Dataset Information

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

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.

SUBMITTER: Yoon JH

PROVIDER: S-EPMC8171049 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Publications

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

Yoon Jeong-Hwa JH Dias Sofia S Hahn Seokyung S

BMC medical research methodology 20210601 1

<h4>Background</h4>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 inconsiste ...[more]

PMID: 34074239

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Project description:BackgroundThe unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials.MethodsWe proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials.ResultsThe original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points' deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples.ConclusionsThe refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network.HighlightsWe have refined the unrelated mean effects (UME) model to incorporate multiarm trials properly and to estimate all observed comparisons in complex networks of interventions.Forest plots with posterior summaries of all observed comparisons under the network meta-analysis and refined UME model can uncover the consequences of potential inconsistency in the network.Using complementary plots to investigate the individual data points' deviance contribution in conjunction with model fit measures and estimated heterogeneity aid in detecting possible inconsistency.

Project description:BackgroundData from a randomized multinational phase 3 trial of 320 adults with acute myeloid leukemia (AML) demonstrated that maintenance therapy with 3-week cycles of histamine dihydrochloride plus low-dose interleukin-2 (HDC/IL-2) for up to 18 months significantly improved leukemia-free survival (LFS) but lacked power to detect an overall survival (OS) difference.PurposeTo assess the consistency of treatment benefit across patient subsets and the robustness of data with respect to trial centers and endpoints.MethodsForest plots were constructed with hazard ratios (HRs) of HDC/IL-2 treatment effects versus no treatment (control) for prospectively defined patient subsets. Inconsistency coefficients (I²) and interaction tests (X²) were used to detect any differences in benefit among subsets. Robustness of results to the elimination of individual study centers was performed using "leave-one-center-out" analyses. Associations between treatment effects on the endpoints were evaluated using weighted linear regression between HRs for LFS and OS estimated within countries.ResultsThe benefit of HDC/IL-2 over controls was statistically consistent across all subsets defined by baseline prognostic variables. I² and P-values of X² ranged from 0.00 to 0.51 and 0.14 to 0.91, respectively. Treatment effects were statistically significant in 14 of 28 subsets analyzed. The "leave-one-center-out" analysis confirmed that no single center dominated (P-values ranged from 0.004 to 0.020 [mean 0.009]). The HRs representing the HDC/IL-2 effects on LFS and OS were strongly correlated at the country level (R² = 0.84).LimitationsSmall sample sizes in some of the subsets analyzed.ConclusionsThese analyses confirm the consistency and robustness of the HDC/IL-2 effect as compared with no treatment. LFS may be an acceptable surrogate for OS in future AML trials. Analyses of consistency and robustness may aid interpretation of data from multicenter trials, especially in populations with rare diseases, when the size of randomized clinical trials is limited.Trial registrationClinicalTrials.gov: NCT00003991.

Dataset Information

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

Background

Methods

Results

Conclusions

Publications

A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

Similar Datasets

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