An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout
In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the paramet...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2019/1019303 |
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| author | Amal Almohisen Robin Henderson Arwa M. Alshingiti |
| author_facet | Amal Almohisen Robin Henderson Arwa M. Alshingiti |
| author_sort | Amal Almohisen |
| collection | DOAJ |
| description | In any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the parameters of the longitudinal model for simulated data and real data using the Linear Mixed Effect (LME) method. We investigate the consequences of misspecifying the missingness mechanism by deriving the so-called least false values. These are the values the parameter estimates converge to, when the assumptions may be wrong. The knowledge of the least false values allows us to conduct a sensitivity analysis, which is illustrated. This method provides an alternative to a local misspecification sensitivity procedure, which has been developed for likelihood-based analysis. We compare the results obtained by the method proposed with the results found by using the local misspecification method. We apply the local misspecification and least false methods to estimate the bias and sensitivity of parameter estimates for a clinical trial example. |
| format | Article |
| id | doaj-art-521fb371f0f242a9a44af530dde2f654 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-521fb371f0f242a9a44af530dde2f6542025-02-03T06:11:54ZengWileyJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/10193031019303An Alternative Sensitivity Approach for Longitudinal Analysis with DropoutAmal Almohisen0Robin Henderson1Arwa M. Alshingiti2Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaSchool of Mathematics and Statistics, Newcastle University, Newcastle Upon Tyne, UKDepartment of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaIn any longitudinal study, a dropout before the final timepoint can rarely be avoided. The chosen dropout model is commonly one of these types: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR), and Shared Parameter (SP). In this paper we estimate the parameters of the longitudinal model for simulated data and real data using the Linear Mixed Effect (LME) method. We investigate the consequences of misspecifying the missingness mechanism by deriving the so-called least false values. These are the values the parameter estimates converge to, when the assumptions may be wrong. The knowledge of the least false values allows us to conduct a sensitivity analysis, which is illustrated. This method provides an alternative to a local misspecification sensitivity procedure, which has been developed for likelihood-based analysis. We compare the results obtained by the method proposed with the results found by using the local misspecification method. We apply the local misspecification and least false methods to estimate the bias and sensitivity of parameter estimates for a clinical trial example.http://dx.doi.org/10.1155/2019/1019303 |
| spellingShingle | Amal Almohisen Robin Henderson Arwa M. Alshingiti An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout Journal of Probability and Statistics |
| title | An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout |
| title_full | An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout |
| title_fullStr | An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout |
| title_full_unstemmed | An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout |
| title_short | An Alternative Sensitivity Approach for Longitudinal Analysis with Dropout |
| title_sort | alternative sensitivity approach for longitudinal analysis with dropout |
| url | http://dx.doi.org/10.1155/2019/1019303 |
| work_keys_str_mv | AT amalalmohisen analternativesensitivityapproachforlongitudinalanalysiswithdropout AT robinhenderson analternativesensitivityapproachforlongitudinalanalysiswithdropout AT arwamalshingiti analternativesensitivityapproachforlongitudinalanalysiswithdropout AT amalalmohisen alternativesensitivityapproachforlongitudinalanalysiswithdropout AT robinhenderson alternativesensitivityapproachforlongitudinalanalysiswithdropout AT arwamalshingiti alternativesensitivityapproachforlongitudinalanalysiswithdropout |