A latent-class mixture model for incomplete longitudinal Gaussian data
| Autor: | Geert Molenberghs, Geert Verbeke, Craig H. Mallinckrodt, Caroline Beunckens |
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| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
Statistics and Probability
Biometry Gaussian Normal Distribution Context (language use) computer.software_genre Models Biological General Biochemistry Genetics and Molecular Biology Normal distribution symbols.namesake Econometrics Computer Simulation Longitudinal Studies Mathematics Likelihood Functions Forgetting Models Statistical General Immunology and Microbiology Applied Mathematics General Medicine Missing data Mixture model Random effects model Sample size determination Data Interpretation Statistical Sample Size symbols Data mining latent class nonrandom missingness random effect shared parameter General Agricultural and Biological Sciences Artifacts Epidemiologic Methods computer Algorithms |
| Zdroj: | Missing Data in Clinical Studies |
| Popis: | In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple methods that are valid only if the data are missing completely at random, to more principled ignorable analyses, which are valid under the less restrictive missing at random assumption. The availability of the necessary standard statistical software nowadays allows for such analyses in practice. While the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials. Rather than either forgetting about or blindly shifting to an MNAR framework, the optimal place for MNAR analyses is within a sensitivity-analysis context. One such route for sensitivity analysis is to consider, next to selection models, pattern-mixture models or shared-parameter models. The latter can also be extended to a latent-class mixture model, the approach taken in this article. The performance of the so-obtained flexible model is assessed through simulations and the model is applied to data from a depression trial. CB, GM, and GV gratefully acknowledge the financial support from the IAP research Network P6/03 of the Belgian Government (Belgian Science Policy). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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