A comparison of methods for longitudinal data with nonignorable dropout with an application in systemic sclerosis
Longitudinal studies in the medical field often experience data loss resulting from subject dropout. The general practice is still dominated by the use of unproven ad-hoc techniques. Modeling methods for longitudinal data with absent values exist and are valid under different missingness as...
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| Format: | Others |
| Language: | en |
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McGill University
2009
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| Online Access: | http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66862 |