| Summary: |
Appropriate and efficient decisions about the introduction and reimbursement of health technologies are critical for the sustainability of healthcare systems. Health technology assessment (HTA) is a multidisciplinary process and iswidely used to inform decisions about the adequate use of technologies. The relative effects of different health technologies on health outcomes are key inputs for HTA processes. Gold standard estimates of these relative effects frequently come from systematic reviews and meta-analysis of randomized controlled trials (RCTs). To assure thatoptimal decisions are made, it is therefore essential that trustworthy inputs are available to HTA evaluations.However, a substantial number of trials with significant shortcomings in their conduct are often found in the medical literature and included in systematic reviews. Several studies have reported empirical evidence that the inclusion of such trials in a meta-analysis may introduce bias into the results. When performing a meta-analysis, investigators are therefore confronted with the decision of restricting the analysis only to trials at low risk of bias (RoB), which may ignore a significant amount of the evidence and generate a result that is unbiased but imprecise, or adopt an "allavailable evidence" approach, which will yield higher precision at the expense of an increased bias. The choice of the latter option may deliver a spuriously precise effect estimate for HTA evaluations and generate inappropriatedecisions.
Recently, Bayesian meta-analytical models were developed that make it possible to adjust and down-weight the effect estimates from studies at high RoB, through the incorporation of prior distributions for bias-related parameters estimated from meta-epidemiological data. These models are a cutting-edge methodology that could offer a more favourable compromise between bias and precision than simply restricting the analysis to low risk of bias studies.Given its promising  |
Summary
Appropriate and efficient decisions about the introduction and reimbursement of health technologies are critical for the sustainability of healthcare systems. Health technology assessment (HTA) is a multidisciplinary process and iswidely used to inform decisions about the adequate use of technologies. The relative effects of different health technologies on health outcomes are key inputs for HTA processes. Gold standard estimates of these relative effects frequently come from systematic reviews and meta-analysis of randomized controlled trials (RCTs). To assure thatoptimal decisions are made, it is therefore essential that trustworthy inputs are available to HTA evaluations.However, a substantial number of trials with significant shortcomings in their conduct are often found in the medical literature and included in systematic reviews. Several studies have reported empirical evidence that the inclusion of such trials in a meta-analysis may introduce bias into the results. When performing a meta-analysis, investigators are therefore confronted with the decision of restricting the analysis only to trials at low risk of bias (RoB), which may ignore a significant amount of the evidence and generate a result that is unbiased but imprecise, or adopt an "allavailable evidence" approach, which will yield higher precision at the expense of an increased bias. The choice of the latter option may deliver a spuriously precise effect estimate for HTA evaluations and generate inappropriatedecisions.
Recently, Bayesian meta-analytical models were developed that make it possible to adjust and down-weight the effect estimates from studies at high RoB, through the incorporation of prior distributions for bias-related parameters estimated from meta-epidemiological data. These models are a cutting-edge methodology that could offer a more favourable compromise between bias and precision than simply restricting the analysis to low risk of bias studies.Given its promising performance in generating more precise, valid, and reliable results than standard meta-analysis,this approach was recently included in the Cochrane Handbook for Systematic Reviews of Interventions as a potential strategy to eliminate the bias from meta-analysis results. However, at the present state of knowledge, the Cochrane Collaboration cannot yet recommend bias-adjustment using empirically based priors for routine use, givenits still experimental nature. Issues about the lack of statistical power of meta-epidemiological studies, the assumption that the meta-analysis to be adjusted is exchangeable with those in the meta-epidemiological datasetand, the sensitivity of results to model and prior choice need to be clarified before this method can be recommendedfor routine use in HTA.
Investigating if and how this new methodology could be confidently applied in routine evidence synthesis and bead vantageous to the process of decision-making is therefore of extreme relevance and importance in view of animproved and more efficient resource allocation that strengthens the sustainability of healthcare systems.
The overall goal of this project is to provide empirical evidence on whether bias-adjusted meta-analysis using empirically based priors is likely to be feasible, reliable, and valid to be confidently and routinely used in the setting of evidence synthesis of RCTs so that more trustworthy inputs could be available to HTA evaluations. We will address questions related to sample size requirements of meta-epidemiological datasets, context-dependency of bias,sensitivity to model inputs, and eff ects of bias-adjustment compared to restricting the meta-analysis to low RoBtrials. These insights are critical to inform HTA stakeholders on the suitability of this innovative methodology to the process of evidence synthesis in the setting of decision-making in health care and HTA.
With this end in view, three tasks have been specified. The first one aims to address the feasibility of the method interms of sample size requirements where through simulation studies we will explore the number of meta-analysesrequired for the analysis of meta-epidemiological data. The second task aims to address the validity of themethodology by assessing if prior distributions of biases-related parameters are exchangeable between meta-analyses with diff erent outcomes and comparators and, if bias-adjustment models shift the pooled eff ect size of themeta-analysis in the direction of trials at low RoB. The third task aims to address the reliability of the method bymeasuring the agreement between bias-adjustment results using diff erent model and prior assumptions. |