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Biostatistics 2004 5(4):531-544; doi:10.1093/biostatistics/kxh006
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Biostatistics Vol. 5 No. 4 © Oxford University Press 2004; all rights reserved.

Sensitivity analysis of longitudinal binary data with non-monotone missing values

Pascal Minini

Laboratoire GlaxoSmithKline, Unité Méthodologie et Biostatistique, 100 route de Versailles, 78163 Marly le Roi, France, and INSERM U472, 16 avenue Paul Vaillant-Couturier, 94807 Villejuif, France

Michel Chavance*

INSERM U472, 16 avenue Paul Vaillant-Couturier, 94807 Villejuif, France
chavance{at}vjf.inserm.fr

* To whom correspondence should be addressed.

This paper highlights the consequences of incomplete observations in the analysis of longitudinal binary data, in particular non-monotone missing data patterns. Sensitivity analysis is advocated and a method is proposed based on a log–linear model. A sensitivity parameter that represents the relationship between the response mechanism and the missing data mechanism is introduced. It is shown that although this parameter is identifiable, its estimation is highly questionable. A far better approach is to consider a range of plausible values and to estimate the parameters of interest conditionally upon each value of the sensitivity parameter. This allows us to assess the sensitivity of study's conclusion to assumptions regarding the missing data mechanism. The method is applied to a randomized clinical trial comparing the efficacy of two treatment regimens in patients with persistent asthma.

Keywords: Binary data; EM; Ignorance; Longitudinal study; Missing; Multiple imputation; Non-monotone; Sensitivity analysis; Uncertainty


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