Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out
Longitudinally observed quality of life data with large amounts of drop‐out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern‐mixture models. These are also straightforward to implement, but result...
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| Veröffentlicht in: | Statistics in medicine 2002-04, Vol.21 (8), p.1023-1041 |
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| container_title | Statistics in medicine |
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| creator | Michiels, Bart Molenberghs, Geert Bijnens, Luc Vangeneugden, Tony Thijs, Herbert |
| description | Longitudinally observed quality of life data with large amounts of drop‐out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern‐mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop‐out mechanisms. Since selection models and pattern‐mixture models are based upon different factorizations of the joint distribution of measurement and drop‐out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis. Copyright © 2002 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.1064 |
| format | Article |
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Med</addtitle><date>2002-04-30</date><risdate>2002</risdate><volume>21</volume><issue>8</issue><spage>1023</spage><epage>1041</epage><pages>1023-1041</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><abstract>Longitudinally observed quality of life data with large amounts of drop‐out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern‐mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop‐out mechanisms. Since selection models and pattern‐mixture models are based upon different factorizations of the joint distribution of measurement and drop‐out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis. Copyright © 2002 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>11933032</pmid><doi>10.1002/sim.1064</doi><tpages>19</tpages></addata></record> |
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| ispartof | Statistics in medicine, 2002-04, Vol.21 (8), p.1023-1041 |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Aged Antineoplastic Agents - therapeutic use Biological and medical sciences Breast Neoplasms - drug therapy Carcinoma - drug therapy Computerized, statistical medical data processing and models in biomedicine delta method Female Humans linear mixed model Longitudinal Studies Medical sciences Medical statistics missing data Models, Statistical Neoplasms, Hormone-Dependent - drug therapy Patient Dropouts Quality of Life repeated measures Triazoles - therapeutic use |
| title | Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out |
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