Academic Journal
Robustness of the Pairwise-Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study.
| Τίτλος: | Robustness of the Pairwise-Fitting Approach Under Missing at Random Dropout: A Case and Simulation Study. |
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| Συγγραφείς: | De Witte D; L-BioStat, KU Leuven, Leuven, Belgium., Verbeke G; L-BioStat, KU Leuven, Leuven, Belgium.; I-BioStat, UHasselt, Diepenbeek, Belgium., Neyens T; L-BioStat, KU Leuven, Leuven, Belgium.; I-BioStat, UHasselt, Diepenbeek, Belgium., Abad AA; L-BioStat, KU Leuven, Leuven, Belgium.; I-BioStat, UHasselt, Diepenbeek, Belgium., Molenberghs G; L-BioStat, KU Leuven, Leuven, Belgium.; I-BioStat, UHasselt, Diepenbeek, Belgium. |
| Πηγή: | Pharmaceutical statistics [Pharm Stat] 2026 Sep-Oct; Vol. 25 (5), pp. e70115. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 101201192 Publication Model: Print Cited Medium: Internet ISSN: 1539-1612 (Electronic) Linking ISSN: 15391604 NLM ISO Abbreviation: Pharm Stat Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester, UK : Wiley, c2002- |
| Ιατρικοί όροι (MeSH): | Patient Dropouts*/statistics & numerical data , Research Design*/statistics & numerical data , Computer Simulation* , Models, Statistical*, Humans ; Likelihood Functions ; Longitudinal Studies ; Bias ; Data Interpretation, Statistical |
| Περίληψη: | In many studies, multiple longitudinal outcomes are collected, and interest lies in studying the association between these outcomes. Joint modeling is then required, but full likelihood estimation becomes infeasible as the number of outcomes increases. To address this, the pairwise-fitting approach was developed. However, the robustness of this pseudo-likelihood-based approach under missing at random (MAR) remains unclear. We investigate the impact of MAR dropout on the pairwise-fitting approach through a case and simulation study and compare the results to full likelihood estimation. In the simulation study, we simulate three continuous longitudinal outcomes so that full likelihood estimation remains computationally feasible, allowing a comparison with the pairwise fitting approach. Various settings are examined, including random intercept and random intercept-and-slope models, in which we vary the standard deviation of the error terms and the degree of correlation between random effects. Our results show that bias remains limited in random intercept models and in most random intercept-and-slope models. However, when the standard deviation of the error terms becomes large compared to that of the random effects, some bias appears in the covariances between the random effects of the outcomes not driving dropout. This bias is mitigated using multiple imputation. As a case study, we analyzed data from a schizophrenia study using both full likelihood and pseudo-likelihood approaches and compared the results. (© 2026 John Wiley & Sons Ltd.) |
| References: | Stat Med. 2025 Feb 10;44(3-4):e10274. (PMID: 39846338) Biometrics. 2006 Jun;62(2):424-31. (PMID: 16918906) J Biopharm Stat. 2016;26(4):601-18. (PMID: 26098411) Stat Methods Med Res. 2014 Feb;23(1):42-59. (PMID: 22523185) Stat Med. 2004 Oct 30;23(20):3093-104. (PMID: 15449333) Br J Psychiatry. 1995 Jun;166(6):712-26; discussion 727-33. (PMID: 7545060) |
| Grant Information: | G0A4121N Fonds Wetenschappelijk Onderzoek |
| Contributed Indexing: | Keywords: MAR; joint modeling; multiple imputation; multivariate longitudinal data; pairwise‐fitting approach; pseudo‐likelihood |
| Entry Date(s): | Date Created: 20260828 Date Completed: 20260828 Latest Revision: 20260831 |
| Update Code: | 20260831 |
| PubMed Central ID: | PMC13524844 |
| DOI: | 10.1002/pst.70115 |
| PMID: | 42665560 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1539-1612 |
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| DOI: | 10.1002/pst.70115 |