns, evaluating the effect of both the regional and national BCOC campaigns on the stage distribution of non-small-cell lung cancer (NSCLC) at diagnosis.

METHODS

A natural history model of NSCLC was developed using incidence data, data elicited from clinical experts and model calibration techniques. This structure is used to consider the lifetime cost and quality-adjusted survival implications of the early awareness campaigns. Incremental cost-effectiveness ratios (ICERs) in terms of additional costs per quality-adjusted life-years (QALYs) gained are presented. Two scenario analyses were conducted to investigate the role of changes in the 'worried-well' population and the route of diagnosis that might occur as a result of the campaigns.

RESULTS

The base-case theoretical model found the regional and national early awareness campaigns to be associated with QALY gains of 289 and 178 QALYs and ICERs of £13 660 and £18 173 per QALY gained, respectively. The scenarios found that increases in the 'worried-well' population may impact the cost-effectiveness conclusions.

CONCLUSIONS

Subject to the available evidence, the analysis suggests that early awareness campaigns in lung cancer have the potential to be cost-effective. However, significant additional research is required to address many of the limitations of this study. In addition, the estimated natural history model presents previously unavailable estimates of the prevalence and rate of disease progression in the undiagnosed population.

Cost-effectiveness of different strategies for stroke prevention in patients with atrial fibrillation in a health resource-limited setting

PURPOSE

To compare the lifetime cost and effectiveness of five alternative chronic atrial fibrillation (AF) management strategies: rivaroxaban, warfarin, aspirin plus clopidogrel, aspirin and no prevention.

METHODS

An individual-level state-transition model was developed to track the lifetime disease course associated with AF. The clinical and utility data were derived from published studies. The cost data were estimated based on local charges and current Chinese practices. Sensitivity analyses were used to explore the impact of uncertainty on the results.

RESULTS

For base-case patients with a CHADS2 score of 3, the cost per additional quality-adjusted life-years (QALYs) gained for rivaroxaban compared with no prevention, aspirin, aspirin plus clopidogrel and warfarin was $116,884, $153,944, $155,979 and $216,273, respectively. CHADS2 score had a substantial impact on the model outcomes for different prevention strategies. The time distribution of warfarin international normalised ratio (INR), stroke and intracranial haemorrhage (ICH) risks, cost of rivaroxaban and utility of warfarin therapy had substantial impacts on the results. Based on a willingness-to-pay threshold of $16,350/QALY, no prevention strategy was the preferred therapy for a patient with a low risk for stroke and a high risk for ICH; aspirin was preferred for patients with a moderate risk for stroke and ICH; and warfarin was preferred for patients with a high risk for stroke and a low risk of ICH.

CONCLUSION

In the context of limited health resources, rivaroxaban is unlikely to be cost-effective, although it provided more health benefits comparing with other strategies. Additionally, warfarin with good INR control might be more suitable for AF patients in developing regions.

Guaiac faecal occult blood test performance at initial and repeat screens in the English Bowel Cancer Screening Programme

BACKGROUND

In many countries, screening for colorectal cancer (CRC) relies on repeat testing using the guaiac faecal occult blood test (gFOBT). This study aimed to compare gFOBT performance measures between initial and repeat screens.

METHODS

Data on screening uptake and outcomes from the English Bowel Cancer Screening Programme (BCSP) for the years 2008 and 2011 were used. An existing CRC natural history model was used to estimate gFOBT sensitivity and specificity, and the cost-effectiveness of different screening strategies.

RESULTS

The gFOBT sensitivity for CRC was estimated to decrease from 27.35% at the initial screen to 20.22% at the repeat screen. Decreases were also observed for the positive predictive value (8.4-7.2%) and detection rate for CRC (0.19-0.14%). Assuming equal performance measures for both the initial and repeat screens led to an overestimate of the cost effectiveness of gFOBT screening compared with the other screening modalities.

