Evaluating network meta‐analysis and inconsistency using arm‐parameterized model in structural equation modeling

Shih, Ming‐Chieh; Tu, Yu–Kang

doi:10.1002/jrsm.1344

Cited by 5 publications

(12 citation statements)

References 39 publications

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“…Current direct-indirect evidence inconsistency models have also been shown to fall under a class of model which splits the inconsistency parameter between the treatments of interest. 7,12,13 However, the split weights in these models are assigned either out of convenience or symmetricity, rather than solid theoretical justifications. We have shown that our evidence-splitting model implies that the split weights should be proportional to the marginal variance of each treatment outcome, which is guided by the intuition of evidence independence.…”

Section: Discussionmentioning

confidence: 99%

“…Current direct‐indirect evidence inconsistency models have also been shown to fall under a class of model which splits the inconsistency parameter between the treatments of interest 7,12,13 . However, the split weights in these models are assigned either out of convenience or symmetricity, rather than solid theoretical justifications.…”

Section: Discussionmentioning

confidence: 99%

“…Details for implementing arm‐parameterized inconsistency models in structural equation modeling can be found in our previous paper. 13 …”

Section: Evidence‐splitting Model For Studies With K Armsmentioning

confidence: 99%

“…Note that as τ 2 appears in both the mean and variance components, its estimation is not supported by most statistical software packages for linear mixed model. A better option is to undertake the estimation under structural equation modeling framework, 13,17 which allows flexible constraints on both model parameters. Figure 4 shows the path diagrams for studies with direct evidence (Panel B) and without direct evidence (Panel A), respectively.…”

Section: Evidence‐splitting Model For Studies With K Armsmentioning

confidence: 99%

“…We then develop our evidence‐splitting model using the intuitive premise of independence between direct and indirect evidence, and show that the resulting model produces correct estimates for direct evidence, which is compatible with the framework of inconsistency weight distribution proposed by Tu 12 . Finally, we demonstrate how the evidence‐splitting model can be undertaken under the flexible framework of structural equation modeling we previously proposed 13 …”

Section: Introductionmentioning

confidence: 98%

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An evidence‐splitting approach to evaluation of direct‐indirect evidence inconsistency in network meta‐analysis

Shih

2021

Research Synthesis Methods

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Network meta‐analysis (NMA) compares the efficacy and harm between several treatments by combining direct and indirect evidence. The validity of NMA requires that all available evidence form a coherent network. Failure to meet such requirement is known as inconsistency. The most popular approach to inconsistency detection is to compare the direct and indirect evidence for each treatment contrast. Although several models have been proposed to evaluate direct‐indirect evidence inconsistency, there is no comprehensive study on the implications of how these models separate direct from indirect evidence. The main objective of this study is to show that evidence is not properly split into direct and indirect evidence in current inconsistency models, and to propose a novel approach to inconsistency evaluation based on the principle of independence between direct and indirect evidence. We further demonstrated that current models for direct‐indirect evidence inconsistency can potentially lead to misleading conclusions in inconsistency detection and NMA quality appraisal, while our proposed evidence‐splitting model satisfies the principle of independence when splitting the direct from indirect evidence in multi‐arm trials. Moreover, we showed that all these direct‐indirect evidence inconsistency models differ in how the weight of the inconsistency parameter is split between the treatments of interest, yet only the evidence‐splitting model assigns satisfying weights. Finally, we demonstrated how the evidence‐splitting model can be implemented within the structural equation modeling framework. The evidence‐splitting model may be a valuable tool to assess the inconsistency within NMA and evaluate the quality of its evidence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Details for implementing arm‐parameterized inconsistency models in structural equation modeling can be found in our previous paper. 13 …”

Section: Evidence‐splitting Model For Studies With K Armsmentioning

confidence: 99%

Section: Evidence‐splitting Model For Studies With K Armsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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An evidence‐splitting approach to evaluation of direct‐indirect evidence inconsistency in network meta‐analysis

Shih

2021

Research Synthesis Methods

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Bias propagation in network meta‐analysis models

Shih

Song

et al. 2022

Research Synthesis Methods

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Network meta-analysis combines direct and indirect evidence to compare multiple treatments. As direct evidence for one treatment contrast may be indirect evidence for other treatment contrasts, biases in the direct evidence for one treatment contrast may affect not only the estimate for this particular treatment contrast but also estimates of other treatment contrasts. Because network structure determines how direct and indirect evidence are combined and weighted, the impact of biased evidence will be determined by the network geometry. Thus, this study's aim was to investigate how the impact of biased evidence spreads across the whole network and how the propagation of bias is influenced by the network structure. In addition to the popular Lu & Ades model, we also investigate bias propagation in the baseline model and arm-based model to compare the effects of bias in the different models. We undertook extensive simulations under different scenarios to explore how the impact of bias may be affected by the location of the bias, network geometry and the statistical model. Our results

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Persistence of E. coli O157:H7 in urban recreational waters from Spring and Autumn: a comparison analysis

Xie

Zhu

Lyu

et al. 2022

Environ Sci Pollut Res

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Persistence of E. coli O157: H7 (EcO157) in 48 water samples (24 Spring samples and 24 Autumn samples) from 3 urban recreational waters in Changchun City was investigated, and multivariate statistical analysis was performed to correlate survival data with water physicochemical properties and bacterial communities. Our data showed that EcO157 survived longer in Spring samples than in Autumn samples regardless of the lakes. Results revealed that recreational water physicochemical properties and bacterial community in Spring samples were different from those in Autumn samples. Mantel and Partial Mantel tests, as well as co-occurrence network analysis illustrated that EC salinity, TOC and bacterial community were correlated with survival time (ttd) (p < 0.05). Variation partition analysis (VPA) indicated that bacterial community, EC, TOC and TN explained about 64.81% of overall ttd variation in Spring samples, and bacterial community, EC, pH and TP accounted for about 56.59% of overall ttd variation in Autumn samples. Structural equation model (SEM) illustrated that EC indirectly positively affected ttd through bacterial community. The correlation between bacterial community and ttd was negative in Spring samples and positive in Autumn samples. TN appeared a direct positive effect on ttd in Spring samples. TP displayed a direct negative effect on ttd in Autumn samples. Our results concluded that there was seasonal variation in environmental factors that directly or indirectly affected the survival of EcO157 in urban recreational waters.

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Evaluating network meta‐analysis and inconsistency using arm‐parameterized model in structural equation modeling

Cited by 5 publications

References 39 publications

An evidence‐splitting approach to evaluation of direct‐indirect evidence inconsistency in network meta‐analysis

An evidence‐splitting approach to evaluation of direct‐indirect evidence inconsistency in network meta‐analysis

Bias propagation in network meta‐analysis models

Persistence of E. coli O157:H7 in urban recreational waters from Spring and Autumn: a comparison analysis

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