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Activity Number: 17
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #320750
Title: Choosing Estimands in Clinical Trials with Missing Data
Author(s): Craig Mallinckrodt*
Companies: Eli Lilly and Company
Keywords: estimands ; missing data

Consensus exists that in preventing and treating missing data clear objectives should be defined along with the causal estimands, trial design and conduct should maximize adherence to the protocol specified interventions, and a sensible primary analysis should be paired with plausible sensitivity analyses. Two general categories of estimands are: effects of the drug as actually taken (effectiveness, de-facto) and effects of the drug if taken as directed (efficacy, de-jure). Examples are used to illustrate how maximizing adherence reduces sensitivity to missing data assumptions for de-jure estimands, but may also reduce generalizability of results for de-facto estimands if the methods used to maximize adherence in the trial are not feasible in clinical practice.

Authors who are presenting talks have a * after their name.

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