Single-arm Trials With External Comparators and Confounder Misclassification
How Adjustment Can Fail
Bibliographic Data
| ID | 9101860 |
|---|---|
| Authors | Michael Webster-Clark, Michael Webster‐Clark (0000-0002-8079-8504, University of North Carolina at Chapel Hill, corresponding author), Michele Jonsson Funk (0000-0002-3756-7540, University of North Carolina at Chapel Hill), Til Stürmer (0000-0002-9204-7177, University of North Carolina at Chapel Hill) |
| Year | 2020 |
| Volume | 58 |
| Issue | 12 |
| Pages | 1116-1121 |
| Publication date | 2020-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000001400 |
| PMID | 32925456 |
| OpenAlex | W3086383656 |
| Language | EN |
| References cited | 28 |
BACKGROUND: "Single-arm trials" with external comparators that contrast outcomes in those on experimental therapy to real-world patients have been used to evaluate efficacy and safety of experimental drugs in rare and severe diseases. Regulatory agencies are considering expanding the role these studies can play; guidance thus far has explicitly considered outcome misclassification with little discussion of misclassification of confounding variables. OBJECTIVES: This work uses causal diagrams to illustrate how adjustment for a misclassified confounder can result in estimates farther from the truth than ignoring it completely. This theory is augmented with quantitative examples using plausible values for misclassification of smoking in real-world pharmaceutical claims data. A tool is also provided for calculating bias of adjusted estimates with specific input parameters. RESULTS: When confounder misclassification is similar in both data sources, adjustment generally brings estimates closer to the truth. When it is not, adjustment can generate estimates that are considerably farther from the truth than the crude. While all nonrandomized studies are subject to this potential bias, single-arm studies are particularly vulnerable due to perfect alignment of confounder measurement and treatment group. This is most problematic when the prevalence of the confounder does not differ between data sources and misclassification does, but can occur even with strong confounder-data source associations. DISCUSSION: Researchers should consider differential confounder misclassification when designing protocols for these types of studies. Subsample validation of confounders, followed by imputation or other bias correction methods, may be a key tool for combining trial and real-world data going forward
Confounding · Data mining · Econometrics · Information bias · Selection bias · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Medicine · Meta-analysis and systematic reviews · Statistical Methods in Clinical Trials
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| Highly cited | No |
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