Real-World Evidence Generation
Expert-defined terms from the Advanced Skill Certificate in Market Access for Pharmaceuticals course at LearnUNI. Free to read, free to share, paired with a professional course.
Ablative Analysis refers to a statistical technique used in Real #
World Evidence Generation to evaluate the impact of a specific treatment or intervention on patient outcomes. This method involves comparing the outcomes of patients who received the treatment with those who did not, while controlling for various confounding factors. Ablative analysis is commonly used in pharmaceutical research to assess the effectiveness of a new treatment in a real-world setting.
Absolute Risk Reduction is a measure used to quantify the difference in risk<… #
In the context of Real-World Evidence Generation, Absolute Risk Reduction is used to evaluate the effectiveness of a treatment in reducing the risk of a particular outcome, such as a adverse event or a relapse.
Accelerated Approval is a regulatory pathway that allows pharmaceutical c… #
In the context of Real-World Evidence Generation, Accelerated Approval is used to facilitate the development of new treatments for serious or life-threatening conditions, where there is an urgent need for effective therapies.
Actionable Insights refer to the practical, meaningful conclusions that c… #
Actionable Insights are typically derived from the analysis of large datasets, using advanced analytical techniques and statistical methods.
Adaptive Design refers to a flexible approach to clinical trial design, which al… #
In the context of Real-World Evidence Generation, Adaptive Design is used to optimize the efficiency and effectiveness of clinical trials, by incorporating real-time data and feedback from patients and clinicians.
Adherence refers to the degree to which patients follow their prescribed treatme… #
In the context of Real-World Evidence Generation, Adherence is a critical factor in evaluating the effectiveness of treatments in real-world settings, as poor adherence can compromise treatment outcomes.
Administrative Claims Data refers to the vast amounts of data generated b… #
In the context of Real-World Evidence Generation, Administrative Claims Data is used to analyze patterns of healthcare utilization, treatment outcomes, and costs associated with different treatments and interventions.
Adverse Event refers to an unwanted or harmful occurrence that is associa… #
In the context of Real-World Evidence Generation, Adverse Events are a critical aspect of safety monitoring and risk assessment, as they can have significant implications for patient health and well-being.
Aggregate Data refers to summarized or combined data that is deriv… #
In the context of Real-World Evidence Generation, Aggregate Data is used to analyze trends and patterns in healthcare utilization, treatment outcomes, and costs, while protecting patient privacy and confidentiality.
Algorithm refers to a set of rules or procedures that are used to analyze dat… #
In the context of Real-World Evidence Generation, Algorithms are used to identify patterns and trends in large datasets, and to develop predictive models that can inform decision-making in healthcare and pharmaceutical development.
Analytic Framework refers to a structured approach to analyzing data and… #
In the context of Real-World Evidence Generation, Analytic Frameworks are used to guide the development of research questions, study designs, and statistical analysis plans, and to ensure that results are valid and reliable.
Annualized Rate refers to the rate of occurrence of a particular event or outcom… #
In the context of Real-World Evidence Generation, Annualized Rates are used to express the frequency or prevalence of specific outcomes, such as adverse events or relapses, in a way that is easy to understand and interpret.
Anonymization refers to the process of removing or masking personal ident… #
In the context of Real-World Evidence Generation, Anonymization is a critical step in preparing data for analysis, as it enables researchers to use real-world data while minimizing the risk of breaches of patient confidentiality.
Association Rule Learning is a machine learning technique that is used to… #
In the context of Real-World Evidence Generation, Association Rule Learning is used to analyze data from electronic health records, claims databases, and other sources, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Attribution Modeling refers to the process of assigning cause and effe… #
In the context of Real-World Evidence Generation, Attribution Modeling is used to evaluate the impact of different treatments or interventions on patient outcomes, and to identify the most effective strategies for improving health outcomes.
Average Treatment Effect refers to the average difference in outcome between a t… #
In the context of Real-World Evidence Generation, Average Treatment Effect is used to evaluate the effectiveness of treatments or interventions in real-world settings, and to inform decision-making in healthcare and pharmaceutical development.