CONCLUSIONS

Performance measures for gFOBT screening were generally lower in the repeat screen compared with the initial screen. Screening for CRC using gFOBT is likely to be cost-effective; however, the use of different screening modalities may result in additional benefits. Future economic evaluations of gFOBT should not assume equal sensitivities between screening rounds.

A patient-level calibration framework for evaluating surveillance strategies: a case study of mammographic follow-up after early breast cancer

OBJECTIVE

Currently all women who have completed their primary treatment for early breast cancer are invited to receive routine annual mammography. There is no randomized controlled trial evidence to support this schedule, and model-based analysis is required. This paper describes a novel data collection and model calibration process to analyze the cost-effectiveness of alternative follow-up schedules for early breast cancer survivors.

METHODS

A discrete event simulation model describes the progression of early breast cancer after the completion of primary treatment, representing impalpable and palpable recurrence and the detection of impalpable disease via follow-up mammography. Retrospective data from the South Australian Cancer Registry and clinical and administrative hospital databases were linked for 407 postmenopausal women diagnosed with moderate-prognosis early breast cancer from 2000 to 2008. These data formed the basis of a patient-level probabilistic calibration process.

RESULTS

For 50- to 69-year-old survivors, annual follow-up for 5 years, with visits every 2 years thereafter, appears to be cost-effective. For women aged 70 to 79 years at diagnosis, a surveillance schedule similar to general population screening (2 yearly) appears to be most cost-effective if high rates of adherence can be maintained.

CONCLUSIONS

This study demonstrated the potential value of combining linked, retrospective data and decision analytic modeling to provide estimates of costs and health outcomes that are sufficiently robust to inform cancer clinical guidelines and individual patient decisions regarding appropriate follow-up schedules.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

BACKGROUND

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better.

METHODS

We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting.

RESULTS

For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results.

CONCLUSIONS

Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.

Effectiveness and cost-effectiveness of an awareness campaign for colorectal cancer: a mathematical modeling study

Background

A campaign to increase the awareness of the signs and symptoms of colorectal cancer (CRC) and encourage self-presentation to a GP was piloted in two regions of England in 2011. Short-term data from the pilot evaluation on campaign cost and changes in GP attendances/referrals, CRC incidence, and CRC screening uptake were available. The objective was to estimate the effectiveness and cost-effectiveness of a CRC awareness campaign by using a mathematical model which extrapolates short-term outcomes to predict long-term impacts on cancer mortality, quality-adjusted life-years (QALYs), and costs.

Methods

A mathematical model representing England (aged 30+) for a lifetime horizon was developed. Long-term changes to cancer incidence, cancer stage distribution, cancer mortality, and QALYs were estimated. Costs were estimated incorporating costs associated with delivering the campaign, additional GP attendances, and changes in CRC treatment.

Results

Data from the pilot campaign suggested that the awareness campaign caused a 1-month 10 % increase in presentation rates. Based on this, the model predicted the campaign to cost £5.5 million, prevent 66 CRC deaths and gain 404 QALYs. The incremental cost-effectiveness ratio compared to “no campaign” was £13,496 per QALY. Results were sensitive to the magnitude and duration of the increase in presentation rates and to disease stage.

Conclusions

The effectiveness and cost-effectiveness of a cancer awareness campaign can be estimated based on short-term data. Such predictions will aid policy makers in prioritizing between cancer control strategies. Future cost-effectiveness studies would benefit from campaign evaluations reporting as follows: data completeness, duration of impact, impact on emergency presentations, and comparison with non-intervention regions.

The burden of cancer in New Zealand: a comparison of incidence and DALY metrics and its relevance for ethnic disparities

AIM

Cancer burden measured in disability adjusted life years (DALYs) captures survival and disability impacts of incident cancers. In this paper, we estimate the prospective burden of disease arising from 27 cancer sites diagnosed in 2006, by sex and ethnicity; and determine how its distribution differs from that for incidence rates alone.

