Prevention and Treatment of Missing Data
About the short course:
Recent research has fostered new guidance on preventing and treating missing data in clinical trials and an addendum to the ICH Guidelines is anticipated (ICH E9-R1). The first day of this is short course focuses on clinical issues and how they influence statistical considerations – and is therefore directed to a cross-functional audience. The second ½ day focuses on issues specific to statisticians, including analytic details and specific code to implement analyses.
Day 1 begins with an overview of recent developments, including the report form the National Research Council Expert Panel on missing data that provided detailed advice to FDA on the prevention and treatment of missing data, along with other key papers and developments. The course will distil common elements from recent guidance into 3 pillars:
1) setting clear objectives and defining causal estimands;
2) minimizing missing data; and,
3) pre-specifying a sensible primary analyses and appropriate sensitivity analyses.
Specific means for putting the guidance into action are discussed from a cross-functional (aka not highly statistical) perspective, and are reinforced by examples and instructor-facilitated study development exercises.
Day 1 Learning objectives
1) Understand the three pillars of preventing and treating missing data, with emphasis on a structured approach to study development that includes a sensible primary analysis and appropriate sensitivity analyses – however, the greatest focus on day one will be on choosing and clearly specifying objectives and estimands.
2) Be able to apply the three pillars principles and the structured approach to study development to their own research
3) Understand basic principles important to interpreting results from primary and sensitivity analyses.
Day 1 Outline
Objectives and estimands
Preventing missing data
A non-statistical perspective on primary and sensitivity analyses
Study development exercise
Day 2 is a ½ day session that takes an in-depth look at the theory behind and implementation details of common analytic approaches for primary analyses and for sensitivity analyses. Emphasis will be placed on matching analytic approaches with the various estimands discussed on day 1. Specific code will be shared to implement key analyses in commercially available software. Emphasis will be placed on newer sensitivity analyses that allow assessment of the consequences of departures from missing at random (MAR). These methods include the so-called reference-based imputation approaches and delta-adjustment approaches, including implementations in a tipping-point approach.
Day 2 Learning objectives
1) Understand key theory relevant to understanding the merits of various analytic approaches in differing circumstances.
2) Be able to implement common primary analytic approaches using simple code in commercial software.
3) Be able to implement reference-based imputation and delta adjustment tipping point approaches using commercial software, and know where to obtain specialty code for alternative sensitivity analyses.
Day 2 outline
History and theoretical background
Overview of primary analyses
Overview of MNAR methods
28 February 2917: 9:00am – 4:30pm
1 March 2017: 9:00am – 1:00pm
About the presenter:
Dr. Craig Mallinckrodt received his PhD in 1993 from Colorado State University, where he subsequently held a joint appointment in the departments of statistics and clinical sciences. Craig joined Lilly in 1998 and has extensive drug development experience covering all four clinical phases in multiple therapeutic areas. Dr. Mallinckrodt has published extensively on missing data. He led the PhRMA expert team on missing data and currently leads the Drug Information Association Scientific Working Group on missing data. Dr. Mallinckrodt is a Fellow of the American Statistical Association and recently won the Royal Statistical Society’s award for excellence in the pharmaceutical industry for his book titled A Practical Guide to the Prevention and Treatment of Missing Data.
Early Bird: Payment before or on 15 January 2017
SSA Members: $600.00
SSA Non Members: $600.00
SSA Student Members: $600.00
SSA Non-Member Students: $600.00
Payment from 16 January 2017 – 19 February 2017
SSA Members: $700.00
SA Non Members: $800.00
SSA Student Members: $650
SSA Non-Member Students (full-time)*: $750
*Please email proof of full-time student status to [email protected]
The registration fees include workshop attendance, morning and afternoon tea as well as lunch on the first day and morning tea on the second day.
Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. Please contact the SSA Office before making any travel arrangements to confirm that the workshop will go ahead, because the Society will not be held responsible for any travel or accommodation expenses incurred due to a workshop cancellation.
Cancellations received prior to Tuesday, 21 February 2017, will be refunded, minus a $20 administration fee. From 21 February 2017 no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to [email protected].
|When:||28/02/2017 - 01/03/2017|
|Time:||9:00 am - 4:30 pm|
|Location:||MGSM CBD Executive Conference Centre,
37 Pitt Street,