1. A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption
- Author
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Jeong Hwa Yoon, Seokyung Hahn, and Sofia Dias
- Subjects
Medicine (General) ,Epidemiology ,Computer science ,Health Informatics ,computer.software_genre ,Inconsistency ,Indirect comparisons ,03 medical and health sciences ,0302 clinical medicine ,Star-shaped network ,Data imputation ,R5-920 ,Consistency (statistics) ,Robustness (computer science) ,Humans ,Computer Simulation ,030212 general & internal medicine ,Sensitivity (control systems) ,Imputation (statistics) ,Network meta-analysis ,Reliability (statistics) ,business.industry ,030503 health policy & services ,Reproducibility of Results ,Usability ,Technical Advance ,Ranking ,Pairwise comparison ,Data mining ,0305 other medical science ,business ,Sensitivity analysis ,computer - Abstract
Background In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency. Methods The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets. Results Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated. Conclusions Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.
- Published
- 2021