66 methods
This method uses machine learning to predict how likely patients with a rare blood cancer (T-cell prolymphocytic leukemia) are to develop a serious complication (acute graft-versus-host disease) after a stem cell transplant. Because the disease is rare, there's not much data available, which makes predictions harder. The study tested different machine learning models and found that Linear Discriminant Analysis worked best for predicting whether the complication would occur, though it struggled when trying to predict how severe the complication would be.
MethodPredictive ModellingML/AIRWEThis method helps estimate how introducing a new health technology will affect government budgets over time. It looks at costs and savings across different sectors, especially public finances, to show the overall fiscal impact.
ToolCosting & Resource UseSocietal ValueThis framework identifies and organizes the key factors that influence health technology assessment (HTA) recommendations across different countries and settings. It is based on analysis of real-world data from HTA agencies and helps understand why decisions on funding or coverage of health technologies differ internationally.
MethodAppraisalA cost-effectiveness model that integrates individual patient-level data and predicts personalised costs and effects.
MethodRWEHeterogeneityA checklist to help determine if an Oblique Meta-Analysis (OBMEA) is feasible when evaluating health technologies in rare diseases, where data is often limited.
ChecklistPricing/PayerCHEERS-AI is a checklist that helps researchers clearly report economic evaluations of health interventions that use artificial intelligence. It builds on existing standards by adding AI-specific details so that decision makers can better understand how the AI works and how it affects costs and effectiveness.
ToolReporting Standards & Best PracticeClinFlow is an interactive tool that helps clinical researchers analyze and visualize medical data without needing programming or advanced statistics skills. It supports exploration of patient data to generate insights and test ideas, especially in disease patterns and patient outcomes.
Web-based AppData MiningML/AIData Exploration & VisualizationCohortMethod is a software tool that helps researchers compare the effects of different treatments using real-world patient data stored in a standard format. It focuses on studies that look at people who are starting a treatment for the first time, and it uses advanced statistics to reduce bias when analyzing outcomes like disease progression or side effects.
Software PackageRWEPredictive ModellingA standard set of outcomes for diabetes care that includes both clinical measures and patient-reported experiences, developed with input from people with diabetes, healthcare providers, and other stakeholders across Europe. It helps ensure that care is person-centred and consistently measured.
ToolPROsPROMsA standard set of health outcomes and measurements for inflammatory bowel disease (IBD) has been developed to improve patient care and support research. It includes key variables that should be measured in clinical practice, such as symptoms, quality of life, and lab tests, with specific tools recommended for patients to report their experiences.
ToolPROsPROMsThis is a standardized set of outcomes that matter most to lung cancer patients and healthcare providers. It includes patient-reported experiences, quality of life, symptoms, and clinical data to help measure the true impact of treatments and improve care across Europe.
ToolPROsPROMsA standardized list of outcomes that matter most to patients with metastatic breast cancer, especially related to quality of life, symptoms, and treatment impact. It helps ensure that studies and care focus on what's important to patients.
ToolPROsPROMsA standardized set of outcomes that should be measured and reported in all clinical studies and practice for multiple myeloma, developed with input from patients and healthcare professionals to ensure relevance and consistency.
ToolPROsPROMsCross-NMA/NMR is a set of advanced statistical models that combine results from different types of studies, including randomized trials and non-randomized studies, as well as individual patient data and aggregate data, to give a more complete picture of how well treatments work.
MethodEvidence SynthesisIndirect ComparisonsRWEThe crossnma R package helps researchers combine different types of study data—like results from randomised trials and observational studies, as well as individual and summary-level data—to compare multiple treatments using advanced statistical methods.
Software PackageEvidence SynthesisRWEData quality rules to assess health data across its lifecycle in terms of completeness, consistency, correctness, and other relevant quality dimensions.
MethodData Quality AssessmentData GovernanceStandardisationThe DataQuality Dashboard (DQD) is a tool that helps check and improve the quality of health data before it is used for research. It is especially useful when converting data into a standard format called the OMOP Common Data Model (CDM). The tool identifies errors in how data is structured, checks if important information is missing, and detects unlikely or impossible values. Using the DQD helps ensure that databases meet basic quality standards for research use.
MethodRWEThe DataQualityDashboard R package helps researchers check the quality of real-world health data that has been converted into a standard format (OMOP CDM). It runs over 3,300 automated checks to find possible data problems, so users can trust the results they get from analyzing this data.
Software PackageRWEThis method helps doctors and patients choose the best treatment when there are several options by showing which strategy—like using a personalized prediction model or treating everyone the same way—leads to better outcomes across different patient preferences.
MethodDecision Curve AnalysisIndirect ComparisonsThis R package helps researchers build and test deep learning models to predict individual patient outcomes using real-world health data stored in a standard format called OMOP CDM.
Software PackageRWEPredictive ModellingThe DICE Modelling Platform is an online tool that helps users create and run health economic models using a structured simulation framework. It provides templates, software tools, examples, and training to support model development.
