Background and objectives: Drawing causal conclusions from real-world data (RWD) poses methodological challenges and risk of bias. We aimed to systematically assess the type and impact of potential biases that may occur when analyzing RWD using the case of progressive ovarian cancer.
Methods: We retrospectively compared overall survival with and without second-line chemotherapy (LOT2) using electronic medical records. Potential biases were determined using directed acyclic graphs. We followed a stepwise analytic approach ranging from crude analysis and multivariable-adjusted Cox model up to a full causal analysis using a marginal structural Cox model with replicates emulating a reference randomized controlled trial (RCT). To assess biases, we compared effect estimates (hazard ratios [HRs]) of each approach to the HR of the reference trial.
Results: The reference trial showed an HR for second line vs. delayed therapy of 1.01 (95% confidence interval [95% CI]: 0.82-1.25). The corresponding HRs from the RWD analysis ranged from 0.51 for simple baseline adjustments to 1.41 (95% CI: 1.22-1.64) accounting for immortal time bias with time-varying covariates. Causal trial emulation yielded an HR of 1.12 (95% CI: 0.96-1.28).
Conclusion: Our study, using ovarian cancer as an example, shows the importance of a thorough causal design and analysis if one is expecting RWD to emulate clinical trial results.
Keywords: Causal inference; Comparative effectiveness; Electronic health records; Inverse probability weighting; Longitudinal data; Target trial.
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