1. When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?
- Author
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Adrian F. Hernandez, Gregory E. Simon, Alex John London, Jonathan H. Watanabe, Richard Platt, Robert M. Califf, Michael A. Horberg, Jessica M. Franklin, and Nancy A Dreyer
- Subjects
Data Analysis ,medicine.medical_specialty ,Non-Randomized Controlled Trials as Topic ,Clinical Trials and Supportive Activities ,MEDLINE ,Inference ,Therapeutics ,030226 pharmacology & pharmacy ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,Randomized controlled trial ,Bias ,law ,Clinical Research ,Health care ,Credibility ,medicine ,Humans ,Pharmacology (medical) ,Pharmacology & Pharmacy ,Intensive care medicine ,Pharmacology ,Evidence-Based Medicine ,Epidemiologic ,business.industry ,Gold standard ,Confounding ,food and beverages ,Confounding Factors, Epidemiologic ,Pharmacology and Pharmaceutical Sciences ,Confounding Factors ,8.4 Research design and methodologies (health services) ,030220 oncology & carcinogenesis ,Generic health relevance ,Biostatistics ,business ,Health and social care services research - Abstract
The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.
- Published
- 2022