ToolCost-effectiveness ModellingThis is a set of values that helps measure how different health conditions affect the quality of life of children and adolescents, specifically from the perspective of a 10-year-old child. It was developed for use in Slovenia but is the first such set available globally for the EQ-5D-Y-3L questionnaire.
Value setHRQoLXAI models help predict health outcomes by using machine learning methods that show how decisions are made, making it easier for doctors and patients to understand and trust the results.
MethodPredictive ModellingML/AIThis framework shows how health technology assessment (HTA) methods can help overcome challenges in reusing existing drugs for new diseases. It identifies common problems in drug repurposing and suggests when and how HTA tools can be used early in the process to improve success.
MethodDrug RepurposingThe GetReal Trial Tool is an online tool that helps researchers design clinical trials that better reflect real-world patient care. It allows users to see how different trial design choices affect how applicable the results will be to everyday medical practice, while also considering reliability, accuracy, and practical challenges.
ToolRWEThis guideline provides clear definitions and standardized methods for calculating healthcare costs in economic evaluations within the European Union. It aims to improve consistency and comparability across studies.
GuidelineCosting & Resource UseThis guideline helps researchers and decision-makers improve the quality and consistency of economic evaluations for personalised medicines. It offers practical advice on how to model the value of these treatments, especially when evidence is limited or uncertain.
GuidelineCost-effectiveness ModellingGuidance on use and implementation of outcome-based managed entry agreements for rare disease treatments including considerations for successful implementation.
GuidelinePricing/PayerThis guidance explores how outcomes-based managed entry agreements (OBMEAs) can be used for rare disease treatments, using real-world examples like nusinersen and tisagenlecleucel. It highlights best practices for setting up these agreements, such as clear outcome measures, data collection systems, and stakeholder collaboration, while cautioning that OBMEAs are complex and should only be used when necessary.
GuidelinePricing/PayerThis document provides recommendations on how to incorporate Patient-Reported Outcome Measures (PROMs) when assessing the effectiveness and impact of health technologies for rare diseases, ensuring patient perspectives are included in decision-making.
GuidelinePROMsHRQoLA structured approach to assess the value and impact of treatments for rare diseases, considering clinical benefits, costs, safety, and patient experiences.
GuidelineAppraisalA structured method to assess health technologies by combining multiple factors like effectiveness, cost, patient impact, and broader societal values into an overall value judgment.
MethodAppraisalThe information provided does not describe the purpose or function of the INSAFEDARE data pipeline platform, and no meaningful summary can be generated from the source text.
ToolSynthetic DataPAID 4.0 is a web-based tool that helps researchers estimate future healthcare costs when evaluating medical treatments. It calculates expected annual healthcare spending for individuals based on their age, sex, and how close they are to death, allowing for more accurate long-term cost predictions in health studies.
ToolCosting & Resource UseUnrelated Future CostsA single value set that combines health state preferences from multiple European countries to estimate quality of life for use in health technology assessments across Europe.
Value setHRQoLThis is a form that patient groups can use to share real-life experiences about a rare disease treatment that was accessed through an OBMEA (a type of early access program). It helps describe how patients’ lives changed before and after treatment, and highlights any practical challenges during the access period. HTA bodies can adapt this form or use it as a guide for interviews or focus groups when re-evaluating the treatment.
TemplatePricing/PayerAn R package that builds and evaluates predictive models for individual patients using observational health data structured in the OMOP Common Data Model. It helps predict whether a patient will experience a specific health outcome based on their past medical history.
Software PackageRWEPredictive ModellingThe Pay for Innovation Observatory is an online platform that collects and presents information about different pricing and payment models used for health innovations around the world.
ToolPricing/PayerThe PECUNIA Reference Unit Cost (RUC) Compendium is a database that provides standardized unit costs for services and resources used across multiple sectors—like health, social care, education, justice, employment, and informal care—in several European countries. These costs are designed to be comparable across countries and sectors, supporting economic evaluations from a societal perspective.
ToolCosting & Resource UseThe PECUNIA RUC Templates help standardize how costs of health and social services are calculated across different countries and sectors in Europe. They make it easier to compare costs and use reliable data in health economic studies.
ToolCosting & Resource UseThe PECUNIA RUM is a tool designed to measure how resources are used across different sectors—like healthcare, social care, and education—when providing health-related services in Europe. It helps researchers collect accurate data on what services people use so they can estimate costs more reliably in health economic studies.
ToolCosting & Resource UseSocietal ValueThe PROM-select app helps researchers and healthcare professionals find the best patient-reported outcome measures (PROMs) for their studies or clinical practice. It provides guidance on selecting valid and reliable questionnaires that capture patients' perspectives on their health and treatment.
Web-based AppPROMsRecommendations outlining key actions for the reimbursement of personalised medicines and proposed timeframes for their implementation.
