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Correspondence to: M L Bell [email protected]
Introduction
Dropout in longitudinal randomised controlled trials is common and a potential source of bias in terms of evidence based medicine. A review of 71 randomised controlled trials in four top medical journals showed dropout rates of 20% or more in 18% of the trials.1 Similar rates were found in a review of quality of life outcomes.2 In specialist journals, the rates are likely to be higher.
When dropout rates differ between treatment arms, so that fewer patients are followed up in one arm than the other, it is sometimes called “differential dropout” or “differential attrition.” Despite extensive literature on incomplete data methods for randomised controlled trials, guidance from the CONSORT reports, and the National Research Council’s recent report on missing data,3 many applied researchers have misconceptions about how to handle dropout.1 2 4 Two common misunderstandings about differential dropout need to be debunked:
Myth 1—if dropout rates in longitudinal clinical trials are similar between study arms, bias is not a concern.
Myth 2—if dropout rates are dissimilar between study arms, the results will necessarily be biased.
Although differential dropout can bias results, equal dropout rates between study arms does not imply that results will be unbiased (myth 1). The inverse is true as well: unequal dropout rates do not mean the results are biased (myth 2). Whether dropout rates between the arms are differential or not can be a red herring; the two key factors are the type of missingness and the statistical analysis. Our aims were to show that for continuous outcome data biased estimates of treatment effects can be obtained when no differential dropout occurs (refute myth 1); unbiased estimates of treatment effects can be obtained when differential dropout occurs (refute myth 2); and when missingness is non-random, the degree of bias depends, in part, on the actual mechanism of dropout. We begin with an example from a real randomised controlled trial and then show the generalisability of our assertions with a computer simulation study (see glossary).
Glossary
Missing completely at random (MCAR)—Outcome data are MCAR if their missingness does not depend on observed covariates or previous outcomes. As a result, no systematic differences exist between dropouts and completers. For example,...