• Andrew C Leon, Craig H Mallinckrodt, Christy Chuang-Stein, Donald G Archibald, Graeme E Archer, and Kevin Chartier.
• Department of Psychiatry, Weill Medical College, Cornell University, New York, New York 10021, USA. acleon@med.cornell.edu
• Biol. Psychiatry. 2006 Jun 1;59(11):1001-5.

AbstractAttrition is a ubiquitous problem in randomized controlled clinical trials (RCT) of psychotropic agents that can cause biased estimates of the treatment effect, reduce statistical power, and restrict the generalizability of results. The extent of the problem of attrition in central nervous system (CNS) trials is considered here and its consequences are examined. The taxonomy of missingness mechanisms is then briefly reviewed in order to introduce issues underlying the choice of data analytic strategies appropriate for RCTs with various forms of incomplete data. The convention of using last observation carried forward to accommodate attrition is discouraged because its assumptions are typically inappropriate for CNS RCTs, whereas multiple imputation strategies are more appropriate. Mixed-effects models often provide a useful data analytic strategy for attrition as do the pattern-mixture and propensity adjustments. Finally, investigators are encouraged to consider asking participants, at each assessment session, the likelihood of attendance at the subsequent assessment session. This information can be used to eliminate some of the very obstacles that lead to attrition, and can be incorporated in data analyses to reduce bias, but it will not eliminate all attrition bias.

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