Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26190

Title: An evaluation of the trimmed mean approach in clinical trials with dropout
Authors: Wang, Ming-Dauh
Liu, Jiajun
Molenberghs, Geert
Mallinckrodt, Craig
Issue Date: 2018
Citation: PHARMACEUTICAL STATISTICS, 17(3), p. 278-289
Abstract: The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm. The untrimmed values are used to calculated means or mean changes. An explicit intent of trimming is to favor the group with lower dropout because having more completers is a beneficial effect of the drug, or conversely, higher dropout is a bad effect. In the simulation study, difference between treatments estimated from trimmed means was greater than the corresponding effects estimated from untrimmed means when dropout favored the experimental group, and vice versa. The trimmed mean estimates a unique estimand. Therefore, comparisons with other methods are difficult to interpret and the utility of the trimmed mean hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients, and adherence decisions in the trial are sufficiently similar to clinical practice in order to generalize the results. Trimming might be applicable to other inter-current events such as switching to or adding rescue medicine. Given the well-known biases in some methods that estimate effectiveness, such as baseline observation carried forward and non-responder imputation, the trimmed mean may be a useful alternative when its assumptions are justifiable.
Notes: Mallinckrodt, C (reprint author), Eli Lilly & Co, Lilly Res Labs, Indianapolis, IN 46285 USA. cmallinc@lilly.com
URI: http://hdl.handle.net/1942/26190
DOI: 10.1002/pst.1858
ISI #: 000433592300006
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version340.53 kBAdobe PDF
Peer-reviewed author version356.51 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.