Attrition Bias Update 01/14/2014: Missing Data Points: Difference or No Difference — Does it Matter?
A colleague recently wrote us to ask us more about attrition bias. We shared with him that the short answer is that there is less conclusive research on attrition bias than on other key biases. Attrition does not necessarily mean that attrition bias is present and distorting statistically significant results. Attrition may simply result in a smaller sample size which, depending upon how small the remaining population is, may be more prone to chance due to outliers or false non-significant findings due to lack of power.
If randomization successfully results in balanced groups, if blinding is successful including concealed allocation of patients to their study groups, if adherence is high, if protocol deviations are balanced and low, if co-interventions are balanced, if censoring rules are used which are unbiased, and if there are no differences between the groups except for the interventions studied, then it may be reasonable to conclude that attrition bias is not present even if attrition rates are large. Balanced baseline comparisons between completers provides further support for such a conclusion as does comparability in reasons for discontinuation, especially if many categories are reported.
On the other hand, other biases may result in attrition bias. For example, imagine a comparison of an active agent to a placebo in a situation in which blinding is not successful. A physician might encourage his or her patient to drop out of a study if they know the patient is on placebo, resulting in biased attrition that, in sufficient numbers, would potentially distort the results from what they would otherwise have been.