METHODS

Using a prospective approach, Markov and cancer disease models were used to estimate DALYs with inputs of population counts, incidence and excess mortality rates, disability weights, and background mortality. DALYs were discounted at 3.5% per year.

RESULTS

The age standardised Māori:non-Māori incidence rate ratios were 1.00 for males and 1.19 for females, whereas for DALYs they were greater at 1.42 for males and 1.68 for females. The total burden of cancer for 2006 incident cases (i.e. not age standardised) was estimated to be approximately 127,000 DALYs. Breast (27%), lung (14%) and colorectal (13%) cancers for females and lung (16%), colorectal (14%), and prostate (16%) cancers for males were the top contributors. By ethnicity, Māori experienced a substantially higher burden from lung cancer (around 25% for both sexes).

CONCLUSIONS

Due to Māori both having higher rates of cancers with a worse survival (e.g. lung cancer), and tending to have worse survival for each cancer site, ethnic disparities in the age-standardised DALY burden were greater than those for incidence (rate ratios of 1.52 and 1.07 respectively, sexes pooled).

Beyond the cancer cell: progression-level determinants highlight the multiscale nature of carcinogenesis risk

Over the last several decades, improved awareness of the prevalence of carcinogens in the environment, along with a growing appreciation of the complexity of the carcinogenesis process, has shifted policy on cancer risk from one of strict avoidance of carcinogens to one of adherence to exposure limits deemed "safe" based on quantitative risk estimation. Meanwhile, given the mutagenic nature of most carcinogens, attention has gravitated to developing a genetic rationale for measuring and comparing risks. This focus has culminated in the now well-established multistage mutational paradigm, which holds that a stepwise sequence of mutations drives cell "initiation" and the subsequent "transformation" of an initiated cell into a cancer cell, and that, once created, a cancer cell will inevitably undergo "progression" to become overt disease. Unanticipated by this paradigm is the effect progression-phase population- and tissue-level bottleneck events may have on this process. Attesting to this is the prevalence of tumor dormancy, a state of arrested growth of an otherwise fully malignant, often microscopic cancer mass, maintained by interactions among cancer cells and between cancer and host cells. The proper inclusion of such progression-modifying influences would clearly behoove risk estimation and improve our understanding of the natural history of cancer by accounting for the less-than-certain risk of eventual cancer disease even when cancer cells are present. Such an improved understanding, in turn, stands to better inform policy-making and influence such clinical practice decisions as whether to treat the increasingly smaller tumors detectable with advancing technologies.

Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial

Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models.

Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model

OBJECTIVE

To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions.

METHODS

A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics.

RESULTS

Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels.

CONCLUSIONS

This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines.

Using resource modelling to inform decision making and service planning: the case of colorectal cancer screening in Ireland

Background

Organised colorectal cancer screening is likely to be cost-effective, but cost-effectiveness results alone may not help policy makers to make decisions about programme feasibility or service providers to plan programme delivery. For these purposes, estimates of the impact on the health services of actually introducing screening in the target population would be helpful. However, these types of analyses are rarely reported. As an illustration of such an approach, we estimated annual health service resource requirements and health outcomes over the first decade of a population-based colorectal cancer screening programme in Ireland.

Methods

A Markov state-transition model of colorectal neoplasia natural history was used. Three core screening scenarios were considered: (a) flexible sigmoidoscopy (FSIG) once at age 60, (b) biennial guaiac-based faecal occult blood tests (gFOBT) at 55–74 years, and (c) biennial faecal immunochemical tests (FIT) at 55–74 years. Three alternative FIT roll-out scenarios were also investigated relating to age-restricted screening (55–64 years) and staggered age-based roll-out across the 55–74 age group. Parameter estimates were derived from literature review, existing screening programmes, and expert opinion. Results were expressed in relation to the 2008 population (4.4 million people, of whom 700,800 were aged 55–74).