Bayesian Analysis is a statistical approach that uses Bayes' theorem to u… #
In the context of Real-World Evidence Generation, Bayesian Analysis is used to analyze data from multiple sources, and to develop predictive models that can inform decision-making in healthcare and pharmaceutical development.
Big Data refers to the vast amounts of data that are generated by various… #
In the context of Real-World Evidence Generation, Big Data is used to analyze patterns and trends in healthcare utilization, treatment outcomes, and costs, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Biomarker refers to a measurable indicator of a biological process or … #
In the context of Real-World Evidence Generation, Biomarkers are used to diagnose diseases, monitor treatment response, and predict patient outcomes, and to identify subpopulations that are most likely to benefit from specific treatments or interventions.
Biostatistics refers to the application of statistical principles and met… #
In the context of Real-World Evidence Generation, Biostatistics is used to analyze data from various sources, and to develop predictive models that can inform decision-making in healthcare and pharmaceutical development.
Bundle Payment refers to a payment model in which a single payment is made for a… #
In the context of Real-World Evidence Generation, Bundle Payment is used to incentivize providers to deliver high-quality, cost-effective care, and to reduce variability in treatment outcomes and costs.
Case #
Control Study is a research design in which patients with a specific outcome or disease are compared with patients without the outcome or disease, to identify risk factors and predictors of the outcome. In the context of Real-World Evidence Generation, Case-Control Studies are used to evaluate the effectiveness of treatments or interventions in real-world settings, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Causal Inference refers to the process of drawing conclusions about the cause… #
In the context of Real-World Evidence Generation, Causal Inference is used to evaluate the impact of different treatments or interventions on patient outcomes, and to identify the most effective strategies for improving health outcomes.
Censoring refers to the process of removing or masking data that i… #
In the context of Real-World Evidence Generation, Censoring is used to handle missing or incomplete data, and to ensure that results are valid and reliable.
Clinical Decision Support refers to the use of computerized systems to pr… #
In the context of Real-World Evidence Generation, Clinical Decision Support is used to improve the quality and consistency of care, and to reduce variability in treatment outcomes and costs.
Clinical Endpoint refers to a specific outcome or event that is used to e… #
In the context of Real-World Evidence Generation, Clinical Endpoints are used to define the success of a treatment or intervention, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Clinical Trial refers to a research study that is designed to evaluate th… #
In the context of Real-World Evidence Generation, Clinical Trials are used to generate evidence on the effectiveness of new treatments or interventions, and to inform decision-making in healthcare and pharmaceutical development.
Cohort Study is a research design in which a group of patients is followe… #
In the context of Real-World Evidence Generation, Cohort Studies are used to evaluate the effectiveness of treatments or interventions in real-world settings, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Comparative Effectiveness Research refers to the study of the relative <b… #
In the context of Real-World Evidence Generation, Comparative Effectiveness Research is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most effective strategies for improving health outcomes.
Confidence Interval refers to a statistical measure that is used to express the… #
In the context of Real-World Evidence Generation, Confidence Intervals are used to quantify the uncertainty associated with estimates of treatment effects or outcomes, and to inform decision-making in healthcare and pharmaceutical development.
Confounding Variable refers to a factor or variable that can influence th… #
In the context of Real-World Evidence Generation, Confounding Variables are used to control for biases and confounding factors that can affect the validity of results.
Cost #
Benefit Analysis refers to a method of evaluating the costs and benefits of a treatment or intervention, in order to determine whether it is cost-effective. In the context of Real-World Evidence Generation, Cost-Benefit Analysis is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most cost-effective strategies for improving health outcomes.
Cost #
Effectiveness Analysis refers to a method of evaluating the costs and effectiveness of a treatment or intervention, in order to determine whether it is cost-effective. In the context of Real-World Evidence Generation, Cost-Effectiveness Analysis is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most cost-effective strategies for improving health outcomes.
Cost #
Utility Analysis refers to a method of evaluating the costs and utility of a treatment or intervention, in order to determine whether it is cost-effective. In the context of Real-World Evidence Generation, Cost-Utility Analysis is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most cost-effective strategies for improving health outcomes.
