Investigating patient exclusion bias in meta-analysis.

BACKGROUND: Trial investigators frequently exclude patients from trial analyses which may bias estimates of the effect of treatment. Combining these estimates in a meta-analysis could aggregate any such biases.
METHODS: To investigate how excluding patients from trials can affect the results of both trials and meta-analyses, we used 14 meta-analyses of individual patient data (IPD) that addressed therapeutic questions in cancer. These included 133 randomized controlled trials (RCT) and 21 905 patients. We explored whether exclusions were related to trial characteristics and categorized the reasons for exclusions. For each RCT and meta-analysis, we compared results of an intention-to-treat analysis of all randomized patients with an analysis based on those patients included in the investigators' analysis.
RESULTS: In all, 92 trials (69%) excluded between 0.3 and 38% of patients randomized. Trials excluding patients tended to be older and larger than those that did not. Most patients were excluded because of ineligibility or protocol violations. Exclusions varied substantially by meta-analysis, more patients tending to be excluded from the treatment arm. Comparing trial analyses there was no clear indication that exclusion of patients altered the results more in favour of either treatment or control. However, comparing meta-analysis results, there was a tendency for those based on 'included' patients to favour the research treatment (P = 0.03). Inconsistency of trial results was often increased as a result of the investigators' exclusions.
CONCLUSIONS: Trials, systematic reviews, and meta-analyses may be prone to bias associated with post-randomization exclusion of patients. Wherever possible, the level of such exclusions should be taken into account when assessing the potential for bias in trials, systematic reviews, and meta-analyses. Ideally, trials, systematic reviews, and meta-analyses should be based on all randomized patients.