Missingness Mechanism that Incorporated Joint Modeling of Longitudinal Data with Monotone Dropout

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.