Sensitivity analysis of longitudinal binary data with non-monotone missing values. (English) Zbl 1069.62101

Minini, Pascal; Chavance, Michel

Biostatistics 5, No. 4, 531-544 (2004).

Summary: 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 studies’ conclusions 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.

MSC:

62P10	Applications of statistics to biology and medical sciences; meta analysis
62N02	Estimation in survival analysis and censored data

Keywords:

Binary data; EM-ignorance; Longitudinal study; Missing; Multiple imputation; Non-monotone; Sensitivity analysis; Uncertainty