Literature DB >> 27523396

Trimmed means for symptom trials with dropouts.

Thomas Permutt1, Feng Li1.   

Abstract

Dropouts from randomized trials, often for lack of efficacy or toxicity, have usually been handled as 'missing data'. We suggest that they are instead complete observations, just not numeric ones. We propose an exact test of the hypothesis of no drug effect, taking all randomized patients into account, based on a readily interpretable statistic. The method also copes with a drug that is toxic in some patients but beneficial to others, a difficult problem for standard methods. A robust conclusion of efficacy can be drawn with no assumptions other than randomization. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Entities:  

Keywords:  dropout; missing data; robust; trimmed mean

Mesh:

Year:  2016        PMID: 27523396     DOI: 10.1002/pst.1768

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  5 in total

Review 1.  Essential statistical principles of clinical trials of pain treatments.

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2.  Estimands and missing data in clinical trials of chronic pain treatments: advances in design and analysis.

Authors:  Xueya Cai; Jennifer S Gewandter; Hua He; Dennis C Turk; Robert H Dworkin; Michael P McDermott
Journal:  Pain       Date:  2020-10       Impact factor: 7.926

3.  Impute the missing data using retrieved dropouts.

Authors:  Shuai Wang; Haoyan Hu
Journal:  BMC Med Res Methodol       Date:  2022-03-27       Impact factor: 4.615

4.  Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator.

Authors:  Audinga-Dea Hazewinkel; Jack Bowden; Kaitlin H Wade; Tom Palmer; Nicola J Wiles; Kate Tilling
Journal:  Stat Med       Date:  2022-01-31       Impact factor: 2.497

5.  Identifying treatment effects using trimmed means when data are missing not at random.

Authors:  Alex Ocampo; Heinz Schmidli; Peter Quarg; Francesca Callegari; Marcello Pagano
Journal:  Pharm Stat       Date:  2021-06-24       Impact factor: 1.234

  5 in total

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