Sensitivity analysis of longitudinal binary data with non-monotone missing values.
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.
Laboratoire GlaxoSmithKline, Unité Méthodologie et Biostatistique, 100 route de Versailles, 78163 Marly le Roi, France.
Pascal
P
Minini
Michel
M
Chavance
D000305:Adrenal Cortex Hormones / Q000008:administration & dosage / Q000627:therapeutic use; D000318:Adrenergic beta-Agonists / Q000008:administration & dosage / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy; D004359:Drug Therapy, Combination; D006801:Humans; D020024:Leukotriene Antagonists / Q000008:administration & dosage / Q000627:therapeutic use; D016014:Linear Models; D008137:Longitudinal Studies; D008954:Models, Biological; D016032:Randomized Controlled Trials as Topic / Q000379:methods*
D000305:Adrenal Cortex Hormones; D000318:Adrenergic beta-Agonists; D020024:Leukotriene Antagonists