An actuarial model of drug prescriptions from a general practictioner is presented. The non life actuarial approach is applied to a health economics problem
The document lists several projects completed by the individual including studies of pet insurance, automobile insurance, life insurance pricing, group legal insurance, equity analysis, pension plan pricing, and a claim experience analysis. The projects involved researching various types of insurance, analyzing industry data, developing actuarial models in Excel and SAS, calculating premiums and reserves, and presenting results.
The document describes stochastic simulations of chemical reaction cascades. It discusses simulating a series of reactions (A to B, B to C, etc.) at different rates. A simulation script is provided, and sample output shows species A decreasing while B increases over the first second. The model is expanded to allow species E to decay via a new reaction. Visualizations show this does not affect A-D profiles but changes E's profile. Faster decay of E is also discussed.
The document defines stochastic processes and their basic properties such as stationarity and ergodicity. It discusses analyzing systems using stochastic processes, including how the power spectrum represents the frequency content of a wide-sense stationary process. The power spectrum is the Fourier transform of the autocorrelation function, and the power spectrum of the output of a linear, time-invariant system is equal to the multiplication of the input power spectrum and the transfer function of the system.
Stochastic modelling and its applicationsKartavya Jain
Stochastic processes and modelling have various applications in telecommunications. Token rings, continuous-time Markov chains, and fluid-flow models are used to model traffic flow and network performance. Aggregate dynamic stochastic models can model air traffic control by representing aircraft arrivals as Poisson processes. Disturbances like weather can be incorporated by altering flow rates. Wireless network models use search algorithms and location stochastic processes to track mobile users.
This document discusses deterministic and stochastic models. Deterministic models have unique outputs for given inputs, while stochastic models incorporate random elements, so the same inputs can produce different outputs. The document provides examples of how each model type is used, including for steady state vs. dynamic processes. It notes that while deterministic models are simpler, stochastic models better account for real-world uncertainties. In nature, deterministic models describe behavior based on known physical laws, while stochastic models are needed to represent random factors and heterogeneity.
The document discusses evaluating the cost-effectiveness of diagnostic tests through modeling. It provides an overview of how cost-effectiveness analyses are applied to determine if a diagnostic test represents value for money. The modeling requires estimating test accuracy, modeling patient outcomes for different test results, and calculating an incremental cost-effectiveness ratio to compare the new test to current practice. It provides an example of modeling different diagnostic strategies for deep vein thrombosis.
Evaluating the cost effectiveness of diagnostic tests ScHARR HEDS
Evaluating the cost-effectiveness of diagnostic tests requires modeling their accuracy, the downstream patient experiences and costs based on test outcomes, and calculating the incremental cost-effectiveness ratio compared to current practice. For new diagnostic tests for sepsis, models would estimate quality of life and costs for high, medium, and low risk groups under different testing strategies incorporating a risk assessment tool and more accurate but costly test. Additional complications in evaluating diagnostics include understanding complex patient pathways, limitations of available data, dependencies between tests, and imperfect reference standards.
The document lists several projects completed by the individual including studies of pet insurance, automobile insurance, life insurance pricing, group legal insurance, equity analysis, pension plan pricing, and a claim experience analysis. The projects involved researching various types of insurance, analyzing industry data, developing actuarial models in Excel and SAS, calculating premiums and reserves, and presenting results.
The document describes stochastic simulations of chemical reaction cascades. It discusses simulating a series of reactions (A to B, B to C, etc.) at different rates. A simulation script is provided, and sample output shows species A decreasing while B increases over the first second. The model is expanded to allow species E to decay via a new reaction. Visualizations show this does not affect A-D profiles but changes E's profile. Faster decay of E is also discussed.
The document defines stochastic processes and their basic properties such as stationarity and ergodicity. It discusses analyzing systems using stochastic processes, including how the power spectrum represents the frequency content of a wide-sense stationary process. The power spectrum is the Fourier transform of the autocorrelation function, and the power spectrum of the output of a linear, time-invariant system is equal to the multiplication of the input power spectrum and the transfer function of the system.
Stochastic modelling and its applicationsKartavya Jain
Stochastic processes and modelling have various applications in telecommunications. Token rings, continuous-time Markov chains, and fluid-flow models are used to model traffic flow and network performance. Aggregate dynamic stochastic models can model air traffic control by representing aircraft arrivals as Poisson processes. Disturbances like weather can be incorporated by altering flow rates. Wireless network models use search algorithms and location stochastic processes to track mobile users.
This document discusses deterministic and stochastic models. Deterministic models have unique outputs for given inputs, while stochastic models incorporate random elements, so the same inputs can produce different outputs. The document provides examples of how each model type is used, including for steady state vs. dynamic processes. It notes that while deterministic models are simpler, stochastic models better account for real-world uncertainties. In nature, deterministic models describe behavior based on known physical laws, while stochastic models are needed to represent random factors and heterogeneity.
The document discusses evaluating the cost-effectiveness of diagnostic tests through modeling. It provides an overview of how cost-effectiveness analyses are applied to determine if a diagnostic test represents value for money. The modeling requires estimating test accuracy, modeling patient outcomes for different test results, and calculating an incremental cost-effectiveness ratio to compare the new test to current practice. It provides an example of modeling different diagnostic strategies for deep vein thrombosis.
Evaluating the cost effectiveness of diagnostic tests ScHARR HEDS
Evaluating the cost-effectiveness of diagnostic tests requires modeling their accuracy, the downstream patient experiences and costs based on test outcomes, and calculating the incremental cost-effectiveness ratio compared to current practice. For new diagnostic tests for sepsis, models would estimate quality of life and costs for high, medium, and low risk groups under different testing strategies incorporating a risk assessment tool and more accurate but costly test. Additional complications in evaluating diagnostics include understanding complex patient pathways, limitations of available data, dependencies between tests, and imperfect reference standards.
