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Overcoming Challenges of Patient-Reported Outcomes Research in Orphan Drug Development
October 22, 2014
Please enjoy the archive of this webinar delivered on Oct 22, 2014.
In this session we discuss the challenges of Patient Reported Outcomes in Orphan Drug Development. From exploring the key concerns in developing an endpoint strategy to incorporating the patient perspective in publications of data to support payers. Learn how a mixed methods approach may be the solution to address patient-centered outcomes research challenges in Orphan Drugs.
Mixed methods research can be defined as “research in which the investigator collects and analyzes data, integrates the findings, and draws inferences using both qualitative and quantitative approaches or methods in a single study or program of inquiry.”
Industry experts Dr. Benoit Arnould and Dr. Antoine Regnault will share their knowledge on Patient-Centered Rare Disease research and the challenges of gaining regulatory approval for labeling claims. Dr. Arnould will discuss the ability of standardized questionnaires and semi-structured interviews in documenting the patient perspective; he will present how mixed methods research (MMR) can be a powerful tool in clinical research when working with limited patient populations.
Recorded on 22 October 2014 at:
9:00 am, Eastern Daylight Time (New York, GMT-04:00)
2:00 pm, GMT Summer Time (London, GMT+01:00)
3:00 pm, Europe Summer Time (Brussels, GMT+02:00)
BENOIT ARNOULD PhD
Senior Director – Patient-Centered Outcomes, Mapi HEOR & Strategic Market Access
Benoit leads the Patient-Centered Outcomes research team and has conducted studies to develop and validate Patient-Reported Outcome (PRO) measures for over 15 years. Leveraging on his experience Benoit leads the Endpoint strategy definition and consulting services. He has research experience experience spanning of indications including urinary incontinence, gastrointestinal disorders, COPD, depression, neuropathic pain, erectile dysfunction, metabolic disorders, ophthalmologic diseases, cancer, and various rare diseases. Previously a statistician in a veterinary epidemiological research unit and in the pharmaceutical industry, Benoit has sound knowledge of clinical and epidemiological trial design, data analysis and interpretation. He also has extensive practical experience of a number of different healthcare systems, having worked for several years in various countries in Africa and Asia. Benoit is a Health Economics graduate with a speciality in statistics, and has completed, under the supervision of Professor Gerard Duru, his PhD on tools for clinical decision making, a subject on which he publishes regularly.
ANTOINE REGNAULT, PhD, MSc, MA
Research Director, Statistics & Psychometrics
Antoine leads Mapi’s Statistics & Psychometrics team. His role involves consulting on projects requiring statistical expertise, particularly on questions concerning the implementation and analysis of Patient-Reported Outcomes in clinical trials or epidemiological studies. Antoine has advanced skills in psychometrics and sophisticated statistical analyses.He has also worked on statistical methods for the development of patient scales for clinical practice, and has thus acquired specialist skills applicable in a large number of fields, both in clinical trials and clinical practice. Antoine has presented his work at a number of international conferences. Before working in the Patient-Reported Outcomes (PRO) field, Antoine participated in a study of the progressive disability of elderly people with dementia, using multi-state modelling techniques. Antoine has an MSc in Biostatistics from the University Victor Segalen Bordeaux 2, and an MA in Health Economics and PhD in Applied Statistics from the University Claude Bernard Lyon 1. Antoine’s PhD thesis was about the use of quantitative methods for assessing the cross-cultural validity of PRO instruments.
Among the topics to be covered are:
- Specific challenges for Orphan Drugs
- PRO Claims in Orphan Drugs
- Balancing between generic measures and condition specific measures
- Mixed Methods Research (MMR)