Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Markov Cohort State-Transition Model: A Multinomial Distribution Representation.

HIGHLIGHTS: A Markov model simulates the average experience of a cohort of patients.Monte Carlo simulation, the standard approach for estimating the variance, is computationally expensive.A multinomial distribution provides an exact representation of a Markov model.Using the known formulas of a multinomial distribution, the mean and variance of a Markov model can be readily calculated.

Mathematical Models for Evaluating Effectiveness and Cost-Effectiveness of Cervical Cancer Control Policies in Populations Including Women Living With Human Immunodeficiency Virus: A Scoping Review.

OBJECTIVES: Mathematical modeling is increasingly used to inform cervical cancer control policies, and model-based evaluations of such policies in women living with human immunodeficiency virus (HIV) are an emerging research area. We did a scoping review of published literature to identify research gaps and inform future work in this field. METHODS: We systematically searched literature up to April 2022 and included mathematical modeling studies evaluating the effectiveness or cost-effectiveness of cervical cancer prevention strategies in populations including women living with HIV. We extracted information on prevention strategies and modeling approaches. RESULTS: We screened 1504 records and included 22 studies, almost half of which focused on South Africa. We found substantial between-study heterogeneity in terms of strategies assessed and modeling approaches used. Fourteen studies evaluated cervical cancer screening strategies, 7 studies assessed human papillomavirus vaccination (with or without screening), and 1 study evaluated the impact of HIV control measures on cervical cancer incidence and mortality. Thirteen conducted cost-effectiveness analyses. Markov cohort state-transition models were used most commonly (n = 12). Most studies (n = 17) modeled the effect of HIV by creating HIV-related health states. Thirteen studies performed model calibration, but 11 did not report the calibration methods used. Only 1 study stated that model code was available upon request. CONCLUSIONS: Few model-based evaluations of cervical cancer control strategies have specifically considered women living with HIV. Improvements in model transparency, by sharing information and making model code publicly available, could facilitate the utility of these evaluations for other high disease-burden countries, where they are needed for assisting policy makers.

An approach to quantify parameter uncertainty in early assessment of novel health technologies.

Health economic modeling of novel technology at the early stages of a product lifecycle has been used to identify technologies that are likely to be cost-effective. Such early assessments are challenging due to the potentially limited amount of data. Modelers typically conduct uncertainty analyses to evaluate their effect on decision-relevant outcomes. Current approaches, however, are limited in their scope of application and imposes an unverifiable assumption, that is, uncertainty can be precisely represented by a probability distribution. In the absence of reliable data, an approach that uses the fewest number of assumptions is desirable. This study introduces a generalized approach for quantifying parameter uncertainty, that is, probability bound analysis (PBA), that does not require a precise specification of a probability distribution in the context of early-stage health economic modeling. We introduce the concept of a probability box (p-box) as a measure of uncertainty without necessitating a precise probability distribution. We provide formulas for a p-box given data on summary statistics of a parameter. We describe an approach to propagate p-boxes into a model and provide step-by-step guidance on how to implement PBA. We conduct a case and examine the differences between the status-quo and PBA approaches and their potential implications on decision-making.

Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.

Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.

Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and cost-effectiveness analysis.

Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision-relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for representing and propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the unknown cumulative distribution function (p-box) and without assuming a particular form of the distribution function. We give the formulas of the p-boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p-boxes into a black-box mathematical model, and introduce an approach for decision-making based on the results of PBA. We demonstrate the characteristics and utility of PBA vs PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.

Regulatory and HTA early dialogues in medical devices.

INTRODUCTION: Specific guidance and examples for health technology assessment (HTA) of medical devices are scarce in medical device development. A more intense dialogue of competent authorities, HTA agencies, and manufactures may improve evidence base on clinical and cost-effectiveness. Especially as the new Medical Device Regulation requires more clinical evidence. METHODS: We explore the perceptions of manufacturers, competent authorities, and HTA agencies towards such dialogues and investigate how they should be designed to accelerate the translational process from development to patient access using semi-structured interviews. We synthesized the evidence from manufacturers, competent authorities, and HTA agencies from 14 different jurisdictions across Europe. RESULTS: Eleven HTA agencies, four competent authorities, and eight manufacturers of high-risk devices expressed perceptions on the current situation and the expected development of three types of early dialogues. DISCUSSION: The MDR has to be taken into account when designing the early dialogue processes. Transferring insights from medicinal product regulation is limited as the regulatory pathways differ substantially. CONCLUSION: Early dialogues promise to accelerate the translational process and to provide faster access to innovative medical devices. However, health policy-makers should promote and fully establish regulatory and HTA early dialogues before introducing parallel early dialogues of regulatory, HTA agencies, and manufacturers. For initiating change, the legislator must create the legal basis and set the appropriate incentives for manufacturers.

Age-specific rates of onset of cannabis use in Mexico.