Data Mining refers to the process of automatically discovering patterns a… #
In the context of Real-World Evidence Generation, Data Mining is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop predictive models that can forecast patient outcomes and costs.
Data Quality refers to the accuracy , completeness , and consiste… #
In the context of Real-World Evidence Generation, Data Quality is critical, as poor data quality can compromise the validity and reliability of results.
Data Warehouse refers to a centralized repository of data that is designe… #
In the context of Real-World Evidence Generation, Data Warehouses are used to store and manage large datasets, and to provide access to data for analysis and reporting.
Decision Analysis refers to a method of evaluating the consequences of di… #
In the context of Real-World Evidence Generation, Decision Analysis is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most effective strategies for improving health outcomes.
Decision Tree refers to a graphical representation of a decision or st… #
In the context of Real-World Evidence Generation, Decision Trees are used to evaluate the effectiveness of different treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Disease Management refers to a coordinated approach to managing chroni… #
In the context of Real-World Evidence Generation, Disease Management is used to improve the quality and consistency of care, and to reduce variability in treatment outcomes and costs.
Dose #
Response Curve refers to a graphical representation of the relationship between the dose of a treatment and the resulting response or outcome. In the context of Real-World Evidence Generation, Dose-Response Curves are used to evaluate the efficacy and safety of treatments, and to identify the optimal dose or regimen for a specific condition or disease.
Effect Size refers to a statistical measure of the magnitude of the effect of a… #
In the context of Real-World Evidence Generation, Effect Size is used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Electronic Health Record refers to a digital version of a patient's medical h… #
In the context of Real-World Evidence Generation, Electronic Health Records are used to collect and analyze data on patient outcomes, treatment response, and costs, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Endpoint refers to a specific outcome or event that is used to evaluate t… #
In the context of Real-World Evidence Generation, Endpoints are used to define the success of a treatment or intervention, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Epidemiology refers to the study of the distribution and determinants<… #
In the context of Real-World Evidence Generation, Epidemiology is used to analyze patterns and trends in disease incidence and prevalence, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Evidence #
Based Medicine refers to an approach to medicine that emphasizes the use of evidence from research to inform clinical decision-making. In the context of Real-World Evidence Generation, Evidence-Based Medicine is used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
External Validity refers to the degree to which the results of a study… #
In the context of Real-World Evidence Generation, External Validity is critical, as it determines the applicability of results to real-world settings and populations.
Factor Analysis refers to a statistical technique that is used to identify pa… #
In the context of Real-World Evidence Generation, Factor Analysis is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop predictive models that can forecast patient outcomes and costs.
Health Economics refers to the study of the economic aspects of <i… #
In the context of Real-World Evidence Generation, Health Economics is used to evaluate the costs and benefits of treatments or interventions, and to identify the most cost-effective strategies for improving health outcomes.
Health Outcomes refers to the consequences of a treatment or intervention… #
In the context of Real-World Evidence Generation, Health Outcomes are used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Health Technology Assessment refers to a systematic evaluation of the clinica… #
In the context of Real-World Evidence Generation, Health Technology Assessment is used to inform decision-making in healthcare and pharmaceutical development, and to identify the most effective strategies for improving health outcomes.
Incidence refers to the number of new cases of a disease or condition that occur… #
In the context of Real-World Evidence Generation, Incidence is used to analyze patterns and trends in disease incidence and prevalence, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Intention #
to-Treat refers to a statistical approach that involves analyzing data from all patients who are randomized to a treatment group, regardless of whether they actually receive the treatment. In the context of Real-World Evidence Generation, Intention-to-Treat is used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Internal Validity refers to the degree to which the results of a study… #
In the context of Real-World Evidence Generation, Internal Validity is critical, as it determines the accuracy and reliability of results.
Kaplan #
Meier Curve refers to a graphical representation of the survival or event-free probability of a patient over time. In the context of Real-World Evidence Generation, Kaplan-Meier Curves are used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Longitudinal Study refers to a research design that involves collecting <… #
In the context of Real-World Evidence Generation, Longitudinal Studies are used to evaluate the effectiveness of treatments or interventions, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Machine Learning refers to a type of artificial intelligence that involve… #
In the context of Real-World Evidence Generation, Machine Learning is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop predictive models that can forecast patient outcomes and costs.