Results

FIT-based screening would deliver the greatest health benefits, averting 164 colorectal cancer cases and 272 deaths in year 10 of the programme. Capacity would be required for 11,095-14,820 diagnostic and surveillance colonoscopies annually, compared to 381–1,053 with FSIG-based, and 967–1,300 with gFOBT-based, screening. With FIT, in year 10, these colonoscopies would result in 62 hospital admissions for abdominal bleeding, 27 bowel perforations and one death. Resource requirements for pathology, diagnostic radiology, radiotherapy and colorectal resection were highest for FIT. Estimates depended on screening uptake. Alternative FIT roll-out scenarios had lower resource requirements.

Conclusions

While FIT-based screening would quite quickly generate attractive health outcomes, it has heavy resource requirements. These could impact on the feasibility of a programme based on this screening modality. Staggered age-based roll-out would allow time to increase endoscopy capacity to meet programme requirements. Resource modelling of this type complements conventional cost-effectiveness analyses and can help inform policy making and service planning.

Whole disease modeling to inform resource allocation decisions in cancer: a methodological framework

OBJECTIVES

This article presents a methodological framework for developing health economic models of whole systems of disease and treatment pathways to inform decisions concerning resource allocation-an approach referred to as "Whole Disease Modeling." This system-level approach can provide a consistent mathematical infrastructure for the economic evaluation of virtually any intervention across a disease pathway.

METHODS

The framework has been developed for cancer but is broadly generalizable to other diseases. It has been informed by pilot work, a systematic review of economic analyses, a qualitative examination of model development processes, and other literature from the fields of operational research, statistics, and health economics.

RESULTS

The framework is built on three principles: 1) the model boundary and breadth should capture all relevant aspects of the disease and its treatment-from preclinical disease through to death, 2) the model should be developed such that the decision node is conceptually transferable across the model, and 3) the costs and consequences of service elements should be structurally related. A generalized process for developing Whole Disease Models is presented.

DISCUSSION

Although this approach involves a nontrivial investment of time and resource, its value may be realized when 1) multiple options for service change require economic analysis at a single time point, 2) a disease service changes rapidly and the model can be reused, 3) current services within a pathway have not been subjected to economic analysis, 4) upstream events are expected to have important downstream effects, or 5) simple cost-utility decision rules fail to reflect the complexity of the decision-makers' objectives.

Reappraisal of the options for colorectal cancer screening in England

AIM

The aim was to use newly available data to estimate the cost effectiveness and endoscopy requirements of screening options for colorectal cancer (CRC) to inform screening policy in England.

METHODS

A state transition model simulated the life experience of a cohort of individuals in the general population of England with normal colon/rectal epithelium through to the development of adenomas and CRC and subsequent death. CRC natural history model parameters and screening test characteristics were estimated simultaneously by a process of model calibration. This process was fitted to observed data on CRC incidence in the absence of screening, data from existing screening programmes, and data from the UK flexible sigmoidoscopy (FS) screening trial. The costs, effects and resource impact were evaluated for a range of screening options involving the guaiac or immunochemical faecal occult blood test (gFOBT/iFOBT) and FS.

RESULTS

The model suggests that screening strategies involving FS or iFOBT may produce additional benefits compared with the current policy of biennial gFOBT for 60-74-year-olds. The age at which a single FS screen results in the greatest quality-adjusted life year gain was 55, with similar gains for ages between 52 and 58. Strategies which combined FS and iFOBT showed further benefits and improved economic outcomes.

CONCLUSIONS

Strategies which combine different screening modalities may provide greater clinical and economic benefits. The collection of comprehensive screening data using a uniform format will enable comparative analysis across screening programmes in different countries, will improve our understanding of the disease and will allow identification of optimal screening modalities.

Improved estimates of visual field progression using bayesian linear regression to integrate structural information in patients with ocular hypertension

PURPOSE

To assess whether neuroretinal rim area (RA) measurements of the optic disc could be used to improve the estimate of the rate of change in visual field (VF) mean sensitivity in patients with ocular hypertension (OHT) using a Bayesian linear regression (BLR), compared to a standard ordinary least squares linear regression (OLSLR) of mean sensitivity (MS) measurements alone.