BACKGROUND: Over the previous two decades, the lifetime prevalence of cannabis use has risen among Mexico's population. AIMS: Estimate the sex- and age-specific rates of onset of cannabis use over time. DESIGN: Five nationally representative cross-sectional surveys, the Mexican National Surveys of Addictions (1998, 2002, 2008, 2012) and the Mexican National Survey on Drugs, Alcohol, and Tobacco Consumption (2016). SETTING: Mexico. PARTICIPANTS: Pooled sample of 141,342 respondents aged between 12 and 65 years of which 43.6%(n = 61,658) are male and 56.4% (n = 79,684) are female. MEASUREMENTS: We estimated the age-specific rates of onset of cannabis as the conditional rate of consuming cannabis for the first time at a specific age. METHODS: Time-to-event flexible-parametric models with spline specifications of the hazard function. Stratified analysis by sex and control for temporal trends by year of data collection or decennial birth cohort. FINDINGS: Age-specific rates of onset of cannabis use per 1,000 individuals increased over time for females and males. Peak rates of onset of cannabis use per 1,000 ranged from 0.935 (95%CI = [0.772, 1.148]) in 1998, to 5.391 (95%CI = [4.924, 5.971]) in 2016 for females; and from 7.513 (95%CI = [6.732, 10.063]) in 1998, to 26.107 (95%CI = [25.918,30.654]) in 2016 for males. Across decennial birth-cohorts, peak rates of onset of cannabis use per 1,000 individuals for females ranged from 0.234 (95%CI = [0.078, 0.768]) for those born in the 1930s, to 14.611 (95%CI = [13.243, 16.102]) for those born in the 1990s; and for males, from 4.086 (95%CI = [4.022, 7.857]) for those born in the 1930s, to 38.693 (95%CI = [24.847, 48.670]) for those born in the 1990s. CONCLUSION: Rates of onset of cannabis increased over the previous two decades for both females and males but remained higher for males. Across recent cohorts, the rates of onset have increased at a faster rate among females than males.

Active learning for efficiently training emulators of computationally expensive mathematical models.

An emulator is a fast-to-evaluate statistical approximation of a detailed mathematical model (simulator). When used in lieu of simulators, emulators can expedite tasks that require many repeated evaluations, such as sensitivity analyses, policy optimization, model calibration, and value-of-information analyses. Emulators are developed using the output of simulators at specific input values (design points). Developing an emulator that closely approximates the simulator can require many design points, which becomes computationally expensive. We describe a self-terminating active learning algorithm to efficiently develop emulators tailored to a specific emulation task, and compare it with algorithms that optimize geometric criteria (random latin hypercube sampling and maximum projection designs) and other active learning algorithms (treed Gaussian Processes that optimize typical active learning criteria). We compared the algorithms' root mean square error (RMSE) and maximum absolute deviation from the simulator (MAX) for seven benchmark functions and in a prostate cancer screening model. In the empirical analyses, in simulators with greatly varying smoothness over the input domain, active learning algorithms resulted in emulators with smaller RMSE and MAX for the same number of design points. In all other cases, all algorithms performed comparably. The proposed algorithm attained satisfactory performance in all analyses, had smaller variability than the treed Gaussian Processes, and, on average, had similar or better performance as the treed Gaussian Processes in six out of seven benchmark functions and in the prostate cancer model.

Adding noise to Markov cohort state-transition model in decision modeling and cost-effectiveness analysis.

Following its introduction over 30 years ago, the Markov cohort state-transition model has been used extensively to model population trajectories over time in health decision modeling and cost-effectiveness analysis studies. We recently showed that a cohort model represents the average of a continuous-time stochastic process on a multidimensional integer lattice governed by a master equation, which represents the time-evolution of the probability function of an integer-valued random vector. By leveraging this theoretical connection, this study introduces an alternative modeling method using a stochastic differential equation (SDE) approach, which captures not only the mean behavior but also the variance of the population process. We show the derivation of an SDE model from first principles, describe an algorithm to construct an SDE and solve the SDE via simulation for use in practice, and demonstrate the two applications of an SDE in detail. The first example demonstrates that the population trajectories, and their mean and variance, from the SDE and other commonly used methods in decision modeling match. The second example shows that users can readily apply the SDE method in their existing works without the need for additional inputs beyond those required for constructing a conventional cohort model. In addition, the second example demonstrates that the SDE model is superior to a microsimulation model in terms of computational speed. In summary, an SDE model provides an alternative modeling framework which includes information on variance, can accommodate for time-varying parameters, and is computationally less expensive than a microsimulation for a typical cohort modeling problem.

A theoretical foundation for state-transition cohort models in health decision analysis.

