This is an archived version of the Handbook. For the current version, please go to or search for this chapter here.  High risk of bias due to incomplete outcome data

Unacceptable reasons for missing data

A difference in the proportion of incomplete outcome data across groups is of concern if the availability of outcome data is determined by the participants’ true outcomes. For example, if participants with poorer clinical outcomes are more likely to drop out due to adverse effects, and this happens mainly in the experimental group, then the effect estimate will be biased in favour of the experimental intervention. Exclusion of participants due to ‘inefficacy’ or ‘failure to improve’ will introduce bias if the numbers excluded are not balanced across intervention groups. Note that a non-significant result of a statistical test for differential missingness does not confirm the absence of bias, especially in small studies.


Example (of high risk of bias): “In a trial of sibutramine versus placebo to treat obesity, 13/35 were withdrawn from the sibutramine group, 7 of these due to lack of efficacy. 25/34 were withdrawn from the placebo group, 17 due to lack of efficacy. An ‘intention-to-treat’ analysis included only those remaining” (Cuellar 2000) (i.e. only 9 of 34 in the placebo group) .


Even if incomplete outcome data are balanced in numbers across groups, bias can be introduced if the reasons for missing outcomes differ. For example, in a trial of an experimental intervention aimed at smoking cessation it is feasible that a proportion of the control intervention participants could leave the study due to a lack of enthusiasm at receiving nothing novel (and continue to smoke), and that a similar proportion of the experimental intervention group could leave the study due to successful cessation of smoking.


The common approach to dealing with missing outcome data in smoking cessation studies (to assume that everyone who leaves the study continues to smoke) may therefore not always be free from bias. The example highlights the importance of considering reasons for incomplete outcome data when assessing risk of bias. In practice, knowledge of why most participants drop out is often unavailable, although an empirical study has observed that 38 out of 63 trials with missing data provided information on reasons (Wood 2004), and this is likely to improve through the use of the CONSORT Statement (Moher 2001a).


‘As-treated’ (per-protocol) analyses

Eligible participants should be analysed in the groups to which they were randomized, regardless of the intervention that they actually received. Thus, in a study comparing surgery with radiotherapy for treatment of localized prostate cancer, patients who refused surgery and chose radiotherapy subsequent to randomization should be included in the surgery group for analysis. This is because participants’ propensity to change groups may be related to prognosis, in which case switching intervention groups introduces selection bias. Although this is strictly speaking an issue of inappropriate analysis rather than incomplete outcome data, studies in which ‘as treated’ analyses are reported should be rated as at high risk of bias due to incomplete outcome data, unless the number of switches is too small to make any important difference to the estimated intervention effect.


A similarly inappropriate approach to analysis of a study is to focus only on participants who complied with the protocol. A striking example is provided by a trial of the lipid lowering drug, clofibrate (Coronary Drug Project Research Group 1980). The five-year mortality in 1103 men assigned to clofibrate was 20.0%, and in 2789 men assigned to placebo was 20.9% (P=0.55). Those who adhered well to the protocol in the clofibrate group had lower five-year mortality (15.0%) than those who did not (24.6%). However, a similar difference between ‘good adherers’ and ‘poor adherers’ was observed in the placebo group (15.1% vs 28.3%). Thus, adherence was a marker of prognosis rather than modifying the effect of clofibrate. These findings show the serious difficulty of evaluating intervention efficacy in subgroups determined by patient responses to the interventions. Because non-receipt of intervention can be more informative than non-availability of outcome data, there is a high risk of bias in analyses restricted to compliers, even with low rates of incomplete data.