METHODS

MS and RA measurements were analyzed from a longitudinal series of 179 patients with OHT visiting Moorfields Eye Hospital between 1992 and 2000. For each patient, linear regression of RA was computed after an appropriate transformation to "scale" RA with MS measurements, and the slope coefficient from this regression was used as a prior for BLR of MS. The BLR then was compared with the OLSLR approach by evaluating how accurately each regression technique predicted future MS measurements.

RESULTS

On average, BLR was significantly more accurate than OLSLR for series up to 8 measurements long (root-mean-square prediction error [RMSPE] was 0.14 decibels [dB] smaller with BLR than OLSLR; P < 0.001, Wilcoxon signed-rank test), with OLSLR of VF data alone being more accurate for longer series (RMSPE was 0.06 dB smaller with OLSLR than BLR).

CONCLUSIONS

BLR provides a significantly more accurate estimate of the rate of change in MS than the standard OLSLR approach, especially in short time series, suggesting that structural measurements can be used successfully in statistical models to assist clinicians monitoring VF progression in patients with OHT. Further studies are necessary to validate the method in glaucoma patients.

Cost-effectiveness of population-based screening for colorectal cancer: a comparison of guaiac-based faecal occult blood testing, faecal immunochemical testing and flexible sigmoidoscopy

BACKGROUND

Several colorectal cancer-screening tests are available, but it is uncertain which provides the best balance of risks and benefits within a screening programme. We evaluated cost-effectiveness of a population-based screening programme in Ireland based on (i) biennial guaiac-based faecal occult blood testing (gFOBT) at ages 55-74, with reflex faecal immunochemical testing (FIT); (ii) biennial FIT at ages 55-74; and (iii) once-only flexible sigmoidoscopy (FSIG) at age 60.

METHODS

A state-transition model was used to estimate costs and outcomes for each screening scenario vs no screening. A third party payer perspective was adopted. Probabilistic sensitivity analyses were undertaken.

RESULTS

All scenarios would be considered highly cost-effective compared with no screening. The lowest incremental cost-effectiveness ratio (ICER vs no screening euro 589 per quality-adjusted life-year (QALY) gained) was found for FSIG, followed by FIT euro 1696) and gFOBT (euro 4428); gFOBT was dominated. Compared with FSIG, FIT was associated with greater gains in QALYs and reductions in lifetime cancer incidence and mortality, but was more costly, required considerably more colonoscopies and resulted in more complications. Results were robust to variations in parameter estimates.

CONCLUSION

Population-based screening based on FIT is expected to result in greater health gains than a policy of gFOBT (with reflex FIT) or once-only FSIG, but would require significantly more colonoscopy resources and result in more individuals experiencing adverse effects. Weighing these advantages and disadvantages presents a considerable challenge to policy makers.

Accounting for Methodological, Structural, and Parameter Uncertainty in Decision-Analytic Models

Accounting for uncertainty is now a standard part of decision-analytic modeling and is recommended by many health technology agencies and published guidelines. However, the scope of such analyses is often limited, even though techniques have been developed for presenting the effects of methodological, structural, and parameter uncertainty on model results. To help bring these techniques into mainstream use, the authors present a step-by-step guide that offers an integrated approach to account for different kinds of uncertainty in the same model, along with a checklist for assessing the way in which uncertainty has been incorporated. The guide also addresses special situations such as when a source of uncertainty is difficult to parameterize, resources are limited for an ideal exploration of uncertainty, or evidence to inform the model is not available or not reliable. Methods for identifying the sources of uncertainty that influence results most are also described. Besides guiding analysts, the guide and checklist may be useful to decision makers who need to assess how well uncertainty has been accounted for in a decision-analytic model before using the results to make a decision.