Final intro use of economic methods for injury prevention resource allocation John Wren
This 2010 paper was developed to address a range of information needs for the NZ Injury Prevention Secretariat, in particular:
1) a review of the health economics models and associated issues that must be understood when undertaking cost of injury studies
2) a review of the published New Zealand cost of injury studies to identify the methods utilised, and the size of the cost estimates calculated for various injury events
3) calculated a new total social and economic cost of injury estimate for all injuries and the six injury priority areas respectively, drawing upon the lessons learnt from the reviews undertaken
4) briefly reviewed the ways in which economic methods can be used to inform injury prevention investment decisions, and made recommendations for their use in New Zealand
5) drew conclusions and made recommendations about undertaking future cost of injury work to provide both better standardisation in approach and greater cost discrimination between injury areas.
Cost utility analysis of interventions to return employees to work following ...ScHARR HEDS
The document describes the Health Economics and Decision Science (HEDS) section within the School of Health and Related Research (ScHARR) at the University of Sheffield. HEDS conducts research to promote excellence in healthcare resource allocation and supports the implementation of research results through education and training. Its research portfolio includes areas like evidence synthesis, health economics modeling, and more. HEDS also offers post-graduate programs and short courses. It focuses on a wide range of disease areas and provides consultancy services including modeling for clinical trials and health economics analyses. The decision modeling team at HEDS specializes in cost-effectiveness analyses to support healthcare decision making.
This document describes a 9-step framework for evaluating the expected cost-effectiveness of a service intervention at the design stage. It applies this framework to evaluate an intervention to improve clinical handovers between hospital and community care.
The key steps are: 1) Identifying endpoints and grouping them, 2) Estimating baseline risks, 3) Eliciting expected effectiveness from experts, 4) Assigning utility values to endpoint groups, 5) Costing the intervention, 6) Estimating healthcare costs of adverse events, 7) Calculating health benefits, 8) Determining cost-effectiveness, and 9) Conducting sensitivity analysis.
When applied to a handover improvement intervention, literature suggested adverse events follow 19%
This document provides definitions for economic and decision-making terms used in health economics. It defines over 30 terms across multiple categories, including types of economic analysis (e.g. cost-benefit analysis, cost-effectiveness analysis), costs (e.g. direct costs, indirect costs), decision analysis tools (e.g. decision trees, Markov models), and measures of health outcomes (e.g. quality-adjusted life years, disability-adjusted life years). For each term, it provides a brief 1-2 sentence definition of its meaning in the context of health economic evaluations.
Forecasting Municipal Solid Waste Generation Using a Multiple Linear Regressi...IRJET Journal
- The document describes developing a multiple linear regression model to forecast municipal solid waste generation based on factors like population, population density, education levels, access to services, and income levels.
- The model was developed using data from various municipalities in Italy. Exploratory data analysis was conducted to determine linear relationships between waste generation and predictors.
- The linear regression model achieved a high R-squared value of 91.81%, indicating a close fit to the data. Various error metrics like MAE, MSE and RMSE were calculated to evaluate model performance.
- The regression model provides a simple yet accurate means of predicting municipal solid waste that requires minimal data and can be generalized to other locations.
EWMA 2013 - Ep503 - Quantifying the economic value of diagnostics in wound ca...EWMAConference
The document summarizes an economic model that evaluated the potential cost savings of using a point-of-care test to identify chronic wounds with elevated protease activity (EPA) and targeting protease modulating treatment for those wounds. The model estimated potential savings of £1,906 per EPA wound identified, totaling over £50,000 in savings for every 100 chronic wounds tested. By enabling targeted treatment, the model found that 19 additional wounds could heal while freeing up 556 clinical episodes of care. Therefore, implementing a "test and treat" algorithm using this diagnostic was deemed a cost-effective dominant strategy compared to standard care alone.
ENGINEERING SOLUTIONS TO REDUCE VULNERABILITIES IN BIOTECHNICAL SYSTEMSIAEME Publication
To assess the clinical significance of electrophysiological, functional, and
morphometric studies of the optic nerve in patients with serous meningitis during the
recovery period. Congestive fundus changes during the follow-up observation period in
patients with serous meningitis were diagnosed more often 19.4% (6) compared with
changes in the fundus in the acute period 13% (4)). Analysis of perimetric indices
revealed a significant decrease in photosensitivity in 80% (8) patients with serous
meningitis during the recovery period compared with the control group. According to
optical coherence tomography in patients with serous meningitis, significant changes
were detected in 90% (9) patients. According to the data of visual evoked potentials and
electroretinography in patients with serous meningitis, a deterioration in visual
afferentation was detected.
This study evaluated the lateral tarsal strip technique for correcting lower eyelid ectropion in 30 patients (41 eyelids). Patients were divided into groups based on the type of ectropion: involutional, paralytic, or cicatricial. Most patients presented with tearing. The technique successfully corrected ectropion in 35 eyelids, while 6 required additional procedures. The lateral tarsal strip proved effective for correcting ectropion, especially when lateral canthal tendon laxity is present.
Effective strategies to monitor clinical risks using biostatistics - Pubrica....Pubrica
In clinical science, biostatistics services are essential for data collection, analysis, presentation, and interpretation. Epidemiology, clinical trials, population genetics, systems biology, and other disciplines all benefit from it. It aids in the evaluation of a drug's effectiveness and safety in clinical trials.
Continue Reading: https://bit.ly/3tRRxkW
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
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The article reviews recent research on tourism demand modelling and forecasting published since 2000. It finds that forecasting methods have diversified beyond econometric models to include new techniques. However, no single model consistently performs best in all situations. The article identifies new research areas like integrating quantitative and qualitative forecasts. It also notes that seasonality analysis is commonly used given tourism's seasonal demand patterns. The article concludes by encouraging further research to improve forecast accuracy, such as combining models and focusing on turning point predictions.
