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Single-arm control trials are increasingly proposed as a potential approach for treatment evaluation. However, the limitations of this design restrict its methodological acceptability. Regulatory agencies have raised concerns about this approach, although it is sometimes required in applications based solely on such studies. Consequently, the need for accurate indirect treatment comparisons has become critical, especially when constructing external control arms using routinely collected data as outcome measurements may differ from those recorded in the single-arm trial leading to potential misclassification of outcomes. This study aimed to quantify the bias from ignoring misclassification of a binary outcome within unanchored indirect comparisons, through simulations, and to propose a likelihood-based method to correct this bias (i.e., the outcome-corrected model). Simulations demonstrated that ignoring misclassification results in significant bias and poor coverage probabilities. In contrast, the outcome-corrected model reduced bias, improved 95% confidence interval coverage probability and root mean square error in various scenarios. The methodology was applied to two hepatocellular carcinoma trials illustrating a practical application. The findings underscore the importance of addressing outcome misclassification in indirect comparisons. The proposed correction method may improve reliability in unanchored indirect treatment comparisons.
Nourredine et al. (Mon,) studied this question.