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Introduction
The term attrition is widely used in the clinical trials literature to refer to situations where outcome data are not available for some participants. Missing data may invalidate results from clinical trials by reducing precision and, under certain circumstances, yielding biased results. Missing outcome data are an important and common problem in mental health trials as dropout rate may exceed 50% for certain conditions. 1 The Cochrane Collaboration regards incomplete outcome data as a major factor affecting the credibility of a study and requires systematic reviewers to assess the level of bias in all included trials via the Cochrane Risk of Bias tool. 2 3 The ideal solution is to avoid missing data altogether and the National Research Council suggested ideas for limiting the possibility of missing data in the design of clinical trials. 4 Systematic reviews and meta-analysis are retrospective by their own nature and preventive measures for the avoidance of missing outcome data cannot be used.
The intention-to-treat (ITT) principle is widely accepted as the most appropriate way to analyse data in randomised controlled trials (RCT). 5 6 The ITT principle requires analysing all participants in the group they were originally randomised irrespective of the treatment they actually received. The Cochrane Handbook suggests employing an ITT as the least biased way to estimate intervention effects from randomised trials. 7 However, in order to include in the analysis participants whose outcomes are unknown, one needs to employ an imputation technique and make assumptions about missing data, which may affect the reliability and robustness of study findings.
Missing data mechanisms
There are several reasons why data may be missing and not all of them introduce bias. The risk of bias due to missing data depends on the missing data mechanism which describes how propensity for missing data depends on the participant's characteristics and outcomes. Missing data mechanisms can be classified as follows:
I. Missing completely at random (MCAR)
The probability of a missing outcome is the same for all participants and does not depend on any participant characteristic (eg, if a participant misses some appointments due to scheduling difficulties). The MCAR assumption means that the group of participants who provided data is a random sample of the total population of participants, but this...