An audit strategy for time-to-event outcomes measured with error: application to five randomized controlled trials in oncology.

BACKGROUND

Measurement error in time-to-event end points complicates interpretation of treatment effects in clinical trials. Non-differential measurement error is unlikely to produce large bias [1]. When error depends on treatment arm, bias is of greater concern. Blinded-independent central review (BICR) of all images from a trial is commonly undertaken to mitigate differential measurement-error bias that may be present in hazard ratios (HRs) based on local evaluations. Similar BICR and local evaluation HRs may provide reassurance about the treatment effect, but BICR adds considerable time and expense to trials.

METHODS

We describe a BICR audit strategy [2] and apply it to five randomized controlled trials to evaluate its use and to provide practical guidelines. The strategy requires BICR on a subset of study subjects, rather than a complete-case BICR, and makes use of an auxiliary-variable estimator.

RESULTS

When the effect size is relatively large, the method provides a substantial reduction in the size of the BICRs. In a trial with 722 participants and a HR of 0.48, an average audit of 28% of the data was needed and always confirmed the treatment effect as assessed by local evaluations. More moderate effect sizes and/or smaller trial sizes required larger proportions of audited images, ranging from 57% to 100% for HRs ranging from 0.55 to 0.77 and sample sizes between 209 and 737.

LIMITATIONS

The method is developed for a simple random sample of study subjects. In studies with low event rates, more efficient estimation may result from sampling individuals with events at a higher rate.

CONCLUSION

The proposed strategy can greatly decrease the costs and time associated with BICR, by reducing the number of images undergoing review. The savings will depend on the underlying treatment effect and trial size, with larger treatment effects and larger trials requiring smaller proportions of audited data.