Mapi Values periodically offers workshops on current topics for our clients. The workshops are usually conducted in a “lunch and learn” format and offer ample opportunity for our clients to ask questions specific to their products. All workshops are free when offered in a lunch and learn setting. Mapi Values also offers paid 1-2 day training sessions in a variety of areas.

WORKSHOPS OFFERED
Select a link below for details on a particular topic:

• Obtaining a Patient-Reported Outcome (PRO) Regulatory Claim with the FDA: Current Hot Button Issues
• Trials and tribulations: Validation of PRO endpoints within a clinical
• PRO Regulatory Requirements: Is It Easier to Get a Claim with the EMEA than the FDA?
• Advances in Meta Analysis: Techniques for Including Multiple Study Designs, Multiple Endpoints, and Multiple Treatments
• Product Value Arguments Based on Direct and Indirect Comparisons
• Tailoring the Model to the Decision-Problem in Health Economics
• Intoduction to Dynamic Modeling of Infectious Diseases for Health-Economic Analysis

This workshop explores issues typically encountered by sponsors when trying to obtain labeling claims with the FDA. It covers commonly made mistakes in regulatory submissions and examples from recent Medical Reviews where sponsors have failed to obtain labeling claims. This workshop also includes a discussion of the SEALD position on ePRO validation and consortium projects.

The target audience for this workshop is for professionals in outcomes research and regulatory departments with experience or interest in PRO submissions. The workshop length is approximately 60-90 minutes.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

We are proud to introduce a new lunch and learn workshop on a topic which is becoming critical for our clients: the psychometric validation of patient-reported outcome (PRO) questionnaires within clinical trials. In addition to a robust development process that includes qualitative research with patients, the scientific community and regulators such as the FDA and EMEA highlight the need to validate a PRO measure in the targeted patient population. Standalone validation can create critical impacts on a drug development program in terms of cost as well as expensive delays in already very tight timelines. As a solution, many companies consider in-trial validation, or a “piggy-back” analysis. The information disseminated during this lunch and learn session outlines the benefits of this approach as well as crucial issues to consider a priori for the appropriate implementation, analysis and interpretation of psychometric results from PRO data collected within a clinical trial. Concrete examples and solutions are discussed. Kathleen Rosa, PhD, Director of Psychometrics and Statistics at Mapi Values, will present the lunch and learn workshop. This workshop is complimentary for a limited time.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

This workshop compares and contrasts EMEA and FDA requirements for PRO claims. The EMEA Reflection Paper on PROs is reviewed and differences in requirements are discussed. These include the development history of measures (the acceptability of clinician involvement in the development of measures compared to patient involvement), the necessity for conceptual frameworks and endpoint models, and the level of detail required in PRO dossiers.

The target audience for this workshop is for professionals in outcomes research and regulatory departments who are involved with the submission of PRO data on an international basis. The workshop length is approximately 60-90 minutes.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

In this workshop we provide an overview of the more advanced methods of meta-analysis and interactively discuss the value of additional evidence and the use of a Bayesian approach for decision-making.

Regularly only results from randomized controlled trials (RCTs) with a parallel design are combined in meta-analyses. Cross-over trials are often ignored. Furthermore, single arm trials, while not providing information on relative effects, can provide valuable evidence on absolute effects. Especially for rare diseases, RCTs account for the minority of information, and it can be of interest to draw together both randomized and single arm studies.

Despite an interest in multiple endpoints, meta-analyses are often performed by endpoint. Analyzing multiple outcomes simultaneously has several advantages. For example by documenting relationships between endpoints which may be of clinical or scientific interest (Arends, 2006).

Traditional meta-analysis can also be extended by including multiple different pair-wise comparisons across a range of different interventions (Lu & Ades, 2004). This allows for indirect comparisons in the absence of head-to-head data and can strengthen the evidence base as well.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

Participants will be introduced to the advantages of combining direct and indirect comparisons to benchmark and to support product value among alternatives.

Mixed Treatment Comparison (MTC) has been presented as an extension of traditional meta-analysis by including multiple different pair-wise comparisons across a range of different interventions. MTC can provide very useful value arguments regarding clinical or Patient-Reported Outcomes (PROs). With MTC the relative efficacy or safety of a particular intervention versus competing interventions can be obtained in the absence of head-to-head comparisons; indirect comparison of two interventions is made via a common comparator. Often a Bayesian approach is adopted for MTC, which has several advantages. Furthermore, we will show that MTC can provide interesting value messages to support product uptake.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

Participants will be introduced to a classification of modelling techniques, and guidance on selecting the appropriate method for economic evaluation of health interventions will be provided.

Guidance on modelling approaches has been widely discussed in the published literature, and covering issues such as outcomes of interest, transparency, and evidence. However, there is very limited guidance on how to choose from the different modelling techniques, e.g. Decision trees, Markov cohort models, individual state-transition models, discrete event simulation, dynamic models, etc. Selection of the modelling method should be driven by the decision problem of interest and should take into account the decision maker. Models can be classified into cohort/aggregate and patient simulation. These two types of approaches can be sub-grouped further based on presence of patient interaction and how the interaction occurs through time. When choosing an appropriate model, some of the issues that should be considered are:

• Need for probabilistic sensitivity analysis to quantify uncertainty,
• Need for information regarding variability,
• Interaction between the patients, e.g. infectious disease,
• (Non-)Linear interaction of covariates,
• Uncertainty about relevant sub-groups, and
• Model calculation time.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America

This workshop aims to provide an understanding of modeling as applied to the population dynamics of infectious diseases and the (cost-) effectiveness of interventions.

Dynamic modeling approaches have become very important for estimating the cost-effectiveness of infectious disease control interventions and programs. One of the reasons is that traditional decision trees and Markov models do not capture herd immunity, thereby underestimating the cost-effectiveness of the intervention. Dynamic modeling is based on the nonlinear dynamics of infection spread in a population. By differentiating and relating groups of susceptibles (S), infected (E), infectious (I) and recovered (R), SEIR deterministic models can provide useful insights into the spread of infectious diseases such as influenza, varicella, and Human Papilloma Virus (HPV). These relatively simple deterministic models predict the transmission and spread of viral and bacterial infections and can help identify the factors controlling the persistence, stability and epidemic periods.

For additional details or inquiries, please contact us based on your location: Asia / Europe / North America