The article reviews recent research on tourism demand modeling and forecasting published since 2000. It finds that forecasting methods have diversified beyond econometric models to include new techniques. However, no single model consistently outperforms others in accuracy. The article also identifies new research areas like integrating qualitative and quantitative forecasts. It notes that seasonality analysis is commonly used given tourism's seasonal demand patterns. Forecasters prefer seasonal models to help businesses optimize for demand peaks and valleys. While widely used, opportunities remain to improve seasonal modeling techniques and forecast accuracy overall.
Application of consistency and efficiency test for forecastsAlexander Decker
This document evaluates the forecasting efficiency of food price inflation, consumer price index, GDP per capita, and money supply data from Pakistan from 1975 to 2008. It uses ARIMA models to generate forecasts, which are then evaluated for consistency and efficiency. Consistency tests whether the actual and forecasted values are cointegrated and have the same order of integration. Efficiency tests examine whether forecasts minimize forecast errors and fully incorporate available information. The study finds that food price forecasts are consistent and efficient based on these criteria.
Cost Effectiveness Analysis in Health economicsGerardo García
This document summarizes a web-based seminar on using cost-effectiveness analysis as a decision support tool for employers. It defines cost-effectiveness analysis as a method to compare different health interventions or programs based on their costs and outcomes. The document provides examples of how cost-effectiveness analysis can help employers make informed decisions about benefits programs and coverage options by objectively evaluating alternatives in a standardized way. It also discusses strategic considerations for interpreting and applying cost-effectiveness analyses to support evidence-based decision making.
This study evaluates project cost estimation techniques by analyzing their accuracy under different project conditions. The objectives are to assess the performance of various techniques, understand factors influencing estimation accuracy, and develop new approaches. The research methodology involves collecting primary and secondary data, descriptive and inferential statistical analysis of survey data, and qualitative meta-synthesis of stakeholder interviews. Key findings suggest improving data quality, risk management, and integrating estimation with project management software. The conclusion is that accurate cost estimation is critical for project success and selecting the right techniques is important.
This document presents a risk assessment of an investment project to build a financial complex and business center at Tecnológico de Antioquia University. A Monte Carlo simulation was used to model the net present value and internal rate of return based on the variability of estimated cash flows. The simulation found a 44% chance that the NPV is between $0-51,680 and a 51% chance it is below $0, with an IRR between 7.47%-12.22%. While the financial assessment is positive, the project's main benefit is providing students, teachers and entrepreneurs experience with financial transactions, negotiations and simulations.
This document summarizes a review of 80 systematic reviews on the effectiveness of telemedicine. The reviews covered a range of telemedicine interventions and clinical areas. Some key findings from the reviews included:
- 21 reviews concluded that telemedicine is effective in improving outcomes.
- 18 reviews found evidence that telemedicine is promising but more research is still needed.
- Other reviews found evidence of telemedicine's effectiveness is limited and inconsistent.
- Economic analyses of telemedicine were often problematic and inconclusive.
- Some reviews highlighted benefits of telemedicine for patients, such as improved access to care.
OS20 - A methodology to estimate indirect costs associated with a possible ...EuFMD
The document presents a methodology for estimating indirect costs associated with potential outbreaks of transboundary animal diseases (TADs) in Switzerland. The methodology involves collecting qualitative and quantitative data through literature reviews, stakeholder interviews, and disease control legislation to incorporate into an economic model. An economic model including a cost calculator and decision tree model is used to estimate the cost implications of different disease control options and the impact of uncertainty. This methodology allows identification and characterization of indirect consequential costs, which are often more complex but potentially more significant than direct costs. The decision tree model specifically helps select the optimal disease control policy. This methodology could be applied to estimate costs of other TAD outbreaks.
EVALUATION OF REFERENCE EVAPOTRANSPIRATION ESTIMATION METHODS AND DEVELOPMENT...IAEME Publication
This study is an attempt to find best alternative method to estimate reference evapotranspiration (ETo) for the Nagarjuna Sagar Reservoir Project [NSRP], command area located at Andhra Pradesh, India. When input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices.
The document discusses the advantages and limitations of using mathematical models to estimate indoor air quality and support risk assessments. It provides an overview of commonly used indoor air quality models and notes that while models are useful, they all have limitations and uncertainties. The document summarizes studies evaluating the reliability of the Advanced Reach Tool (ART) and IHMOD Two-Zone models, finding that both can provide reasonable exposure estimates but also show variability between assessors. It emphasizes the importance of model selection, validation, and considering actual air monitoring data.
This document discusses machine learning techniques for actuarial science, including supervised learning methods like linear regression, generalized linear models (GLMs), generalized additive models (GAMs), elastic net, classification and regression trees (CART), random forests, boosted models, and stacked ensembles. It also briefly mentions deep learning techniques like multi-layer perceptrons, convolutional neural networks, and recurrent neural networks, as well as natural language processing applications like word2vec. Key advantages and disadvantages of each method are summarized.
This document discusses unsupervised learning techniques including principal component analysis (PCA), generalized low rank models (GLRM), K-means clustering, and deep learning autoencoders. PCA reduces dimensionality by identifying principal components that explain the most variance in the data. GLRM generalizes PCA to work with mixed data types. K-means clustering groups similar observations to identify homogeneous clusters. Autoencoders can detect anomalies through reconstructing input data. The document provides examples of applying these techniques to vehicle insurance data.
