This Article Full Text (PDF) All Versions of this Article: 0962280207080640v1 17/3/243    most recent References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Baker, S. G Articles by Kramer, B. S Search for Related Content PubMed PubMed Citation Articles by Baker, S. G Articles by Kramer, B. S Social Bookmarking                         What's this?

Randomized trials for the real world: making as few and as reasonable assumptions as possible

Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA, sb16i{at}nih.gov

Barnett S Kramer

Office of Disease Prevention, National Institutes of Health, Bethesda, MD, USA

The strength of the randomized trial to yield conclusions not dependent on assumptions applies only in an ideal setting. In the real world various complications such as loss-to-follow-up, missing outcomes, noncompliance and nonrandom selection into a trial force a reliance on assumptions. To handle real world complications, it is desirable to make as few and as reasonable assumptions as possible. This article reviews four techniques for using a few reasonable assumptions to design or analyse randomized trials in the presence of specific real world complications: 1) a double sampling design for survival data to avoid strong assumptions about informative censoring, 2) sensitivity analysis for partially missing binary outcomes that uses the randomization to reduce the number of parameters specified by the investigator, 3) an estimate of the effect of treatment received in the presence of all-or-none compliance that requires reasonable assumptions, and 4) statistics for binary outcomes that avoid some assumptions for generalizing results to a target population.

This version was published on June 1, 2008

Statistical Methods in Medical Research, Vol. 17, No. 3, 243-252 (2008)
DOI: 10.1177/0962280207080640


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?