Although meta-analyses provide summary effect estimates that help advise patient care, patients often want to compare their overall health to the general population. The Harvard Cancer Risk Index was published in and uses risk ratio estimates and prevalence estimates from original studies across many risk factors to provide an answer to this question.
However, the published version of the formula only uses dichotomous risk factors and its derivation was not provided. The objective of this brief report was to provide the derivation of a more general form of the equation that allows the incorporation of risk factors with three or more levels.
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Synthesizing Econometric Evidence: The Case of Demand Elasticity Estimates
Toggle navigation. Subscribe Register Login. Your Name: optional. Your Email:. Colleague's Email:. Separate multiple e-mails with a ;. Thought you might appreciate this item s I saw at Epidemiology. Such considerations, along with implementation strategies, have appeared in the literature. The Agency of Healthcare Research and Quality developed a framework for determining research gaps using systematic reviews [ 1 ]. Methods for informing aspects of trial design based on a pairwise meta-analysis have also been proposed and include powering a future trial based on a relevant existing meta-analysis [ 2 — 4 ] or investigating how a future trial would alter the meta-analytic summary effect obtained thus far [ 5 , 6 ].
These methods are limited to situations in which existing evidence consists of two interventions. When existing evidence forms a network of interventions, synthesis of available trials can be done using network meta-analysis. Network meta-analysis is increasingly used in health technology assessment HTA to summarize evidence and inform guidelines [ 7 ]. However, its potential to inform trial design has not received much attention.
Methodological developments that use network meta-analysis as a basis for further research [ 3 , 8 ] have been recently collated to form a holistic framework for planning future trials based on a network of interventions [ 9 ].
The framework consists of three parts. The first part asks whether the existing evidence answers the research question.
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This part pertains to interpreting meta-analysis results, which is related to deciding whether existing evidence is conclusive, whether multiple testing is needed when a meta-analysis is regularly updated, and how to interpret evidence from multiple outcomes. The second part of the framework is related to how best to use the existing evidence to answer the research question. The third and last part of the framework addresses how to use the existing evidence to plan future research. The conditional trial design requires that the assumptions of network meta-analysis are plausible and that the credibility of the results is high.
In the case of violation of the transitivity assumption that for each comparison there is an underlying true relative treatment effect which applies to all studies regardless of the treatments compared , or in the presence of studies with a high risk of bias, the existing network of interventions would not provide reliable evidence and thus should not be used to inform the planning of new studies. We conducted a survey of views on the feasibility of the conditional trial design among trial statisticians, methodologists researchers developing methodology , and users of evidence synthesis research.
To this aim, the survey included questions relevant to the three parts of the conditional trial design. In particular, our objectives were to capture opinions and current practices regarding: 1 the decision about whether a meta-analysis answers the research question first part ; 2 the acceptability of network meta-analysis as a technique to enhance the evidence and answer the research question second part ; and 3 the use of evidence synthesis in the planning of future clinical research third part.
Our convenience sample consisted of researchers working in Europe either in nonprofit organizations or in the pharmaceutical industry. We sent a brief description and the link to the survey by email to key personnel within each organization, which included a request to forward it to anyone within their organization who might be interested, or we sent email messages to a mailing list or individuals. We did not track whether an invited person completed the survey, and we sent no reminders. Schematic representation of the parts of the survey to which participants were directed according to their involvement in several aspects of systematic reviews, guidelines, and clinical trials production.
The first part of the survey concerned current practices in deciding whether a meta-analysis answers the research question at hand. Only participants experienced in evidence synthesis and those who had been involved in deciding about funding clinical research were directed to this part.
Certain questions asked participants to choose or report what they are actually doing , in practice, while others asked participants to choose what they think should be done. Topics related to interpretation of the meta-analysis results, how multiple outcomes are integrated, and issues of multiple testing in the context of a continuously updated meta-analysis. A separate section covered issues related to the acceptability of network meta-analysis.
The next part of the survey contained questions about the use of evidence synthesis, as pairwise or network meta-analysis, for the design of clinical trials. For all questions in this part, the term clinical trials referred to randomized, post-marketing e. Participants experienced in clinical trials and those who declared involvement in funding decisions were directed to this part Fig.
Some of the questions were formulated so that the participants answered them in their capacity as citizens who fund research such as EU-funded clinical trials or other research funded by national funds through their taxation. Percentages include missing responses in the denominator. Where a visual analogue scale was used and for the question of rating clinical research proposals submitted for funding, median, 25th, and 75th percentiles are presented.
The rest of the analyses were planned prospectively. All analyses were performed using Stata Opinions and practices of participants regarding evidence-based planning of future trials.
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How do you judge whether a summary treatment effect provides conclusive evidence or whether further research is needed more than one choice allowed? I examine the statistical significance of the summary effect and its CI. I test whether future studies could change the statistical significance of the summary effect.
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Do you think that network meta-analysis should be considered as the preferred evidence synthesis method instead of pairwise meta-analysis? According to your experience, results from relevant meta-analyses are considered to more than one choice allowed :. What do you think is the biggest barrier towards adopting the conditional trial design in designing trials? As a citizen supporting publicly funded research how would you rank from 1 being the top priority to 5 being the least the following proposals tackling the treatments for an important health condition?