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Final intro use of economic methods for injury prevention resource allocation John Wren
This 2010 paper was developed to address a range of information needs for the NZ Injury Prevention Secretariat, in particular:
1) a review of the health economics models and associated issues that must be understood when undertaking cost of injury studies
2) a review of the published New Zealand cost of injury studies to identify the methods utilised, and the size of the cost estimates calculated for various injury events
3) calculated a new total social and economic cost of injury estimate for all injuries and the six injury priority areas respectively, drawing upon the lessons learnt from the reviews undertaken
4) briefly reviewed the ways in which economic methods can be used to inform injury prevention investment decisions, and made recommendations for their use in New Zealand
5) drew conclusions and made recommendations about undertaking future cost of injury work to provide both better standardisation in approach and greater cost discrimination between injury areas.
Cost utility analysis of interventions to return employees to work following ...ScHARR HEDS
The document describes the Health Economics and Decision Science (HEDS) section within the School of Health and Related Research (ScHARR) at the University of Sheffield. HEDS conducts research to promote excellence in healthcare resource allocation and supports the implementation of research results through education and training. Its research portfolio includes areas like evidence synthesis, health economics modeling, and more. HEDS also offers post-graduate programs and short courses. It focuses on a wide range of disease areas and provides consultancy services including modeling for clinical trials and health economics analyses. The decision modeling team at HEDS specializes in cost-effectiveness analyses to support healthcare decision making.
This document describes a 9-step framework for evaluating the expected cost-effectiveness of a service intervention at the design stage. It applies this framework to evaluate an intervention to improve clinical handovers between hospital and community care.
The key steps are: 1) Identifying endpoints and grouping them, 2) Estimating baseline risks, 3) Eliciting expected effectiveness from experts, 4) Assigning utility values to endpoint groups, 5) Costing the intervention, 6) Estimating healthcare costs of adverse events, 7) Calculating health benefits, 8) Determining cost-effectiveness, and 9) Conducting sensitivity analysis.
When applied to a handover improvement intervention, literature suggested adverse events follow 19%
This document provides definitions for economic and decision-making terms used in health economics. It defines over 30 terms across multiple categories, including types of economic analysis (e.g. cost-benefit analysis, cost-effectiveness analysis), costs (e.g. direct costs, indirect costs), decision analysis tools (e.g. decision trees, Markov models), and measures of health outcomes (e.g. quality-adjusted life years, disability-adjusted life years). For each term, it provides a brief 1-2 sentence definition of its meaning in the context of health economic evaluations.
Forecasting Municipal Solid Waste Generation Using a Multiple Linear Regressi...IRJET Journal
- The document describes developing a multiple linear regression model to forecast municipal solid waste generation based on factors like population, population density, education levels, access to services, and income levels.
- The model was developed using data from various municipalities in Italy. Exploratory data analysis was conducted to determine linear relationships between waste generation and predictors.
- The linear regression model achieved a high R-squared value of 91.81%, indicating a close fit to the data. Various error metrics like MAE, MSE and RMSE were calculated to evaluate model performance.
- The regression model provides a simple yet accurate means of predicting municipal solid waste that requires minimal data and can be generalized to other locations.
EWMA 2013 - Ep503 - Quantifying the economic value of diagnostics in wound ca...EWMAConference
The document summarizes an economic model that evaluated the potential cost savings of using a point-of-care test to identify chronic wounds with elevated protease activity (EPA) and targeting protease modulating treatment for those wounds. The model estimated potential savings of £1,906 per EPA wound identified, totaling over £50,000 in savings for every 100 chronic wounds tested. By enabling targeted treatment, the model found that 19 additional wounds could heal while freeing up 556 clinical episodes of care. Therefore, implementing a "test and treat" algorithm using this diagnostic was deemed a cost-effective dominant strategy compared to standard care alone.
ENGINEERING SOLUTIONS TO REDUCE VULNERABILITIES IN BIOTECHNICAL SYSTEMSIAEME Publication
To assess the clinical significance of electrophysiological, functional, and
morphometric studies of the optic nerve in patients with serous meningitis during the
recovery period. Congestive fundus changes during the follow-up observation period in
patients with serous meningitis were diagnosed more often 19.4% (6) compared with
changes in the fundus in the acute period 13% (4)). Analysis of perimetric indices
revealed a significant decrease in photosensitivity in 80% (8) patients with serous
meningitis during the recovery period compared with the control group. According to
optical coherence tomography in patients with serous meningitis, significant changes
were detected in 90% (9) patients. According to the data of visual evoked potentials and
electroretinography in patients with serous meningitis, a deterioration in visual
afferentation was detected.
This study evaluated the lateral tarsal strip technique for correcting lower eyelid ectropion in 30 patients (41 eyelids). Patients were divided into groups based on the type of ectropion: involutional, paralytic, or cicatricial. Most patients presented with tearing. The technique successfully corrected ectropion in 35 eyelids, while 6 required additional procedures. The lateral tarsal strip proved effective for correcting ectropion, especially when lateral canthal tendon laxity is present.
Effective strategies to monitor clinical risks using biostatistics - Pubrica....Pubrica
In clinical science, biostatistics services are essential for data collection, analysis, presentation, and interpretation. Epidemiology, clinical trials, population genetics, systems biology, and other disciplines all benefit from it. It aids in the evaluation of a drug's effectiveness and safety in clinical trials.
Continue Reading: https://bit.ly/3tRRxkW
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44 1618186353
The article reviews recent research on tourism demand modelling and forecasting published since 2000. It finds that forecasting methods have diversified beyond econometric models to include new techniques. However, no single model consistently performs best in all situations. The article identifies new research areas like integrating quantitative and qualitative forecasts. It also notes that seasonality analysis is commonly used given tourism's seasonal demand patterns. The article concludes by encouraging further research to improve forecast accuracy, such as combining models and focusing on turning point predictions.
