The use of multiple imputation (MI) in cluster randomised trials with suspected missing not at random (MNAR) outcome
نویسندگان
چکیده
منابع مشابه
The use of multiple imputation (MI) in cluster randomised trials with suspected missing not at random (MNAR) outcome
Methods Missing-ness in the primary outcome (BMI) was explored in relation to all baseline demographic and post-randomisation variables using logistic regression. An imputation model was developed with cluster (study site) included as a factor together with significant predictors of missing outcome, variables in the primary analysis model, variables used to balance the randomisation and the out...
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OBJECTIVE QoL data were routinely collected in a randomised controlled trial (RCT), which employed a reminder system, retrieving about 50% of data originally missing. The objective was to use this unique feature to evaluate possible missingness mechanisms and to assess the accuracy of simple imputation methods. METHODS Those patients responding after reminder were regarded as providing missin...
متن کاملMultiple imputation methods for bivariate outcomes in cluster randomised trials
Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that i...
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the relation between single nucleotide polymorphisms (snps) and some diseases has been concerned by many researchers. also the missing snps are quite common in genetic association studies. hence, this article investigates the relation between existing snps in dnmt1 of human chromosome 19 with colorectal cancer. this article aims is to presents an imputation method for missing snps not at random...
متن کاملImputation strategies for missing binary outcomes in cluster randomized trials
BACKGROUND Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate depen...
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ژورنال
عنوان ژورنال: Trials
سال: 2015
ISSN: 1745-6215
DOI: 10.1186/1745-6215-16-s2-p143