Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

doi:10.1016/j.jclinepi.2022.10.005

Randomized Controlled Trial

. 2022 Dec:152:269-280.

doi: 10.1016/j.jclinepi.2022.10.005. Epub 2022 Oct 15.

Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

Affiliations

¹ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria.
² Eli Lilly and Company, Indianapolis, IN, USA.
³ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Chair of Health Sciences/Public Health, Medical Faculty "Carl Gustav Carus", Technical University Dresden, Dresden, Germany.
⁴ Department of Obstetrics and Gynecology, Innsbruck Medical University, Innsbruck, Austria.
⁵ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Institute for Clinical Epidemiology, Cancer Registry Tyrol, Tirol Kliniken, Innsbruck, Austria.
⁶ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Center for Health Decision Science and Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: usiebert@hsph.harvard.edu.

PMID: 36252741
DOI: 10.1016/j.jclinepi.2022.10.005

Free article

Randomized Controlled Trial

Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

Felicitas Kuehne et al. J Clin Epidemiol. 2022 Dec.

Free article

. 2022 Dec:152:269-280.

doi: 10.1016/j.jclinepi.2022.10.005. Epub 2022 Oct 15.

Affiliations

¹ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria.
² Eli Lilly and Company, Indianapolis, IN, USA.
³ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Chair of Health Sciences/Public Health, Medical Faculty "Carl Gustav Carus", Technical University Dresden, Dresden, Germany.
⁴ Department of Obstetrics and Gynecology, Innsbruck Medical University, Innsbruck, Austria.
⁵ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Institute for Clinical Epidemiology, Cancer Registry Tyrol, Tirol Kliniken, Innsbruck, Austria.
⁶ Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Center for Health Decision Science and Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: usiebert@hsph.harvard.edu.

PMID: 36252741
DOI: 10.1016/j.jclinepi.2022.10.005

Abstract

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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Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

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Causal analyses with target trial emulation for real-world evidence removed large self-inflicted biases: systematic bias assessment of ovarian cancer treatment effectiveness

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