The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
ACCOUNTING FOR DROPOUT BIAS USING MIXED-EFFECTS MODELS
Abstract
Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic when subjects discontinue (dropout) prior to completing the study. This study assessed the merits of likelihood-based repeated measures analyses (MMRM) compared with fixed-effects analysis of variance where missing values were imputed using the last observation carried forward approach (LOCF) in accounting for dropout bias. Comparisons were made in simulated data and in data from a randomized clinical trial. Subject dropout was introduced in the simulated data to generate ignorable and nonignorable missingness. Estimates of treatment group differences in mean change from baseline to endpoint from MMRM were, on average, markedly closer to the true value than estimates from LOCF in every scenario simulated. Standard errors and confidence intervals from MMRM accurately reflected the uncertainty of the estimates, whereas standard errors and confidence intervals from LOCF underestimated uncertainty.
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“Missing data were managed using LMM, which is based on all observed data and accounts for data missing at random [108-110].”
Citations
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Sorted by citations
Gueorguieva, R., & Krystal, J. H. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.3.310 · Cited by 1,529
“Tradit ional and mixed-effects approaches produce biased results in the presence of informative dropout, but the bias may be smaller in mixed-effects models.(6,7) Mixed models can also be used for sensitivity analysis.”
BackgroundMethodNeurostimulation for Parkinson's disease with early motor complications.
Schuepbach, W., Rau, J., Knudsen, K., Volkmann, J., Krack, P., Timmermann, L., Hälbig, T., Hesekamp, H., Navarro, S., Meier, N., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1205158 · Cited by 1,255
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“This Cox proportional hazards model included treatment and baseline scores for positive symptoms on the Scale of Prodromal Symptoms as the terms in the model.”
MethodInfluential
Metadata
- Authors
- Mallinckrodt, C. H., Clark, W. S., & David, S. R.
- Affiliations
- Eli Lilly (United States) (US)
- Venue
- Journal of Biopharmaceutical Statistics
- Publisher
- Informa UK Limited
- Published
- 2001-01-01
- Volume
- 11
- Issue
- 1-2
- Pages
- 9-21
- ISSN
- 1054-3406 (ISSN-L)
- 1520-5711
- OpenAlex
- W2073155558
- Cited by
- 398
- References
- 14
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 21 · 2023: 12 · 2022: 27 · 2021: 19 · 2020: 20 · 2019: 13 · 2018: 14 · 2017: 14 · 2016: 18 · 2015: 21 · 2014: 16 · 2013: 23 · 2012: 19
- FWCI
- 11.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Biopharmaceutical Statistics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- mixed models, last observation carried forward, missing data, non-ignorable missingness, treatment effect estimation, longitudinal data, randomized controlled trials, standard error estimation
- MeSH terms
- Analysis of Variance, Humans, Longitudinal Studies, Patient Dropouts, Models, Statistical, Bias, Confidence Intervals, Randomized Controlled Trials as Topic
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