TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Kuehne, Felicitas
A1  - Arvandi, Marjan
A1  - Hess, Lisa M.
A1  - Faries, Douglas E.
A1  - Matteucci Gothe, Raffaella
A1  - Gothe, Holger
A1  - Beyrer, Julie
A1  - Zeimet, Alain Gustave
A1  - Stojkov, Igor
A1  - Mühlberger, Nikolai
A1  - Oberaigner, Willi
A1  - Marth, Christian
A1  - Siebert, Uwe
T1  - Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness
JF  - Journal of Clinical Epidemiology
N2  - 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]: 0.82e1.25). The corresponding HRs from the RWD analysis ranged from 0.51 for simple baseline adjustments to 1.41 (95% CI: 1.22e1.64) accounting for immortal time bias with time-varying covariates. Causal trial emulation yielded an HR of 1.12 (95% CI: 0.96e1.28).

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.
KW  - Causal inference
KW  - Comparative effectiveness
KW  - Longitudinal data
KW  - Electronic health records
KW  - Target trial
KW  - Inverse probability weighting
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-27227
SN  - 0895-4356
SS  - 0895-4356
U6  - https://doi.org/10.25968/opus-2722
DO  - https://doi.org/10.25968/opus-2722
VL  - 152
SP  - 269
EP  - 280
ER  -