Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/14849
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mallinckrodt, Craig H. | - |
dc.contributor.author | Lin, Q. | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2013-03-27T13:05:23Z | - |
dc.date.available | 2013-03-27T13:05:23Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Pharmaceutical statistics, 12 (1), p. 1-6 | - |
dc.identifier.issn | 1539-1604 | - |
dc.identifier.uri | http://hdl.handle.net/1942/14849 | - |
dc.description.abstract | The objective of this research was to demonstrate a framework for drawing inference from sensitivity analyses of incomplete longitudinal clinical trial data via re-analysis of data from a confirmatory clinical trial in depression. A likelihood-based approach that assumed missing at random (MAR) was the primary analysis. Robustness to departure from MAR was assessed by comparing the primary result to those from a series of analyses that employed varying missing not at random (MNAR)assumptions (selection models, pattern mixture models and shared parameter models) and to MAR methods that used inclusive models. The key sensitivity analysis used multiple imputation assuming that after dropout the trajectory of drug-treated patients was that of placebo treated patients with a similar outcome history (placebo multiple imputation). This result was used as the worst reasonable case to define the lower limit of plausible values for the treatment contrast. The endpoint contrast from the primary analysis was - 2.79 (p = .013). In placebo multiple imputation, the result was - 2.17. Results from the other sensitivity analyses ranged from - 2.21 to - 3.87 and were symmetrically distributed around the primary result. Hence, no clear evidence of bias from missing not at random data was found. In the worst reasonable case scenario, the treatment effect was 80% of the magnitude of the primary result. Therefore, it was concluded that a treatment effect existed. The structured sensitivity framwork of using a worst reasonable case result based on a controlled imputation approach with transparent and debatable assumptions supplemented a series of plausible alternative models under varying assumptions was useful in this specific situation and holds promise as a generally useful framework. | - |
dc.language.iso | en | - |
dc.rights | (c) 2012 John Wiley & Sons, Ltd. | - |
dc.subject.other | missing data; clinical trials; sensitivity analysis | - |
dc.title | A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 6 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 1 | - |
dc.identifier.volume | 12 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/pst.1547 | - |
dc.identifier.isi | 000313732700001 | - |
item.contributor | Mallinckrodt, Craig H. | - |
item.contributor | Lin, Q. | - |
item.contributor | MOLENBERGHS, Geert | - |
item.validation | ecoom 2014 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Restricted Access | - |
item.fullcitation | Mallinckrodt, Craig H.; Lin, Q. & MOLENBERGHS, Geert (2012) A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials. In: Pharmaceutical statistics, 12 (1), p. 1-6. | - |
crisitem.journal.issn | 1539-1604 | - |
crisitem.journal.eissn | 1539-1612 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mallinckrodt et al 2012 - A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials.pdf Restricted Access | Published version | 97.73 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
26
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
31
checked on Oct 13, 2024
Page view(s)
66
checked on Sep 6, 2022
Download(s)
58
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.