Missing continuous outcomes under covariate dependent missingness in cluster randomised trials
نویسندگان
چکیده
منابع مشابه
Missing continuous outcomes under covariate dependent missingness in cluster randomised trials
Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missin...
متن کاملMissing binary outcomes under covariate‐dependent missingness in cluster randomised trials
Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline covariate-adjusted cluster-level anal...
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In most randomised trials, some patients fail to provide data for study endpoints. 1 We have previously described the analysis of a trial of acupuncture versus sham acupuncture for the treatment of shoulder pain. 2 All 52 randomised patients provided baseline data on pain and range of motion, but only 45 returned for follow-up testing. The statistical question is how to handle those seven patie...
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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...
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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ژورنال
عنوان ژورنال: Statistical Methods in Medical Research
سال: 2016
ISSN: 0962-2802,1477-0334
DOI: 10.1177/0962280216648357