Journals Information
Universal Journal of Applied Mathematics Vol. 6(4), pp. 107 - 122
DOI: 10.13189/ujam.2018.060401
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Missingness Mechanism that Incorporated Joint Modeling of Longitudinal Data with Monotone Dropout
ALUKO O. *, MWAMBI H.
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01 Scottsville 3209, Pietermaritzburg, South Africa
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.
KEYWORDS
Sensitivity Sensitivity Analysis, Multiple Imputation, Selection Model, Pattern Mixture Model, Monotone Dropoutnalysis, Multiple imputation, selection model, pattern mixture model, monotone dropout
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] ALUKO O. , MWAMBI H. , "Missingness Mechanism that Incorporated Joint Modeling of Longitudinal Data with Monotone Dropout," Universal Journal of Applied Mathematics, Vol. 6, No. 4, pp. 107 - 122, 2018. DOI: 10.13189/ujam.2018.060401.
(b). APA Format:
ALUKO O. , MWAMBI H. (2018). Missingness Mechanism that Incorporated Joint Modeling of Longitudinal Data with Monotone Dropout. Universal Journal of Applied Mathematics, 6(4), 107 - 122. DOI: 10.13189/ujam.2018.060401.