Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

Achana, Felix; Cooper, Nicola J.; Bujkiewicz, Sylwia; Hubbard, Stephanie J.; Kendrick, Denise; Jones, David R.; Sutton, Alex J.

doi:10.1186/1471-2288-14-92

Cited by 51 publications

(89 citation statements)

References 51 publications

(106 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test whether the effects on implicit measures are consistent with effects on explicit measures, we fit a network meta-analytic model that allows the simultaneous analysis of two correlated outcomes (Achana et al, 2014;Efthimiou et al, 2015). This model revealed that effects on implicit measures differed from effects on explicit measures, χ 2 (11, k = 570) = 30.58, p = .001.…”

Section: How Do Changes In Implicit Measures Correspond With Changes mentioning

confidence: 99%

A Meta-Analysis of Procedures to Change Implicit Measures

Forscher¹,

Lai²,

Axt³

et al. 2016

Preprint

137

177

View full text Add to dashboard Cite

Using a novel technique known as network meta-analysis, we synthesized evidence from 492 studies (87,418 participants) to investigate the effectiveness of procedures in changing implicit measures, which we define as response biases on implicit tasks. We also evaluated these procedures’ effects on explicit and behavioral measures. We found that implicit measures can be changed, but effects are often relatively weak (|ds| < .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior.

show abstract

Section: How Do Changes In Implicit Measures Correspond With Changes mentioning

confidence: 99%

A Meta-Analysis of Procedures to Change Implicit Measures

Forscher¹,

Lai²,

Axt³

et al. 2016

Preprint

137

177

View full text Add to dashboard Cite

show abstract

“…Disconnected networks can arise in the case of individual outcome measures within a study even though the studies as a whole might form a connected network. In this situation, it might be possible to borrow strength across outcome measures using a multivariate NMA . Abrams et al used this approach to connect disconnected networks based on whether treatment was first or second line in patients with rheumatoid arthritis.…”

Section: Methods Used To Compare Treatments In Studies Forming Disconmentioning

confidence: 99%

“…In this situation, it might be possible to borrow strength across outcome measures using a multivariate NMA. 74 Abrams et al 54 used this approach to connect disconnected networks based on whether treatment was first or second line in patients with rheumatoid arthritis. Multivariate NMA is a developing area of research that typically synthesises sample estimates of treatment effect (eg, sample log hazard ratio) using a multivariate normal likelihood function.…”

Section: Multivariate Meta-analysismentioning

confidence: 99%

A review of methods for comparing treatments evaluated in studies that form disconnected networks of evidence

Stevens

Fletcher

Downey

et al. 2017

Research Synthesis Methods

View full text Add to dashboard Cite

A network meta-analysis allows a simultaneous comparison between treatments evaluated in randomised controlled trials that share at least one treatment with at least one other study. Estimates of treatment effects may be required for treatments across disconnected networks of evidence, which requires a different statistical approach and modelling assumptions to account for imbalances in prognostic variables and treatment effect modifiers between studies. In this paper, we review and discuss methods for comparing treatments evaluated in studies that form disconnected networks of evidence. Several methods have been proposed but assessing which are appropriate often depends on the clinical context as well as the availability of data. Most methods account for sampling variation but do not always account for others sources of uncertainty. We suggest that further research is required to assess the properties of methods and the use of approaches that allow the incorporation of external information to reflect parameter and structural uncertainty.

show abstract

“…We therefore consider models that assume that one or both of Emax and ED50 are exchangeable across agents within drug class,

{Emax}_{t} \sim N (μ_{Emax}, σ_{Emax}^{2})

, and/or

{ED 50}_{t} \sim N (μ_{ED 50}, σ_{ED 50}^{2})

. This allows the model to “borrow strength” across agents in order to improve estimation …”

Section: Methodsmentioning

confidence: 99%

“…This allows the model to "borrow strength" across agents in order to improve estimation. [21][22][23] Estimation MBMAs are typically estimated using frequentist methods in nonlinear modeling software, such as NONMEM, 24 R 25 , and S-Plus, 26 although they could also be specified using Bayesian methods.…”

Section: Materials and Methods Description Of Modelsmentioning

confidence: 99%

Model‐Based Network Meta‐Analysis: A Framework for Evidence Synthesis of Clinical Trial Data

Mawdsley

Bennetts

Dias

et al. 2016

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

Model‐based meta‐analysis (MBMA) is increasingly used in drug development to inform decision‐making and future trial designs, through the use of complex dose and/or time course models. Network meta‐analysis (NMA) is increasingly being used by reimbursement agencies to estimate a set of coherent relative treatment effects for multiple treatments that respect the randomization within the trials. However, NMAs typically either consider different doses completely independently or lump them together, with few examples of models for dose. We propose a framework, model‐based network meta‐analysis (MBNMA), that combines both approaches, that respects randomization, and allows estimation and prediction for multiple agents and a range of doses, using plausible physiological dose‐response models. We illustrate our approach with an example comparing the efficacies of triptans for migraine relief. This uses a binary endpoint, although we note that the model can be easily modified for other outcome types.

show abstract

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

Cited by 51 publications

References 51 publications

A Meta-Analysis of Procedures to Change Implicit Measures

A Meta-Analysis of Procedures to Change Implicit Measures

A review of methods for comparing treatments evaluated in studies that form disconnected networks of evidence

Model‐Based Network Meta‐Analysis: A Framework for Evidence Synthesis of Clinical Trial Data

Contact Info

Product

Resources

About