title: Multiple imputation techniques in small sample clinical trials.
abstract: Clinical trials allow researchers to draw conclusions about the effectiveness of a treatment. However, the statistical analysis used to draw these conclusions will inevitably be complicated by the common problem of attrition. Resorting to ad hoc methods such as case deletion or mean imputation can lead to biased results, especially if the amount of missing data is high. Multiple imputation, on the other hand, provides the researcher with an approximate solution that can be generalized to a number of different data sets and statistical problems. Multiple imputation is known to be statistically valid when n is large. However, questions still remain about the validity of multiple imputation for small samples in clinical trials. In this paper we investigate the small-sample performance of several multiple imputation methods, as well as the last observation carried forward method.
authors.affiliation: Division of Biostatistics, Mayo Clinic, Rochester, MN 55905, and Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA. sbarnes@mayo.edu
authors.forename: Sunni A
authors.initials: SA
authors.lastname: Barnes
authors.forename: Stacy R
authors.initials: SR
authors.lastname: Lindborg
authors.forename: John W
authors.initials: JW
authors.lastname: Seaman
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D004341:Drug Evaluation / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical