Analysing randomised controlled trials with missing data: Choice of approach affects conclusions

Abstract: Background: The publication of a wrong conclusion from a randomised trial could have disastrous consequences. Missing data are unavoidable in most studies, but ignoring the problem may introduce bias to the results. Finding an appropriate way to deal with missing data is of paramount importance. We show how the choice of analysis method can impact on the conclusion of the trial with regard to the quality of life outcomes. Methods: Various analysis strategies (analysis of covariance, linear mixed effects model) with and without imputation were carried out to assess treatment difference in four quality of life outcomes in an example clinical trial. Results: Across all four quality of life outcomes, the various analysis approaches provided different estimates of treatment difference, with varying precision, using different numbers of patients. In some cases the decision about statistical significance differed. The results suggested that where possible extra effort should be made to retrieve missing responses. In the presence of data missing at random, simple imputation was inappropriate with multiple imputation or a linear mixed effects model more useful. Conclusion: Different trial conclusions were obtained for a variety of analysis approaches for the same outcome. Collecting as much data as possible is of paramount importance. Careful consideration should be taken when deciding on the most appropriate strategy for analysis when missing data are involved and this strategy should be pre-specified in the trial protocol. Making inappropriate decisions could result in inappropriate conclusions potentially leading to the adoption of a clinical intervention in error. [Copyright &y& Elsevier]