The article reviews recent research on tourism demand modeling and forecasting published since 2000. It finds that forecasting methods have diversified beyond econometric models to include new techniques. However, no single model consistently outperforms others in accuracy. The article also identifies new research areas like integrating qualitative and quantitative forecasts. It notes that seasonality analysis is commonly used given tourism's seasonal demand patterns. Forecasters prefer seasonal models to help businesses optimize for demand peaks and valleys. While widely used, opportunities remain to improve seasonal modeling techniques and forecast accuracy overall.
Application of consistency and efficiency test for forecastsAlexander Decker
This document evaluates the forecasting efficiency of food price inflation, consumer price index, GDP per capita, and money supply data from Pakistan from 1975 to 2008. It uses ARIMA models to generate forecasts, which are then evaluated for consistency and efficiency. Consistency tests whether the actual and forecasted values are cointegrated and have the same order of integration. Efficiency tests examine whether forecasts minimize forecast errors and fully incorporate available information. The study finds that food price forecasts are consistent and efficient based on these criteria.
Cost Effectiveness Analysis in Health economicsGerardo García
This document summarizes a web-based seminar on using cost-effectiveness analysis as a decision support tool for employers. It defines cost-effectiveness analysis as a method to compare different health interventions or programs based on their costs and outcomes. The document provides examples of how cost-effectiveness analysis can help employers make informed decisions about benefits programs and coverage options by objectively evaluating alternatives in a standardized way. It also discusses strategic considerations for interpreting and applying cost-effectiveness analyses to support evidence-based decision making.
This study evaluates project cost estimation techniques by analyzing their accuracy under different project conditions. The objectives are to assess the performance of various techniques, understand factors influencing estimation accuracy, and develop new approaches. The research methodology involves collecting primary and secondary data, descriptive and inferential statistical analysis of survey data, and qualitative meta-synthesis of stakeholder interviews. Key findings suggest improving data quality, risk management, and integrating estimation with project management software. The conclusion is that accurate cost estimation is critical for project success and selecting the right techniques is important.
This document presents a risk assessment of an investment project to build a financial complex and business center at Tecnológico de Antioquia University. A Monte Carlo simulation was used to model the net present value and internal rate of return based on the variability of estimated cash flows. The simulation found a 44% chance that the NPV is between $0-51,680 and a 51% chance it is below $0, with an IRR between 7.47%-12.22%. While the financial assessment is positive, the project's main benefit is providing students, teachers and entrepreneurs experience with financial transactions, negotiations and simulations.
This document summarizes a review of 80 systematic reviews on the effectiveness of telemedicine. The reviews covered a range of telemedicine interventions and clinical areas. Some key findings from the reviews included:
- 21 reviews concluded that telemedicine is effective in improving outcomes.
- 18 reviews found evidence that telemedicine is promising but more research is still needed.
- Other reviews found evidence of telemedicine's effectiveness is limited and inconsistent.
- Economic analyses of telemedicine were often problematic and inconclusive.
- Some reviews highlighted benefits of telemedicine for patients, such as improved access to care.
OS20 - A methodology to estimate indirect costs associated with a possible ...EuFMD
The document presents a methodology for estimating indirect costs associated with potential outbreaks of transboundary animal diseases (TADs) in Switzerland. The methodology involves collecting qualitative and quantitative data through literature reviews, stakeholder interviews, and disease control legislation to incorporate into an economic model. An economic model including a cost calculator and decision tree model is used to estimate the cost implications of different disease control options and the impact of uncertainty. This methodology allows identification and characterization of indirect consequential costs, which are often more complex but potentially more significant than direct costs. The decision tree model specifically helps select the optimal disease control policy. This methodology could be applied to estimate costs of other TAD outbreaks.
EVALUATION OF REFERENCE EVAPOTRANSPIRATION ESTIMATION METHODS AND DEVELOPMENT...IAEME Publication
This study is an attempt to find best alternative method to estimate reference evapotranspiration (ETo) for the Nagarjuna Sagar Reservoir Project [NSRP], command area located at Andhra Pradesh, India. When input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices.
The document discusses the advantages and limitations of using mathematical models to estimate indoor air quality and support risk assessments. It provides an overview of commonly used indoor air quality models and notes that while models are useful, they all have limitations and uncertainties. The document summarizes studies evaluating the reliability of the Advanced Reach Tool (ART) and IHMOD Two-Zone models, finding that both can provide reasonable exposure estimates but also show variability between assessors. It emphasizes the importance of model selection, validation, and considering actual air monitoring data.
Similar to Actuarial modeling of general practictioners' drug prescriptions costs (20)
This document discusses machine learning techniques for actuarial science, including supervised learning methods like linear regression, generalized linear models (GLMs), generalized additive models (GAMs), elastic net, classification and regression trees (CART), random forests, boosted models, and stacked ensembles. It also briefly mentions deep learning techniques like multi-layer perceptrons, convolutional neural networks, and recurrent neural networks, as well as natural language processing applications like word2vec. Key advantages and disadvantages of each method are summarized.
This document discusses unsupervised learning techniques including principal component analysis (PCA), generalized low rank models (GLRM), K-means clustering, and deep learning autoencoders. PCA reduces dimensionality by identifying principal components that explain the most variance in the data. GLRM generalizes PCA to work with mixed data types. K-means clustering groups similar observations to identify homogeneous clusters. Autoencoders can detect anomalies through reconstructing input data. The document provides examples of applying these techniques to vehicle insurance data.
This document discusses machine learning concepts and applications in actuarial science. It introduces common machine learning algorithms like supervised learning algorithms for regression and classification, as well as unsupervised learning algorithms for clustering and dimensionality reduction. Examples of machine learning use cases in actuarial areas like pricing, claims reserving, and marketing are provided. The document also outlines best practices for machine learning projects including defining the business scope, data preparation, modeling, validation, and deployment. Specific algorithms like linear regression, decision trees, support vector machines, and K-nearest neighbors are explained. Tools for machine learning like H2O and interpretability techniques are also summarized.