Consider also the cost for each research proposal presented in parenthesis in arbitrary units. A well-powered three-arm randomized trial comparing the three most promising interventions none of which is standard care A well-powered three-arm randomized trial comparing the two most promising interventions and standard treatment A well-powered two-arm randomized trial comparing a newly launched treatment and standard treatment A network meta-analysis comparing all available treatments using existing studies Participants were asked about adjustment for multiple testing issues when a meta-analysis is updated with new studies.
Participants were also asked about interpreting evidence from multiple outcomes that bears upon a preference for one of two treatments. The 68 participants who had experience in evidence synthesis were directed to answer questions regarding the second part of the conditional trial design: how to use the existing evidence to answer the research question Fig.
Asked whether they prefer network meta-analysis as an evidence synthesis method to pairwise meta-analysis, participants indicated a comparatively low preference for network meta-analysis. Opinions among researchers on their interpretation of a hypothetical scenario where network meta-analysis provides conclusive evidence that treatment X is better than treatment S while pairwise meta-analysis indicates that further evidence is needed. Participants rated their use of evidence synthesis in the design of clinical trials on a visual rating scale from 0 never to 1 always.
The median value was 0. When asked about the best among five approaches to resolve uncertainty regarding the best pharmaceutical treatment for a given condition, a three-arm randomized trial comparing the two most promising interventions and standard treatment, and a network meta-analysis comparing all treatment alternatives were the most popular options rating medians 2.
The least favorable research design was a large international registry rating median 5. I would answer differently for publicly funded studies. Experienced researchers in evidence synthesis were more likely to have confidence in network meta-analysis. In this survey of methodologists based in Europe, participants reported low to moderate use of evidence synthesis methods in the design of future trials.
Evidence synthesis is used for the design of around half of the trials. The information most used relates to the parameters required for sample size calculations and outcome definitions. Our results broadly agree with those of Clayton et al. The scope of the survey by Clayton et al. Empirical evidence has shown lower uptake of systematic reviews in planning new trials than the findings in the current survey and the survey by Clayton et al. Clarke et al.
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According to their findings, only a small proportion of trial reports attempted to integrate their findings with existing evidence [ 11 , 12 , 15 , 16 ]. Funders of clinical trials often emphasize the importance of using existing evidence in grant applications [ 14 , 22 , 23 ]. The interest of funders in research synthesis dates back to the s when several organizations responsible for funding clinical research started to require systematic reviews of existing research as a prerequisite for considering funding for new trials [ 14 ].
But as Clayton et al. Nasser et al.
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A survey of funding agencies along with a review of their guidance on how trialists should use existing evidence when designing and implementing new trials would be an important step forward. Our study has some limitations that render the generalizability of its results questionable. First, the sample size of our survey was 76 participants, which is relatively small; a bigger sample size would allow us to produce more precise estimates for the outcomes of interest.
Furthermore, using referral or snowball sampling means that we could not estimate the response rate for our survey. Second, we cannot exclude the possibility that the characteristics of participants systematically differed from those who either did not receive the questionnaire or received it but decided not to participate. This indicates that the participants were probably a well-informed sample of methodologists who were up to date with recent developments. Moreover, the questionnaire has not been independently validated and some terms used might have different meaning for researchers with different backgrounds.
A follow-up survey on a larger scale, including representatives from funding agencies, could provide more information on the potential of using existing evidence in the design of new studies. This clarification was made because usually little evidence is available before licensing which constitutes an important barrier to using the proposed method. However, it might be that trials examining licensed treatments are considered phase III because of their size and scope.
Clearer guidance on how comparative effectiveness data can and should be used in the entire process of approval and adoption of new drugs would be of interest [ 25 , 26 ]. This survey indicates a lack of consensus in aspects related to the interpretation of meta-analysis results. None of the answers to the question regarding interpreting evidence from multiple outcomes was selected by more than about a third of participants. Participants also did not agree on the use of adjustment for multiple testing when a meta-analysis is updated.
This lack of consensus is in line with the lack of agreement about using sequential methods in the literature. Opinions range from regularly using sequential meta-analysis [ 27 , 28 ], to adjusting for repeated updates in specific cases [ 29 — 31 ], to never correcting summary treatment effects using sequential methods [ 32 ]. Concerns about the reliability of meta-analysis affect the acceptability of the conditional trial design; we think, however, that such concerns are likely to diminish over time as meta-analysis is increasingly used for decision-making and guideline development.
The second main pillar of skepticism towards the conditional trial design is the perception of trials as independent experiments. It will be interesting to see whether this view will be challenged in the light of increasing awareness of research waste. Resources for health research are limited and thus an economical and ethical allocation of funds for clinical trials requires minimizing human and monetary costs and risks. While certain research funders, clinical trial planners, and journal editors acknowledge the need to consult the existing evidence base before conducting a new trial, in practice these considerations are not concrete and explicit and quantitative methods are rarely used.
We propose that clinical trialists explicitly report e. Further research on ways in which evidence synthesis can be efficiently used in the planning of new trials could use, and possibly combine, considerations from value of information analysis, adaptive design methodology, and formal decision analytic methods. Funding agencies and journal editors could contribute to preventing waste by establishing concrete policies on the use of existing evidence when assessing requests for funding or publishing trials.