Missing CD4+ cell response in randomized clinical trials of maraviroc and dolutegravir. Issue 5 (October 2015)

• Record Type: Journal Article
• Authors: Cuffe, Robert; Barnett, Carly; Granier, Catherine; Machida, Mitsuaki; Wang, Cunshan; Roger, James
• Is Part Of: HIV clinical trials. Volume 16:Issue 5(2015)
• Page Start: 170
• Page End: 177
• Publication Date: 2015-10
• Subjects: HIV Infections -- Chemotherapy -- Periodicals; AIDS (Disease) -- Chemotherapy -- Periodicals; HIV Infections -- Research -- Periodicals; AIDS (Disease) -- Research -- Periodicals; 616.979206105
• DOI: 10.1179/1945577115Y.0000000003
• Languages: German
• ISSNs: 1528-4336

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title>Background:</title> <p>Missing data can compromise inferences from clinical trials, yet the topic has received little attention in the clinical trial community. Shortcomings in commonly used methods used to analyze studies with missing data (complete case, last- or baseline-observation carried forward) have been highlighted in a recent Food and Drug Administration-sponsored report. This report recommends how to mitigate the issues associated with missing data. We present an example of the proposed concepts using data from recent clinical trials.</p> </sec> <sec> <title>Methods:</title> <p>CD4+ cell count data from the previously reported SINGLE and MOTIVATE studies of dolutegravir and maraviroc were analyzed using a variety of statistical methods to explore the impact of missing data. Four methodologies were used: complete case analysis, simple imputation, mixed models for repeated measures, and multiple imputation. We compared the sensitivity of conclusions to the volume of missing data and to the assumptions underpinning each method.</p> </sec> <sec> <title>Results:</title> <p>Rates of missing data were greater in the MOTIVATE studies (35%–68% premature withdrawal) than in SINGLE (12%–20%). The sensitivity of results to assumptions about missing data was related to volume of missing data. Estimates of treatment differences by various analysis methods ranged across a 61 cells/mm<sup>3</sup> window in MOTIVATE and a 22 cells/mm<sup>3</sup> window in SINGLE.</p> </sec> <sec> <title>Conclusions:</title> <p>Where missing data are anticipated, analyses require robust statistical and clinical debate of the necessary but unverifiable underlying statistical assumptions. Multiple imputation makes these assumptions transparent, can accommodate a broad range of scenarios, and is a natural analysis for clinical trials in HIV with missing data.</p> </sec> </abstract>