Giorgio Alfredo Spedicato will give a presentation on machine learning and actuarial science. He will review machine learning theory, including unsupervised and supervised learning algorithms. He will provide examples using various datasets, including using unsupervised learning on an auto insurance dataset and supervised learning for credit scoring and claim severity prediction. Spedicato has experience as a data scientist and actuary and holds a PhD in Actuarial Science.
Meta Analysis Essentials provides an overview of meta-analysis techniques. Meta-analysis allows researchers to synthesize and aggregate results from different studies on the same topic to obtain more consistent statistics. It requires restructuring collected study materials. Meta-analyses typically compare one or two groups and estimate an effect size for each study. Estimates can be obtained using standard, random effects, or mixed effects models, with the latter two accounting for potential heterogeneity across studies. Conducting a meta-analysis requires input data on study details, measures, and results to estimate effect sizes and assess heterogeneity. Tools like R can be used to perform the analyses and generate outputs.
This document discusses using the markovchain package in R to model and analyze long-term care (LTC) insurance policies. It presents transition probabilities for disabled, ill, and dead states for Italian males. The package is used to simulate life trajectories and cash flows to calculate policy premiums and reserves. Simulation allows for stochastic analysis of benefits and reserves over many simulated policyholder lives.
The markovchain R package provides tools for creating, representing, and analyzing discrete time Markov chains (DTMCs). It allows users to easily define DTMC objects, perform structural analysis of transition matrices, estimate transition matrices from data, and simulate stochastic sequences from DTMCs. The package aims to make working with Markov chains straightforward for R programmers through S4 classes and methods.
This document discusses traditional and alternative insurance options for catastrophe risks such as hurricanes, earthquakes, and floods. It provides details on:
- Traditional reinsurance and its limitations in fully meeting coverage demands, leading to the growth of alternative options like catastrophe (CAT) bonds.
- How CAT bonds work, including being issued by a special purpose vehicle to provide reinsurance coverage to primary insurers and allow access to capital markets.
- The components and uses of CAT models, which are computer simulations used to analyze catastrophe risk and loss exposures in property portfolios. CAT models help price coverage and allocate capital.
- Key considerations for primary insurers in deciding between traditional reinsurance or CAT bonds to transfer catastrophe
This document discusses important IT skills for actuaries to have. It recommends that actuaries have general skills in operating systems, Microsoft Office programs like Word and Excel, and have knowledge of databases and programming. Specifically, it suggests actuaries will write VBA code and know SQL. Statistical software mentioned includes SAS, R, and Python, with R gaining more acceptance. Professional actuarial software referenced includes programs from Willis Tower Watson for life and non-life insurance modeling and catastrophe modeling software. Examples given are using R for life and health insurance applications.
1. Actuaries are professionals who quantify and price financial risks for insurance companies. They determine premiums, reserves, and economic capital.
2. Becoming an actuary requires passing examinations in probability, mathematics, statistics, and finance. The requirements vary by country but it is a regulated profession everywhere.
3. Actuaries work on pricing and reserving for various insurance products including general insurance, life insurance, health insurance, and pensions. They also work in reinsurance, catastrophe modeling, and other non-traditional roles applying statistical skills.
This document discusses using R packages to model and simulate annuities while accounting for variability and uncertainty. It introduces the lifecontingencies package for actuarial calculations and describes collecting demographic and financial data. Baseline assumptions are defined and the Lee-Carter model is used to project mortality. Variability is assessed through simulating process variance in lifetimes and annuity present values, and parameter variance by generating random life tables. Interest rates are modeled with Vasicek dynamics and inflation is linked to interest rates through cointegration. The overall algorithm simulates annuity distributions by projecting cash flows over simulated lifetimes and interest/inflation rates.
This document discusses using machine learning techniques like logistic regression and random forests to build insurance retention models. It analyzes a dataset of 50,000 insurance policies to predict renewal probabilities and optimize renewal premiums. Logistic regression performs nearly as well as more complex methods like flexible discriminant analysis. The document also provides technical details on using R packages like caret and rms to fit and evaluate various predictive models for classification tasks.
These slides introduce the lifecontingencies R package functionalities. Pricing, reserving and simulating life contingent insurance will be shown. Similarly, joining Lee Carter mortality projections with demography R package and annuities evaluation with lifecontingencies R package is shown. The work has been all done with R markdown.
- The document proposes using Generalized Additive Models for Location, Scale and Shape (GAMLSS) as an alternative methodology for claims reserving to provide both a point estimate and a measure of uncertainty.
- It describes how GAMLSS allows modeling of multiple distribution parameters, such as modeling the variance of incremental payments as a function of development year, to better fit the data.
- Numerical results on an example claims triangle show that GAMLSS provides a better fit than classical generalized linear models as measured by a lower GAIC, and results in a lower estimated variability for claims reserves.
This document discusses using R to price different types of insurance contracts. It provides examples of pricing life insurance, personal lines insurance, and excess of loss reinsurance contracts. For each type of insurance, it shows how to model costs and losses in R, calculate key metrics like expected claims and capital requirements, and determine final premiums. Code used in the examples is provided in an appendix.
This document discusses modeling underwriting premium risk for motor third party liability (MTPL) insurance under Italy's direct compensation (CARD) system. It provides an overview of the CARD system and the challenges it poses for pricing and risk modeling. Specifically, it notes that negative claim amounts are possible under CARD, the frequency and costs of both caused and suffered claims must be modeled, and historical experience is limited. It then describes the CARD forfeit structure and rules in more detail. The goal is to develop an internal model for assessing underwriting premium risk capital charges on an MTPL portfolio under the CARD system.
