Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures Article Swipe
Yongdong Ouyang
,
Monica Taljaard
,
Andrew Forbes
,
Fan Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/09622802241248382
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/09622802241248382
Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/09622802241248382
- OA Status
- green
- Cited By
- 12
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399131742
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- OpenAlex ID
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https://openalex.org/W4399131742Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/09622802241248382Digital Object Identifier
- Title
-
Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-05-29Full publication date if available
- Authors
-
Yongdong Ouyang, Monica Taljaard, Andrew Forbes, Fan LiList of authors in order
- Landing page
-
https://doi.org/10.1177/09622802241248382Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11499024Direct OA link when available
- Concepts
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Estimator, Random effects model, Statistics, Linear model, Mathematics, Mixed model, Robust statistics, Variance (accounting), Cluster (spacecraft), Computer science, Econometrics, Meta-analysis, Programming language, Business, Accounting, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
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2025: 11, 2024: 1Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.consideration | 14 |
| abstract_inverted_index.discrete-time | 53 |
| abstract_inverted_index.investigation | 126 |
| abstract_inverted_index.random-effect | 234 |
| abstract_inverted_index.stepped-wedge | 8, 18, 132, 179, 306 |
| abstract_inverted_index.bias-corrected | 149 |
| abstract_inverted_index.random-effects | 118 |
| abstract_inverted_index.random-effects. | 36 |
| abstract_inverted_index.recommendations | 314 |
| abstract_inverted_index.cluster-by-period | 214, 245 |
| abstract_inverted_index.misspecification. | 119, 235 |
| abstract_inverted_index.cluster-by-period, | 52 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.98809119 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |