How Valuable are Multiple Treatment Comparison Methods in Evidence-Based Health-Care Evaluation?

Cooper, Nicola J.; Peters, Jaime; Lai, Monica C.W.; Jüni, Peter; Wandel, Simon; Palmer, Steve; Paulden, Mike; Conti, Stefano; Welton, Nicky J; Abrams, Keith R.; Bujkiewicz, Sylwia; Spiegelhalter, David; Sutton, Alex J.

doi:10.1016/j.jval.2010.09.001

Cited by 63 publications

(59 citation statements)

References 22 publications

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“…148 NWMA involves analysing this network as a whole, by assuming consistency across treatment effects, so that a given pairwise comparison B vs C can be derived from trials against a common comparator (A vs B and A vs C trials) even if no B vs C trials exist. 149 NWMA has become increasing popular in decision-making contexts because choosing among more than two treatments requires all pairwise treatment effects to be consistent in this way (the true treatment effects in the decision problem will always be consistent 150,151 ). Given their widespread use in Health Technology Assessment, NWMA commonly uses aggregate data, although there are examples illustrating the value of this approach when individual participant data (IPD) is available, particularly in understanding participant-level effect modification.…”

Section: Methodsmentioning

confidence: 99%

“…148 NWMA involves analysing this network as a whole, by assuming consistency across treatment effects, so that a given pairwise comparison B vs C can be derived from trials against a common comparator (A vs B and A vs C trials) even if no B vs C trials exist. 149 NWMA has become increasing popular in decision-making contexts because choosing among more than two treatments requires all pairwise treatment effects to be consistent in this way (the true treatment effects in the decision problem will always be consistent 150,151 Network meta-analysis extends this analysis by introducing the consistency assumption asshown by equation (6) 1,where 1, j d is defined as the treatment effect of any treatment j in the network compared to a reference treatment (such as standard care), and active(t) and control(t) are the active and control treatments in trial t, respectively. The consistency assumption can further be applied to the t  parameters as shown by equation (7) 258 1 …”

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confidence: 99%

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Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

Patel

Hee

Mistry

et al. 2016

Programme Grants Appl Res

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Total word count including appendix: 93,499 ii ABSTRACT BackgroundThere is good evidence that therapist delivered interventions have modest beneficial effects for people with low back pain (LBP). Identification of subgroups of people with LBP who may benefit from these different treatment approaches is an important research priority. Aim and objectivesOverall aim was to improve the clinical and cost-effectiveness of LBP treatment by providing patients, their clinical advisors, and health service purchasers with better information about which participants are most likely to benefit from which treatment To achieve these objectives required substantial methodological work including the development and evaluation of some novel statistical approaches. This programme of work was not designed to analyse main effect of interventions and no such interpretations should be made.iii MethodsFirstly, we reviewed the literature on treatment moderators and subgroups. We initially invited investigators of trials of therapist-delivered interventions for LBP with >179 participants to share their data with us; some further smaller trials offered to us were also included. Using these trials we developed a repository of individual participant data of therapist delivered interventions for LBP. Using this dataset we sought to identify which participant characteristics, if any, predict response to different treatments (moderators) for clinical and cost effectiveness outcomes.We did an ANCOVA to identify potential moderators to apply in our main analyses.Subsequently we developed and applied three methods of subgroup identification; recursive partitioning (interaction trees and subgroup identification based on a differential effect search), adaptive risk group refinement, and an individual participant data indirect network meta-analysis to identify sub-groups defined by multiple parameters. ResultsWe included data from 19 randomised controlled trials with 9,328 participants (mean age 49 years, 57% females). Our prespecified analyses using recursive partitioning and adaptive risk group refinement performed well and allowed us to identify some subgroups. The differences in the effect size in the different subgroups were typically small, and unlikely to be clinically meaningful. Increasing baseline severity on the outcome of interest was the strongest driver of sub-group identification that we identified. Additionally we explored the application of Bayesian indirect network metaanalysis. This method produced varying probabilities that a particular treatment choice would be most likely to be effective for a specific patient profile. ConclusionThese data lack clinical or cost-effectiveness justification for the use of baseline characteristics in the development of subgroups for back pain. The methodological developments from this work have the potential to be applied in other clinical areas.iv The pooled repository database will serve as a valuable resource to the LBP research community. FundingFunding from the NIHR Programme Grants for Ap...