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
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Actuarial modeling of general practictioners' drug prescriptions costs
1. Introduction The methodology An empirical application Conclusions
An actuarial model for assessing general
practitioners’ prescribing costs
Simona C. Minotti and Giorgio A. Spedicato
Universit` degli Studi di Milano-Bicocca
a
Universit` degli Studi “La Sapienza” di Roma
a
September 13, 2011
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
2. Introduction The methodology An empirical application Conclusions
Table of contents
1 Introduction
2 The methodology
3 An empirical application
4 Conclusions
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
3. Introduction The methodology An empirical application Conclusions
Outline
1 Introduction
2 The methodology
3 An empirical application
4 Conclusions
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
4. Introduction The methodology An empirical application Conclusions
Introduction
The reduction of public financial resources makes the monitoring of
health care expenditures relevant. An important issue for the
efficient allocation of health care resources is monitoring costs of
general practitioners drug prescriptions.
However, literature on this topic is very scarce and almost
exclusively based on linear regression models (see e.g.
[Wilson-Davis and Stevenson, 1992], [Simon et al., 1994]) or panel
data econometric models (see e.g. [Garcia-Goni and Ibern, 2008]).
We propose an actuarial methodology, which is based on three
approaches typical of non-life actuarial statistics, in order to
estimate the distribution of the yearly total cost of prescription drugs
for general practitioners, given the characteristics of their patients.
This can be useful for planning and budgeting health care resources.
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
5. Introduction The methodology An empirical application Conclusions
Outline
1 Introduction
2 The methodology
3 An empirical application
4 Conclusions
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
6. Introduction The methodology An empirical application Conclusions
First approach: Collective risk theory
The distribution of the total cost of claims arising from an insurer
portfolio is typically expressed by means of a convolution of claim
frequency and claim cost (see e.g. f[Savelli and Clemente, 2010]).
˜
The yearly total cost, T , of prescription drugs for a given general
practitioner can be seen as a stochastic variable. We propose to
model the distribution of this variable as a convolution of yearly
˜
single patients’ costs ti , i = 1, ...N:
N
˜
T = ˜i .
t
i=1
The yearly cost of prescription drugs, ˜i , for patient i depends on
t
both the number and the cost of single prescription drugs and
therefore can be written as a convolution of single costs cij ,
˜
j = 1, ...˜i , in a given year:
n
˜i =
t j=0,1,...,˜i
n cij .
˜
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
7. Introduction The methodology An empirical application Conclusions
Second approach: GAMLSS
In property and casualty actuarial practice it is usual to model
claim frequency and claim cost by means of GLMs, in order to
set the price of insurance coverages. [Anderson et al., 2007]
applies Generalized Additive Models for Location, Scale and
Shape (GAMLSS) (see [Rigby and Stasinopoulos, 2005]),
which allows to model parameters other than the mean.
In our proposal frequency ni and cost of drug prescriptions cij
˜ ˜
are modelled by means of GAMLSS as functions of i-th
patient characteristics, as formula 1 shows.
E [˜i ] = f1 (¯i )
n x
var [˜i ] = f2 (¯i )
n x
(1)
E [˜i ] = f3 (¯i )
c x
var [˜i ] = f4 (¯i )
c x
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
8. Introduction The methodology An empirical application Conclusions
Second approach: GAMLSS
A negative binomial marginal distribution is chosen for
1
1
Γ(y + σ ) y
σµ 1 σ
ni ∼ NBI (µ, σ) = Γ 1 Γ(1+y ) 1+σµ
˜ 1+σµ
(σ)
while a inverse gaussian marginal distribution for
1 −1
y
y σ2 exp −
1 σ2 µ
cij ∼ IG (µ, σ) =
˜ 1 1
(σ 2 µ) σ2 Γ
σ2
The specific marginal distribution have been chosen as to
maximize goodness of fit according to normalized quantile
residuals criterion ([Dunn and Smyth, 1996]).
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
9. Introduction The methodology An empirical application Conclusions
Third approach: models for lapse probability and
conversion rate
These models are widely applied in actuarial practice in order to
predict customer churn and conversion, given that an insurer
portfolio represents an open collectivity (see e.g.
[Geoff Werner and Claudine Modlin, 2009]).
During a year, a patient can leave the general practitioner for death
or other reasons, as well as a new patient can arrive.
The effective period at risk for patient i is simulated as follows:
1 a drop out event is simulated using a Bernoulli distribution;
2 a new entrant event is simulated using a Poisson distribution;
3 the fractional exposure periods for drop outs and new patients
are drawn from a U (0, 1) distribution
We propose to model the expected number of drug prescriptions by
an equation where the exposure ln(ei ) is inserted as an offset term
in the link function.
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
10. Introduction The methodology An empirical application Conclusions
The estimation procedure
Parameters of the predictive models for the distributions of ni
˜
and ci are estimated by means of GAMLSS regression models,
˜
assuming Negative Binomial and Inverse Gaussian marginal
distributions respectively.
The systematic relationship between dependent variables and
covariates has been assessed using penalized splines in order
to take into account non linear relationships.
Parameters of model for the stochastic period at risk ei are
˜
estimated using a convolution of a Bernulli (for the probability
to drop out or conversion) and uniform distribution. The
analysis has been separately carried out for drop outs and
conversion.
This part of the model permit to obtain the expected value
˜ ˜
and the variance of ti , but we wish to simulate T .
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
11. Introduction The methodology An empirical application Conclusions
The estimation procedure
˜ ˜
Distributions of ti and T are obtained by Monte Carlo simulation.
˜
A random realization from distribution of the yearly cost ti for
patient i can be generated by means of the following algorithm:
1 Select the number, k, of prescription drugs at random from
the distribution of the frequency ni of prescription drugs.
˜
2 Do the following k times. Select the cost, z, of prescription
drugs at random from the distribution of the cost cij of
˜
prescription drugs.
3 The total cost, ˜i , for patient i is the sum of the k costs,
t
z1 , z2 , ..., zk .
