Wednesday, April 13 – Thursday, April 14, 2011
National Harbor, MD (Washington Metro Area) | The Gaylord National Hotel
This annual conference, co-sponsored by FDA and AdvaMed-MTLI, b
Contents
Wednesday, April 13 – Thursday, April 14, 2011
National Harbor, MD (Washington Metro Area) | The Gaylord National Hotel
This annual conference, co-sponsored by FDA and AdvaMed-MTLI, brings together leading authorities from FDA, industry, and academia to address statistical issues specifically related to medical technology clinical studies, including Bayesian study design and analysis, subgroup analysis, multiple secondary endpoints, adaptive sample size re-estimation, patient-reported outcomes, and statistical challenges related to diagnostics and postmarket studies.
Who Should Attend
Statisticians and clinicians involved in the pre- and postmarket assessment of medical devices and diagnostics.
Agenda Overview
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- Data Standards, Data Quality, CDISC
- Assuring Device Trial Data Quality for Regulatory Decision Making: Can Data Standards help
- Handling of Missing Data in Clinical Trials: Findings of a National Research Council Study
- Tipping-point Analysis in Medical Device Clinical Trials
- Minimizing Missing Data in Study Design: A Regulatory Perspective
- Good Statistical Practices
- What Constitutes a Good Statistical Analysis Plan
- What Constitutes a Good Statistical Section in a PMA or 510(k)
- Issues with trials incorporating Bayesian and/or Adaptive Designs
- Decision Rules and Associated Sample Size Planning for Regional Approval Utilizing Multi-Regional Clinical Trials
- Multiple Testing of Biomarkers in Pharmacogenomics
- Multiplicity issues in Practice: an Industry Perspective
- Non-randomized Study Design Including Propensity Score Analysis
- Statistical Points to Consider in the Precision Testing of Immunohistochemistry Assays
- Interpretation Schema Of Visual Test Results: Interpretation Of Immunohistochemistry, Current Paradigms, and Challenges
- Patient Assessment Algorithms: A Regulatory Perspective
- Statistical Issues in Combining Data from Multiple Sources to Estimate aAsolute Risk
- Strategies for Combining Biomarkers and Clinical Factors to Assess Risk of Disease Progression
- Software Validation and Some Personal Experiences with R Packages: Creation, Testing and Use
- Validation and Verification of Statistical Programming
- FDA’s Perspective on the Best Practice for Software Validation for IVD Products
- Monitoring
See Advamed website for more details