Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness
| dc.rights.license | open | en_US |
| dc.contributor.author | TURNER, E. L. | |
| dc.contributor.author | YAO, L. | |
| dc.contributor.author | LI, F. | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | PRAGUE, Melanie | |
| dc.date.accessioned | 2021-02-19T15:12:28Z | |
| dc.date.available | 2021-02-19T15:12:28Z | |
| dc.date.issued | 2020 | |
| dc.identifier.issn | 1477-0334 (Electronic) 0962-2802 (Linking) | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/26301 | |
| dc.description.abstractEn | The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials. | |
| dc.language.iso | EN | en_US |
| dc.subject | SISTM | |
| dc.title.en | Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness | |
| dc.title.alternative | Stat Methods Med Res | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1177/0962280219859915 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 31293199 | en_US |
| bordeaux.journal | Statistical Methods in Medical Research | en_US |
| bordeaux.page | 1338-1353 | en_US |
| bordeaux.volume | 29 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - U1219 | en_US |
| bordeaux.issue | 5 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.team | SISTM_BPH | |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.export | false | |
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