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
12. Introduction The methodology An empirical application Conclusions
The estimation procedure
If the outlined process is repeated for all N patients of the
general practitioner’s portfolio, we obtain a random realization
˜
from the distribution of the yearly total cost T .
˜ ˜
Finally, in order to obtain the distributions of ti and T it is
necessary to repeat the previous steps M times (M >> 0).
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
13. Introduction The methodology An empirical application Conclusions
Outline
1 Introduction
2 The methodology
3 An empirical application
4 Conclusions
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
14. Introduction The methodology An empirical application Conclusions
Data sources
A dataset containing information about medicals of 6,000
patients, that is: number of medicals, plus a wide choice of
demographic data. This dataset is used to calibrate the model
for the frequency ni of prescription drugs.
˜
A dataset in the same format of the previous one, containing
demographic data about 600 patients belonging to a certain
general practitioner. This dataset is used to simulate the
number of prescriptions for this general practitioner and
˜
therefore to asses the distribution of the yearly total cost T of
prescription drugs.
A dataset collected by ourselves, containing information about
400 prescriptions, that is: costs of prescribed drugs, sex and
age of patients. This dataset is used to calibrate the model
for the cost cij of prescription drugs.
˜
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
15. Introduction The methodology An empirical application Conclusions
Data sources
A life table, split by sex for last available year, that gives the
probability of death of a subject.
A univariate life table collected by ourselves from unofficial
interviews with general practitioners, that gives the probability
of drop-out for reasons other than death (lapse probability).
A univariate life table collected by ourselves, that gives the
rate of new entries (conversion rates).
The provided data sources have been collected for illustrate
the model. Data bases already available to public agencies
can be used to build more effective models.
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
16. Introduction The methodology An empirical application Conclusions
GAMLSS model for ni
˜
model plot.png
Figure: Frequency assessment
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
17. Introduction The methodology An empirical application Conclusions
GAMLSS model for ci
˜
model plot.png
Figure: Cost assessment
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
18. Introduction The methodology An empirical application Conclusions
GAMLSS fitting
Figure: Drug prescriptions cost model fit
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
19. Introduction The methodology An empirical application Conclusions
GAMLSS models discussion
The frequency GAMLSS model in figure 1 shows that factors
affecting number of prescriptions are: sex (female more than
males), age (positive effect), income (negative effect) and
handicap percentage (positive effect).
The cost GAMLSS model in figure 2 shows that the cost of
prescriptions follow a non - linear behaviour and that depends
only by age. The increase of sample size may lead to more
consistent results.
The Normalized Quantile Residual plot 3 of drug prescriptions
shows that the hypnotised model fit well on data. A good
result has been also found in the assessment of the number of
prescriptions.
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
20. Introduction The methodology An empirical application Conclusions
˜
Total loss T simulation results
˜
T distribution can be obtained by Monte - Carlo simulation as
previously described.
˜
However simulating T using Monte - Carlo approach is
computationally long.
Log-Normal distribution shows to approximate fairly well
˜
simulated T behaviour, as shown is 4.
Log-Normal approximation makes more practical the
˜
assessment of T .
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
21. Introduction The methodology An empirical application Conclusions
Log-Normal approximation
cost fit.png
Figure: Total loss fit
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
22. Introduction The methodology An empirical application Conclusions
Log-Normal approximation
cost lognormal.png
Figure: Total loss fit
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
23. Introduction The methodology An empirical application Conclusions
Outline
1 Introduction
2 The methodology
3 An empirical application
4 Conclusions
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
24. Introduction The methodology An empirical application Conclusions
Discussion of results
The proposed approach shows that:
Statistical techniques typical of actuarial practice can successfully
be applied to a health economic problem.
The availability of administrative data makes possible to apply the
proposed methodology to real cases.
Suggested extensions are:
Multi year projections should be considered, in order to evaluate
multi-year costs of drug prescriptions
The data set used to calibrate the model shall be chosen with care.
The inclusion of general practitioners’ characteristics in the model
could improve explicative and predictive power of the model.
Minotti, S.C., Spedicato G.A. CLADAG 2011, 7-9 September 2011, Universit` degli Studi di Pavia
a
An actuarial model for assessing general practitioners’ prescribing costs
25. Introduction The methodology An empirical application Conclusions
Bibliography
Anderson, D., Feldblum, S., Modlin, C., Schirmacher, D., Schirmacher, E., and Thandi, N. (2007).
A practitioner’s guide to generalized linear models.
Technical report, Casualty Actuarial Society.
Dunn, P. and Smyth, G. K. (1996).
Randomized quantile residuals.
J. Computat. Graph. Statist, 5:236–244.
Garcia-Goni, M. and Ibern, P. (2008).
Predictability of drug expenditures: an application using morbidity data.
Health Econ, 17:119–126.
Geoff Werner and Claudine Modlin (2009).
Basic Ratemaking.
Rigby, R. and Stasinopoulos, M. (2005).
Generalized additive models for location, scale and shape,(with discussion).
Applied Statistics, 54:507–554.
Savelli, N. and Clemente, G. (2010).
Hierarchical structures in the aggregation of premium risk for insurance underwriting.
Scandinavian Actuarial Journal.
Simon, G., Francescutti, C., Brusin, S., and Rosa, F. (1994).
Variation in drug prescription costs and general practitioners in an area of north-east italy. the use of current
data.
Epidemiol Prev, 18:224–229.
Wilson-Davis, K. and Stevenson, W. G. (1992).
Predicting prescribing costs: A model of northern ireland 2011, 7-9 September 2011, Universit` degli Studi di Pavia
Minotti, S.C., Spedicato G.A. CLADAG general practices. a
Pharmacoepidemiology and Drug Safety, 1(6):341–345.
An actuarial model for assessing general practitioners’ prescribing costs