# Missing continuous outcomes under covariate dependent missingness in cluster randomised trials

arXiv: https://arxiv.org/abs/1603.04176 · HTML: https://mlsanity.com/paper/1603.04176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials](https://mlsanity.com/paper/2106.15737/) (2021)

## Abstract (arXiv)

Attrition is a common occurrence in cluster randomised trials (CRTs) 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 (LMM) analysis, under baseline covariate dependent missingness (CDM) in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete case analysis (CCA) and multiple imputation (MI) are used to handle the missing outcome data. Four possible scenarios are considered depending on whether the missingness mechanisms and covariate effects on outcome are the same or different in the two intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and the same covariate effects, which is arguably unlikely to hold in practice. LMM and MI give unbiased estimates under all four considered scenarios, provided that an interaction of intervention indicator and covariate is included in the model when the covariate effects are different in the two intervention groups. MI gives slightly overestimation of average standard error, which leads to a decrease in power.