GuidelinePricing/PayerThis document provides guidance on choosing appropriate statistical methods for pragmatic clinical trials, which are studies that test treatments in real-world settings. It helps researchers ensure their analyses are valid and meaningful when applied to everyday clinical practice.
GuidelineRWERecommendations regarding the design, analysis, and reporting of non-randomised studies (NRSs).
GuidelineRWEAn artificial intelligence–driven tool for identifying, selecting, and assessing registries suitable for research, regulatory, and health technology assessment (HTA) purposes.
ToolRWERegistry IdentificationData MappingThis tool helps regulators and researchers assess how well safety measures for medicines work after they are approved. It uses real-world data to see if changes like updated labels or usage guidelines actually change how drugs are used over time.
ToolRWERisk Minimisation MeasuresThis tool helps researchers see if a treatment works differently for patients based on their individual risk levels. It uses real-world health data to compare how well treatments work across different patient groups.
Software PackageRWEHeterogeneityThis study provides reference values for health-related quality of life in the Slovenian adult population using the EQ-5D-5L questionnaire. It reports average scores by age, gender, region, and education level, which can help interpret results from future health studies in Slovenia.
Population normHRQoLThe SMART Tool helps developers of health economic models make smart choices about how simple or complex their models should be. It guides them to justify their choices so the model is clear, valid, and just complex enough to answer the health decision being studied.
ToolModel ValidationTransparencyReporting StandardsThis method creates shared EQ-5D value sets for groups of similar European countries when a country doesn’t have its own value set. It helps choose the best proxy by grouping countries based on cultural, linguistic, healthcare, and social factors.
Value setHRQoLA web-based tool designed to help health technology assessment (HTA) agencies and payers decide how to handle evidence from surrogate endpoints, like lab results or biomarkers, when evaluating medical treatments. It supports more informed decisions when direct evidence on patient outcomes is not available.
Web-based AppSurrogate EndpointsA method that uses real-world data to mimic a randomized clinical trial, helping estimate treatment effects as if patients were randomly assigned to treatments.
MethodRWEA classification system that organizes synthetic healthcare data based on how much real data is used (proportion), the type of data (modality), and how it has been changed or generated (transformation). It helps researchers and practitioners understand and compare different kinds of synthetic health data.
TaxonomyData ClassificationSynthetic DataA ready-to-use template that helps stakeholders create formal agreements on how data will be collected for outcomes-based managed entry agreements (OBMEAs) for rare disease treatments.
TemplatePricing/PayerThis template helps HTA bodies create clear guidelines for committees that monitor outcome-based managed entry agreements (OBMEAs). These agreements link payment for health technologies to their real-world performance. The template outlines what the committee should do, how it should operate, and what issues it needs to track over time.
TemplatePricing/PayerThe Book of OHDSI is a comprehensive guide that explains how to use standardized data and open-source tools to study real-world health data. It helps researchers understand how to organize health data in a common format, check data quality, and perform studies on patient groups, treatment effects, and individual patient outcomes.
GuidelineRWEA collection of standard unit costs from nine European countries designed to help make economic evaluations of health technologies more consistent and comparable.
ToolCosting & Resource UseThe PECUNIA PROM-MH Compendium is a collection of 204 mental health patient-reported outcome measures that can be used to assess quality of life and well-being in economic evaluations across different countries and sectors. It helps researchers choose the right tool by providing detailed information about each measure, including language versions, age-specific forms, and whether they can be used in economic studies.
ToolPROMsHRQoLThis method helps predict how different patients respond to different treatments by combining data from randomized and non-randomized studies. It uses patient characteristics to explain why treatment effects vary and provides personalized predictions.
MethodPredictive ModellingRWEIndirect ComparisonsA methodological toolbox to support decision-makers in evaluating and adopting decrementally cost-effective interventions—those that are less effective but also less costly. The toolbox provides practical tools, including a discrete choice experiment, a checklist, and a decision tree, to guide transparent and ethical deliberation on when and how such interventions should be recommended for use.
ToolDecremental CEAA collection of indicators and guidance to help evaluate differences in how hospitals perform and to find out which organizational factors are linked to better performance.
ToolContext & ImplementationThis toolkit helps assess whether evidence from one healthcare setting can be applied to another by looking at how hospital environments might affect the use and success of a health technology.
ToolContext & ImplementationThe Trajectories R package helps researchers find and visualize important sequences of clinical events in large health datasets that use the OMOP Common Data Model. It can show patterns like which treatments or diagnoses tend to happen before or after each other over time.
Software PackageRWETreatmentPatterns is a tool that helps researchers see how patients are actually treated in real life using real-world data. It makes it easier to study treatment sequences for different diseases in a consistent and clear way.
Software PackageRWEThis method helps estimate how well different treatments work for individual patients by combining information about their personal risk of getting worse with results from multiple treatment studies. It uses two steps: first, predicting a patient's baseline risk of a bad outcome using their personal traits; second, using that risk score to see how treatment benefits change depending on how high or low the risk is.
MethodPredictive ModellingIndirect ComparisonsHeterogeneity