A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons

Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P.

doi:10.1002/jrsm.1153

Cited by 108 publications

(178 citation statements)

References 51 publications

(62 reference statements)

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“…10,11 The AB method does not bear such risks. In addition, simulation results in the Gaussian data context 12 and real data analyses for binary outcomes 9 have shown that the effect size estimates by this AB method have smaller biases and narrower credible intervals (CIs) than those provided by CB methods in some cases.…”

Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness

Zhang

Chu

Hong

et al. 2015

Stat Methods Med Res

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Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable missingness or missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network metaanalysis may selectively choose treatments to include in the analysis, which may also lead to missingness not at random. In this paper, we extend our previous work to incorporate missingness not at random using selection models. The proposed method is then applied to two network meta-analyses and evaluated through extensive simulation studies. We also provide comprehensive comparisons of a commonly used contrast-based method and the arm-based method via simulations in a technical appendix under missing completely at random and missing at random.

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Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness

Zhang

Chu

Hong

et al. 2015

Stat Methods Med Res

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“…The AB approach focuses on absolute risks for each treatment arm, while the CB approach focuses on relative effects (e.g., ORs under binary case). Existing literature [1–4, 31, 32] has explored and discussed the model assumptions and model fit of the two approaches, and two recent discussion papers have further provided detailed comparisons on their strengths and limitations; see [33, 34]. …”

Section: Methodsmentioning

confidence: 99%

The Impact of Excluding Trials from Network Meta-Analyses – An Empirical Study

2016

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Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, which has an inherent appeal for clinicians, patients, and policy decision makers. Two recent reports have shown that the impact of excluding a treatment on NMAs can be substantial. However, no one has assessed the impact of excluding a trial from NMAs, which is important because many NMAs selectively include trials in the analysis. This article empirically examines the impact of trial exclusion using both the arm-based (AB) and contrast-based (CB) approaches, by reanalyzing 20 published NMAs involving 725 randomized controlled trials and 449,325 patients. For the population-averaged absolute risk estimates using the AB approach, the average fold changes across all networks ranged from 1.004 (with standard deviation 0.004) to 1.072 (with standard deviation 0.184); while the maximal fold changes ranged from 1.032 to 2.349. In 12 out of 20 NMAs, a 1.20-fold or larger change is observed in at least one of the population-averaged absolute risk estimates. In addition, while excluding a trial can substantially change the estimated relative effects (e.g., log odds ratios), there is no systematic difference in terms of changes between the two approaches. Changes in treatment rankings are observed in 7 networks and changes in inconsistency are observed in 3 networks. We do not observe correlations between changes in treatment effects, treatment rankings and inconsistency. Finally, we recommend rigorous inclusion and exclusion criteria, logical study selection process, and reasonable network geometry to ensure robustness and generalizability of the results of NMAs.

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“…Therefore, m + d 1 k and

{\overset{μ}{true}}_{i 1} + {\overset{δ}{true}}_{i, 1 k}

correspond to the treatment-specific fixed effect μ k and the random effect v ik in the arm-based model, respectively. More details of NMA models can be found in the work by Hong et al and discussions of it 47-49 . Due to its similarity to the arm-based model and its strong assumptions, this article does not consider the “contrast-based + baseline” model.…”

Section: Table A1mentioning

confidence: 99%

Sensitivity to Excluding Treatments in Network Meta-analysis

2016

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Network meta-analysis (NMA) of randomized controlled trials is increasingly used to combine both direct evidence comparing treatments within trials and indirect evidence comparing treatments across different trials. When the outcome is binary, the commonly used contrast-based NMA methods focus on relative treatment effects such as odds ratios comparing two treatments. As shown in a recent report, when using contrast-based NMA, the impact of excluding a treatment in the network can be substantial, suggesting a methodological limitation. In addition, relative treatment effects are sometimes not sufficient for patients to make decisions. For example, it can be challenging for patients to trade off efficacy and safety for two drugs if they only know the relative effects, not the absolute effects. A recently proposed arm-based NMA, based on a missing-data framework, provides an alternative approach. It focuses on estimating population-averaged treatment-specific absolute effects. This article examines the influence of treatment exclusion empirically using 14 published NMAs, for both arm-based and contrast-based approaches. The difference between these two NMA approaches is substantial, and it is almost entirely due to single-arm trials. When a treatment is removed from a contrast-based NMA, it is necessary to exclude other treatments in two-arm studies that investigated the excluded treatment; such exclusions are not necessary in arm-based NMA, leading to substantial gain in performance.

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A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons

Cited by 108 publications

References 51 publications

Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness

Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness

The Impact of Excluding Trials from Network Meta-Analyses – An Empirical Study

Sensitivity to Excluding Treatments in Network Meta-analysis

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