Following its introduction over three decades ago, the cohort model has been used extensively to model population trajectories over time in decision-analytic modeling studies. However, the stochastic process underlying cohort models has not been properly described. In this study, we explicate the stochastic process underlying a cohort model, by carefully formulating the dynamics of populations across health states and assigning probability rules on these dynamics. From this formulation, we explicate a mathematical representation of the system, which is given by the master equation. We solve the master equation by using the probability generation function method to obtain the explicit form of the probability of observing a particular realization of the system at an arbitrary time. The resulting generating function is used to derive the analytical expressions for calculating the mean and the variance of the process. Secondly, we represent the cohort model by a difference equation for the number of individuals across all states. From the difference equation, a continuous-time cohort model is recovered and takes the form of an ordinary differential equation. To show the equivalence between the derived stochastic process and the cohort model, we conduct a numerical exercise. We demonstrate that the population trajectories generated from the formulas match those from the cohort model simulation. In summary, the commonly-used cohort model represent the average of a continuous-time stochastic process on a multidimensional integer lattice governed by a master equation. Knowledge of the stochastic process underlying a cohort model provides a theoretical foundation for the modeling method.

The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening.

BACKGROUND: The ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model is a state-transition microsimulation model evaluating the benefits and harms of prostate cancer (PCa) screening. The natural history and detection component of the original model was based on the 2003 version of the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model, which was not calibrated to prevalence data. Compared with data from autopsy studies, prevalence of latent PCa assumed by the original model is low, which may bias the model toward screening. Our objective was to recalibrate the original model to match prevalence data from autopsy studies as well and compare benefit-harm predictions of the 2 model versions differing in prevalence. METHODS: For recalibration, we reprogrammed the natural history and detection component of the PCOP model as a deterministic Markov state-transition cohort model in the statistical software package R. All parameters were implemented as variables or time-dependent functions and calibrated simultaneously in a single run. Observed data used as calibration targets included data from autopsy studies, cancer registries, and the European Randomized Study of Screening for Prostate Cancer. Compared models were identical except for calibrated parameters. RESULTS: We calibrated 46 parameters. Prevalence from autopsy studies could not be fitted using the original parameter set. Additional parameters, allowing for interruption of disease progression and age-dependent screening sensitivities, were needed. Recalibration to higher prevalence demonstrated a considerable increase of overdiagnosis and decline of screening sensitivity, which significantly worsened the benefit-harm balance of screening. CONCLUSIONS: Our calibration suggests that not all cancers are at risk of progression, and screening sensitivity may be lower at older ages. PCa screening models that use calibration to simulate disease progression in the unobservable latent phase are highly sensitive to prevalence assumptions.

Calibrating longitudinal models to cross-sectional data: the effect of temporal changes in health practices.

OBJECTIVES: To assess the impact of simulating temporal changes in health-care practice patterns when calibrating longitudinal models to cross-sectional data. METHODS: A Markov model of cervical cancer was calibrated to recent age-specific US data on the prevalence of cervical abnormalities, cervical cancer incidence, and related mortality. The impact of failing to account for temporal changes in screening practices was assessed by comparing results from 1) a conventional calibration that incorrectly assumed that all women had been exposed to current screening practices in the past and 2) an historically accurate calibration that reflected the fact that US women 65 years of age and older had not received currently available screening practices at younger ages. RESULTS: The parameter set derived from conventional calibration produced a cervical cancer incidence rate of 13.4 per 100,000 among women aged 65 years and older, which is equal to the target end point. However, when this parameter set was used in the model to simulate the effects of historically correct screening, cervical incidence and related mortality in the 65 years and older age group were overestimated by 18% and 47%, respectively. Finally, when the parameter set was correctly calibrated by assuming historical changes in screening in the calibration process, excellent calibration to both incidence and mortality was obtained. CONCLUSIONS: Calibrating longitudinal models to cross-sectional data without accounting for temporal changes in clinical practice may result in a parameter set that is not as optimized as it appears and may lead to bias in evaluating the effectiveness of interventions.

Methods of model calibration: observations from a mathematical model of cervical cancer.

BACKGROUND: Mathematical models are commonly used to predict future benefits of new therapies or interventions in the healthcare setting. The reliability of model results is greatly dependent on accuracy of model inputs but on occasion, data sources may not provide all the required inputs. Therefore, calibration of model inputs to epidemiological endpoints informed by existing data can be a useful tool to ensure credibility of the results. OBJECTIVE: To compare different computational methods of calibrating a Markov model to US data. METHODS: We developed a Markov model that simulates the natural history of human papillomavirus (HPV) infection and subsequent cervical disease in the US. Because the model consists of numerous transition probabilities that cannot be directly estimated from data, calibration to multiple disease endpoints was required to ensure its predictive validity. Goodness of fit was measured as the mean percentage deviation of model-predicted endpoints from target estimates. During the calibration process we used the manual, random and Nelder-Mead calibration methods. RESULTS: The Nelder-Mead and manual calibration methods achieved the best fit, with mean deviations of 7% and 10%, respectively. Nelder-Mead accomplished this result with substantially less analyst time than the manual method, but required more intensive computing capability. The random search method achieved a mean deviation of 39%, which we considered unacceptable despite the ease of implementation of that method. CONCLUSIONS: The Nelder-Mead and manual techniques may be preferable calibration methods based on both performance and efficiency, provided that sufficient resources are available.