Meta #
Analysis refers to a statistical technique that involves combining data from multiple studies to draw more general conclusions. In the context of Real-World Evidence Generation, Meta-Analysis is used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Missing Data refers to data that is not available or is incomplete … #
In the context of Real-World Evidence Generation, Missing Data is a critical issue, as it can affect the accuracy and reliability of results.
Multivariate Analysis refers to a statistical technique that involves analyzing… #
In the context of Real-World Evidence Generation, Multivariate Analysis is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop predictive models that can forecast patient outcomes and costs.
Natural Language Processing refers to a type of artificial intelligence t… #
In the context of Real-World Evidence Generation, Natural Language Processing is used to extract insights from unstructured data, such as clinical notes and medical records.
Network Meta #
Analysis refers to a statistical technique that involves combining data from multiple studies to draw more general conclusions about the relative efficacy of different treatments or interventions. In the context of Real-World Evidence Generation, Network Meta-Analysis is used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Non #
Randomized Study refers to a research design that does not involve randomizing patients to treatment groups. In the context of Real-World Evidence Generation, Non-Randomized Studies are used to evaluate the effectiveness of treatments or interventions in real-world settings, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Observational Study refers to a research design that involves observing p… #
In the context of Real-World Evidence Generation, Observational Studies are used to evaluate the effectiveness of treatments or interventions, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Patient #
Centered Outcome refers to a measure of patient health and well-being that is relevant to the patient's experience and quality of life. In the context of Real-World Evidence Generation, Patient-Centered Outcomes are used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Patient #
Reported Outcome refers to a measure of patient health and well-being that is reported directly by the patient. In the context of Real-World Evidence Generation, Patient-Reported Outcomes are used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Pharmacoeconomics refers to the study of the economic aspects of <… #
In the context of Real-World Evidence Generation, Pharmacoeconomics is used to evaluate the costs and benefits of treatments or interventions, and to identify the most cost-effective strategies for improving health outcomes.
Pharmacovigilance refers to the monitoring of the safety of pha… #
In the context of Real-World Evidence Generation, Pharmacovigilance is used to evaluate the safety of treatments or interventions, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Precision Medicine refers to an approach to medicine that involves tailor… #
In the context of Real-World Evidence Generation, Precision Medicine is used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Predictive Modeling refers to the use of statistical or machine le… #
In the context of Real-World Evidence Generation, Predictive Modeling is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop strategies for improving health outcomes.
Prevalence refers to the number of cases of a disease or condition that exist wi… #
In the context of Real-World Evidence Generation, Prevalence is used to analyze patterns and trends in disease incidence and prevalence, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Propensity Score refers to a statistical measure that is used to balance… #
In the context of Real-World Evidence Generation, Propensity Scores are used to control for biases and confounding factors that can affect the validity of results.
Prospective Study refers to a research design that involves collecting <i… #
In the context of Real-World Evidence Generation, Prospective Studies are used to evaluate the effectiveness of treatments or interventions, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Quality of Life refers to a measure of patient health and well #
being that is relevant to the patient's experience and quality of life. In the context of Real-World Evidence Generation, Quality of Life is used to evaluate the effectiveness of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Randomized Controlled Trial refers to a research design that involves ran… #
In the context of Real-World Evidence Generation, Randomized Controlled Trials are used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.
Real #
World Evidence refers to data or evidence that is generated in real-world settings, as opposed to in a controlled or experimental setting. In the context of Real-World Evidence Generation, Real-World Evidence is used to evaluate the effectiveness of treatments or interventions, and to identify insights that can inform decision-making in healthcare and pharmaceutical development.
Regression Analysis refers to a statistical technique that involves modeling the… #
In the context of Real-World Evidence Generation, Regression Analysis is used to identify insights that can inform decision-making in healthcare and pharmaceutical development, and to develop predictive models that can forecast patient outcomes and costs.
Relative Risk refers to a statistical measure that is used to compare the… #
In the context of Real-World Evidence Generation, Relative Risk is used to evaluate the efficacy of treatments or interventions, and to identify the most effective strategies for improving health outcomes.