Abstract
BACKGROUND: The objectives of the present study were to evaluate the performance of a time-to-event data reconstruction method, to assess the bias and efficiency of unanchored matching-adjusted indirect comparison (MAIC) methods for the analysis of time-to-event outcomes, and to propose an approach to adjust the bias of unanchored MAIC when omitted confounders across trials may exist. METHODS: To evaluate the methods using a Monte Carlo approach, a thousand repetitions of simulated data sets were generated for two single-arm trials. In each repetition, researchers were assumed to have access to individual-level patient data (IPD) for one of the trials and the published Kaplan-Meier curve of another. First, we compared the raw data and the reconstructed IPD using Cox regressions to determine the performance of the data reconstruction method. Then, we evaluated alternative unanchored MAIC strategies with varying completeness of covariates for matching in terms of bias, efficiency, and confidence interval coverage. Finally, we proposed a bias factor-adjusted approach to gauge the true effects when unanchored MAIC estimates might be biased due to omitted variables. RESULTS: Reconstructed data sufficiently represented raw data in the sense that the difference between the raw and reconstructed data was not statistically significant over the one thousand repetitions. Also, the bias of unanchored MAIC estimates ranged from minimal to substantial as the set of covariates became less complete. More, the confidence interval estimates of unanchored MAIC were suboptimal even using the complete set of covariates. Finally, the bias factor-adjusted method we proposed substantially reduced omitted variable bias. CONCLUSIONS: Unanchored MAIC should be used to analyze time-to-event outcomes with caution. The bias factor may be used to gauge the true treatment effect.