The impact of loss to follow-up on hypothesis tests of the treatment effect for several statistical methods in substance abuse clinical trials.


Journal Article

"Loss to follow-up" can be substantial in substance abuse clinical trials. When extensive losses to follow-up occur, one must cautiously analyze and interpret the findings of a research study. Aims of this project were to introduce the types of missing data mechanisms and describe several methods for analyzing data with loss to follow-up. Furthermore, a simulation study compared Type I error and power of several methods when missing data amount and mechanism varies. Methods compared were the following: Last observation carried forward (LOCF), multiple imputation (MI), modified stratified summary statistics (SSS), and mixed effects models. Results demonstrated nominal Type I error for all methods; power was high for all methods except LOCF. Mixed effect model, modified SSS, and MI are generally recommended for use; however, many methods require that the data are missing at random or missing completely at random (i.e., "ignorable"). If the missing data are presumed to be nonignorable, a sensitivity analysis is recommended.

Full Text

Cited Authors

  • Hedden, SL; Woolson, RF; Carter, RE; Palesch, Y; Upadhyaya, HP; Malcolm, RJ

Published Date

  • July 2009

Published In

Volume / Issue

  • 37 / 1

Start / End Page

  • 54 - 63

PubMed ID

  • 19008067

Pubmed Central ID

  • 19008067

Electronic International Standard Serial Number (EISSN)

  • 1873-6483

International Standard Serial Number (ISSN)

  • 0740-5472

Digital Object Identifier (DOI)

  • 10.1016/j.jsat.2008.09.011


  • eng