Missing outcome data management in acute stroke trials testing iv thrombolytics. Is there risk of bias?

Missing outcome data may undermine interpretation of randomised clinical trials by weakening power and limiting apparent effect size. We assessed bias and inefficiency of two imputation methods commonly used in stroke trials evaluating the efficacy of iv thrombolysis.

We searched the virtual international stroke trials archive (VISTA)-acute for ischaemic stroke patients with 90-day modified Rankin scale as an outcome, and known thrombolysis status. We excluded any with missing 30-day modified Rankin scale. We planned two analyses; first, we calculated odds ratios for outcome in thrombolysed versus not thrombolysed from imputed-only data, (a) among patients with missing modified Rankin scale 90 and (b) among matched patients with intact data (using propensity score methods and relevant covariates). Imputation approaches were last observation carried forward (LOCF) or multiple imputation. Outcome comparisons used dichotomisation and shift analysis. Thereafter, we calculated whole-population odds ratios using LOCF and multiple imputation (also through dichotomisation and shift analysis); first with the original 1.5% missing outcome data, and then artificially increasing the burden (5%; 10%; 20%; 30%).

We considered 9657 patients from eight of the studies included in VISTA, 3034 (31%) thrombolysed. Missing data replacement by LOCF with analysis by dichotomisation gave the highest estimate of thrombolysis influence. Imputing while increasing the burden of missing data progressively raised the odds ratios estimates, though thresholds for overestimation were 10% for LOCF; 20% for multiple imputation.Discussion: Replacing missing outcome data tended to overestimate differences of thrombolysed versus non-thrombolysed patients, but had minimal impact below a 10% burden of missing data.Conclusion: In the specific context of acute stroke trials testing iv thrombolytics, replacing missing data by carrying forward the last observation tended to overestimate treatment odds ratios more than multiple imputation.