Abstract
We analyzed repeated measurement of continuous responses with monotone dropout. We are interested in reducing the bias associated with treatment effects, but the results' credibility relies on the validity of the techniques applied to analyze the data, and under the conditions where the techniques gives reliable answers. Furthermore, the robustness of the trial findings are determined through the application of sensitivity analysis which verifies to which extent the results are affected by changes in techniques, values of unmeasured variables and model assumptions. Moreover, the results obtain from the missing not at random (MNAR) is the same as their counterpart in missing at random (MAR). In addition, using multiple imputation (MI) in the analysis also improves the accuracy of results.
Highlights
The prevalence of missing data is high in real application data
When the dependence of the missingness probability is on the observed measurement yio, but not on the unobserved measurements yim, which means that f=f, the process is called missing at random(MAR)
The age-related macular degeneration (ARMD) data arise from a randomized multi-center clinical trial comparing an experimental (Interferon-α) versus placebo for patients diagnosed with the Age-related macular degeneration (ARMD) [8]
Summary
The prevalence of missing data is high in real application data. The techniques for handling missing data is classified into ignorable and non-ignorable, within the likelihood-based (parametric) framework [30]. An advocacy was raised by the national research council [27] for practitioners to conduct sensitivity analyses where the assumptions about the non-identified model components are to determine how much conclusions are being driven by untestable assumptions about the missingness. Considering family of such assumptions, reference was made to the estimates which index the restrictions as a sensitivity parameter. There are avoidance of assumptions by these methods about the full-data distribution, and the parametric model for the missingness given the outcome (reference as to the missing data mechanism) is specified and, it is optional for semiparametric model [13].
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