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Section: Methodsmentioning

confidence: 99%

“…148 NWMA involves analysing this network as a whole, by assuming consistency across treatment effects, so that a given pairwise comparison B vs C can be derived from trials against a common comparator (A vs B and A vs C trials) even if no B vs C trials exist. 149 NWMA has become increasing popular in decision-making contexts because choosing among more than two treatments requires all pairwise treatment effects to be consistent in this way (the true treatment effects in the decision problem will always be consistent 150,151 Network meta-analysis extends this analysis by introducing the consistency assumption asshown by equation (6) 1,where 1, j d is defined as the treatment effect of any treatment j in the network compared to a reference treatment (such as standard care), and active(t) and control(t) are the active and control treatments in trial t, respectively. The consistency assumption can further be applied to the t  parameters as shown by equation (7) 258 1 …”

mentioning

confidence: 99%

Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

Patel

Hee

Mistry

et al. 2016

Programme Grants Appl Res

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“…The MTC analysis produces tighter credibility intervals (CrI, analogous to the confidence interval of frequentist methodology), which implies greater precision of the estimates than the direct comparison analysis. 17 For the purpose of analysis, the 6 stent types were defined: BMS, SES, PES, EES, ZES, and ZES-R. The primary analysis compared each individual DES to BMS, which was used as reference.…”

Section: Mixed-treatment Comparisonsmentioning

confidence: 99%

Short- and Long-Term Outcomes With Drug-Eluting and Bare-Metal Coronary Stents

et al. 2012

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Background-Drug-eluting stents (DES) have been in clinical use for nearly a decade; however, the relative short-and long-term efficacy and safety of DES compared with bare-metal stents (BMS) and among the DES types are less well defined. with each other or against BMS for de novo coronary lesions, enrolling at least 100 patients and with follow-up of at least 6 months. Short-term (Յ1 year) and long-term efficacy (target-vessel revascularization, target-lesion revascularization) and safety (death, myocardial infarction, stent thrombosis) outcomes were evaluated and trial-level data pooled by both mixed-treatment comparison and direct comparison analyses. From 76 randomized clinical trials with 117 762 patient-years of follow-up, compared with BMS, each DES reduced long-term target-vessel revascularization (39%-61%), but the magnitude varied by DES type (EESϳSESϳZES-RϾPESϳZESϾBMS), with a Ͼ42% probability that EES had the lowest target-vessel revascularization rate. There was no increase in the risk of any long-term safety outcomes, including stent thrombosis, with any DES (versus BMS). In addition, there was reduction in myocardial infarction (all DES except PES versus BMS) and stent thrombosis (with EES versus BMS: Rate ratio, 0.51; 95% credibility interval, 0.35-0.73). The safest DES appeared to be EES (Ͼ86% probability), with reduction in myocardial infarction and stent thrombosis compared with BMS. Short-term outcomes were similar to long-term outcomes, with SES, ZES-R, and everolimus-eluting stent being the most efficacious and EES being the safest stent. Conclusions-DES are highly efficacious at reducing the risk of target-vessel revascularization without an increase in any safety outcomes, including stent thrombosis. However, among the DES types, there were considerable differences, such that EES, SES, and ZES-R were the most efficacious and EES was the safest stent. (Circulation. 2012;125:2873-2891.)

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“…Additional interventions (such as placebo or no treatment) may provide useful information for the treatments of interest via indirect evidence. These additional interventions are expected to increase the precision of the results (36), connect a sparse network or help estimate heterogeneity, among other reasons. The concept of joint randomizability should apply to all treatments included in the network.…”

Section: Illustrative Cer Protocolsmentioning

confidence: 99%

Additional considerations are required when preparing a protocol for a systematic review with multiple interventions

Chaimani

Caldwell

et al. 2017

Journal of Clinical Epidemiology

112

105

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. The number of systematic reviews that aim to compare multiple interventions using network meta-analysis is increasing. In this paper, we highlight aspects of a standard systematic review protocol that may need modification when multiple interventions are to be compared. Study Design and setting:We take the protocol format suggested by Cochrane for a standard systematic review as our reference, and compare the considerations for a pairwise review with those required for a valid comparison of multiple interventions. We suggest new sections for protocols of systematic reviews including network meta-analyses with a focus on how to evaluate their assumptions. We provide example text from published protocols to exemplify the considerations. Results and Conclusion:Standard systematic review protocols for pairwise meta-analyses need extensions to accommodate the increased complexity of network meta-analysis. Our suggested modifications are widely applicable to both Cochrane and non-Cochrane systematic reviews involving network meta-analyses. Keywords

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How Valuable are Multiple Treatment Comparison Methods in Evidence-Based Health-Care Evaluation?

Cited by 63 publications

References 22 publications

Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

Short- and Long-Term Outcomes With Drug-Eluting and Bare-Metal Coronary Stents

Additional considerations are required when preparing a protocol for a systematic review with multiple interventions

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