Flexible quantitative bias analysis for unmeasured confounding in subject-level indirect treatment comparisons with...

Indirect treatment comparisons can provide evidence of relative efficacy for novel therapies when implementation of a randomised controlled trial is infeasible. However, such comparisons are vulnerable to unmeasured confounding bias due to incomplete data collection and non-random treatment assignment. Quantitative bias analysis (QBA) is a framework used to assess the sensitivity of a study's conclusions to unmeasured confounding. As indirect comparisons between therapies with differing treatment modalities may result in violation of the proportional hazards (PH) assumption, QBA methods that are applicable in this context are required. However, few QBA methods are valid under PH violation.

We proposed a simulation-based QBA framework which quantifies the sensitivity of the difference in restricted mean survival time (dRMST) to unmeasured confounding, and is therefore valid under violation of the PH assumption. The proposed framework utilises Bayesian data augmentation for the multiple imputation of an unmeasured confounder with user-specified characteristics. Adjustment of dRMST is then implemented in a weighted analysis using the imputed values. The accuracy and precision of our proposed imputation-based adjustment method was assessed through a simulation study. Confounded data was simulated using a common non-PH data generating process, and imputation-based effect estimates were compared against estimates obtained following adjustment for all confounders. Implementation of the proposed QBA framework was also illustrated using a data from an external control arm study demonstrating clear PH violation.

Imputation-based adjustment using Bayesian data augmentation was observed to estimate the true adjusted dRMST with minimal bias. Moreover, the bias was comparable to that observed under adjustment when all confounders were measured. Application of the proposed QBA framework to an indirect treatment comparison study enabled identification of the characteristics of an unmeasured confounder that would be required to nullify the study's conclusions.

Imputation-based adjustment can accurately recover the true adjusted dRMST in the presence of unmeasured confounding with known exposure and outcome associations. Therefore, the proposed QBA framework can correctly determine the characteristics required by an unmeasured confounder to invalidate a study's conclusions. Consequently, this framework enables the construction of sensitivity analyses to investigate the robustness of relative efficacy evidence derived from indirect treatment comparisons which exhibit PH violation.