A Note on Partial Covariate-Adjustment and Design Considerations in Noninferiority Trials When Patient-Level Data are not Available
| Autor: | Haitao Chu, Karen Qi, Yong Chen, Guoxing Greg Soon, Lei Nie |
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| Rok vydání: | 2013 |
| Předmět: |
Statistics and Probability
HIV Infections Machine learning computer.software_genre Antiviral Agents Margin (machine learning) Covariate Confidence Intervals Econometrics Humans Pharmacology (medical) Mathematics Pharmacology Flexibility (engineering) Analysis of Variance Models Statistical business.industry Experimental data Hepatitis C Treatment Outcome Research Design Patient level data Sample Size Controlled Clinical Trials as Topic Artificial intelligence business computer |
| Zdroj: | Journal of Biopharmaceutical Statistics. 23:1042-1053 |
| ISSN: | 1520-5711 1054-3406 |
| Popis: | The traditional fixed margin approach to evaluating an experimental treatment through an active-controlled noninferiority trial is simple and straightforward. However, its utility relies heavily on the constancy assumption of the experimental data. The recently developed covariate-adjustment method permits more flexibility and improved discriminatory capacity compared to the fixed margin approach. However, one major limitation of this covariate-adjustment methodology is its adherence on the patient-level data, which may not be accessible to investigators in practice. In this article, under some assumptions, we examine the feasibility of a partial covariate-adjustment approach based on data typically available from journal publications or other public data when the patient-level data are unavailable. We illustrate the usefulness of this approach through two real examples. We also provide design considerations on the efficiency of the partial covariate-adjustment approach. |
| Databáze: | OpenAIRE |
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