Bayesian meta‐analysis using <scp>SAS PROC BGLIMM</scp>

Rott, Kollin W.; Lin, Lifeng; Hodges, James S.; Siegel, Lianne; Shi, Amy; Chen, Yong; Chu, Haitao

doi:10.1002/jrsm.1513

Cited by 8 publications

(14 citation statements)

References 34 publications

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“…Current software packages are available to implement our one-step models from Bayesian approaches, such as the PROCBGLIMM in SAS. 72,73 Future studies may compare the performance of Bayesian methods with the methods discussed in this article. In addition, one may use other types of bivariate random-effects models, such as the marginal beta-binomial model approach.…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis methods for risk difference: a comparison of different models

Guo

Xiao

Chu

et al. 2022

Preprint

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Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: 1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and 2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

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Section: Discussionmentioning

confidence: 99%

Meta-analysis methods for risk difference: a comparison of different models

Guo

Xiao

Chu

et al. 2022

Preprint

Self Cite

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“…As pointed out and nicely illustrated by Rott et al, 1 the arm‐based approach has the advantage that it allows obtaining estimates of absolute risks. A further advantage of the arm‐based approach is that model specification using a generalized linear mixed model (GLMM) package is straightforward, especially for users familiar with analysis‐of‐variance (ANOVA) procedures from other contexts 2 .…”

Section: Introductionmentioning

confidence: 87%

“…We here closely follow the notation in Rott et al 1 and consider a logit link and a binomial response. Their arm‐based model for the binomial probability

p_{italicik}

of the k th treatment arm

(k = 1, \dots, K)

in the i th study

(i = 1, \dots, T)

logit (p_{ik}) = μ_{k} + v_{italicik}

where

μ_{k}

is the expected log‐odds over studies for the k th treatment, and

v_{i} = (v_{i 1}, v_{i 2}, \dots, v_{iK}) \sim MVN (0, \sum_{K})

, MVN denotes the multivariate normal distribution and

{normal∑}_{K}

is a

K \times K

variance–covariance matrix.…”

Section: Modelsmentioning

confidence: 99%

“…Rott et al 1 provide an excellent illustration of how a Bayesian meta‐analysis for a binary response can be implemented using the recently developed SAS procedure BGLIMM. They illustrate the procedure using two examples, one for simple pairwise meta‐analysis and one for network (multi‐treatment) meta‐analysis.…”

Section: Introductionmentioning

confidence: 99%

“…A further advantage of the arm‐based approach is that model specification using a generalized linear mixed model (GLMM) package is straightforward, especially for users familiar with analysis‐of‐variance (ANOVA) procedures from other contexts 2 . A disadvantage of the arm‐based approach in the form considered in Rott et al 1 is that it recovers inter‐study information, and this prompts many meta‐analysts to favor the contrast‐based approach, which only utilizes the intra‐study information 3–6 . This is often deemed desirable, or indeed required, if wanting to observe the principle of concurrent control, demanding that all inference should be protected by randomization 7 .…”

Section: Introductionmentioning

confidence: 99%

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How to observe the principle of concurrent control in an arm‐based meta‐analysis using SAS procedures GLIMMIX and BGLIMM

Piepho

Madden

2022

Research Synthesis Methods

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Network meta‐analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within‐study information on treatment contrasts and integrates this over a network of studies. One advantage of this approach is that all inference is protected by within‐study randomization. By contrast, arm‐based analyses have been criticized in the past because they may also recover inter‐study information when studies are modeled as random, which is the dominant practice, hence violating the principle of concurrent control, requiring treated individuals to only be compared directly with randomized controls. This issue arises regardless of whether analysis is implemented within a frequentist or a Bayesian framework. Here, we argue that recovery of inter‐study information can be prevented in an arm‐based analysis by adding a fixed study main effect. This simple device means that it is possible to honor the principle of concurrent control in a two‐way analysis‐of‐variance approach that is very easy to implement using generalized linear mixed model procedures and hence may be particularly welcome to those not well versed in the more intricate coding required for a contrast‐based analysis.

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2023

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Bayesian meta‐analysis using SAS PROC BGLIMM

Cited by 8 publications

References 34 publications

Meta-analysis methods for risk difference: a comparison of different models

Meta-analysis methods for risk difference: a comparison of different models

How to observe the principle of concurrent control in an arm‐based meta‐analysis using SAS procedures GLIMMIX and BGLIMM

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