GRADE EVIDENCE TO DECISION (EtD) FRAMEWORK FOR COVERAGE DECISIONS.

OBJECTIVES: Coverage decisions are decisions by third party payers about whether and how much to pay for technologies or services, and under what conditions. Given their complexity, a systematic and transparent approach is needed. The DECIDE (Developing and Evaluating Communication Strategies to Support Informed Decisions and Practice Based on Evidence) Project, a GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group initiative funded by the European Union, has developed GRADE Evidence to Decision (EtD) framework for different types of decisions, including coverage ones. METHODS: We used an iterative approach, including brainstorming to generate ideas, consultation with stakeholders, user testing, and pilot testing of the framework. RESULTS: The general structure of the EtD includes formulation of the question, an assessment using twelve criteria, and conclusions. Criteria that are relevant for coverage decisions are similar to those for clinical recommendations from a population perspective. Important differences between the two include the decision-making processes, accountability, and the nature of the judgments that need to be made for some criteria. Although cost-effectiveness is a key consideration when making coverage decisions, it may not be the determining factor. Strength of recommendation is not directly linked to the type of coverage decisions, but when there are important uncertainties, it may be possible to cover an intervention for a subgroup, in the context of research, with price negotiation, or with restrictions. CONCLUSIONS: The EtD provides a systematic and transparent approach for making coverage decisions. It helps ensure consideration of key criteria that determine whether a technology or service should be covered and that judgments are informed by the best available evidence.

GRADE guidelines: 10. Considering resource use and rating the quality of economic evidence.

OBJECTIVES: In this article, we describe how to include considerations about resource utilization when making recommendations according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. STUDY DESIGN AND SETTINGS: We focus on challenges with rating the confidence in effect estimates (quality of evidence) and incorporating resource use into evidence profiles and Summary of Findings (SoF) tables. RESULTS: GRADE recommends that important differences in resource use between alternative management strategies should be included along with other important outcomes in the evidence profile and SoF table. Key steps in considering resources in making recommendations with GRADE are the identification of items of resource use that may differ between alternative management strategies and that are potentially important to decision makers, finding evidence for the differences in resource use, making judgments regarding confidence in effect estimates using the same criteria used for health outcomes, and valuing the resource use in terms of costs for the specific setting for which recommendations are being made. CONCLUSIONS: With our framework, decision makers will have access to concise summaries of recommendations, including ratings of the quality of economic evidence, and better understand the implications for clinical decision making.

Randomisation to protect against selection bias in healthcare trials.

BACKGROUND: Randomised trials use the play of chance to assign participants to comparison groups. The unpredictability of the process, if not subverted, should prevent systematic differences between comparison groups (selection bias). Differences due to chance will still occur and these are minimised by randomising a sufficiently large number of people. OBJECTIVES: To assess the effects of randomisation and concealment of allocation on the results of healthcare studies. SEARCH STRATEGY: We searched the Cochrane Methodology Register, MEDLINE, SciSearch and reference lists up to September 2009. In addition, we screened articles citing included studies (ISI Science Citation Index) and papers related to included studies (PubMed). SELECTION CRITERIA: Eligible study designs were cohorts of studies, systematic reviews or meta-analyses of healthcare interventions that compared random allocation versus non-random allocation or adequate versus inadequate/unclear concealment of allocation in randomised trials. Outcomes of interest were the magnitude and direction of estimates of effect and imbalances in prognostic factors. DATA COLLECTION AND ANALYSIS: We retrieved and assessed studies that appeared to meet the inclusion criteria independently. At least two review authors independently appraised methodological quality and extracted information. We prepared tabular summaries of the results for each comparison and assessed the results across studies qualitatively to identify common trends or discrepancies. MAIN RESULTS: A total of 18 studies (systematic reviews or meta-analyses) met our inclusion criteria. Ten compared random allocation versus non-random allocation and nine compared adequate versus inadequate or unclear concealment of allocation within controlled trials. All studies were at high risk of bias.For the comparison of randomised versus non-randomised studies, four comparisons yielded inconclusive results (differed between outcomes or different modes of analysis); three comparisons showed similar results for random and non-random allocation; two comparisons had larger estimates of effect in non-randomised studies than in randomised trials; and two comparisons had larger estimates of effect in randomised than in non-randomised studies.Five studies found larger estimates of effect in trials with inadequate concealment of allocation than in trials with adequate concealment. The four other studies did not find statistically significant differences. AUTHORS' CONCLUSIONS: The results of randomised and non-randomised studies sometimes differed. In some instances non-randomised studies yielded larger estimates of effect and in other instances randomised trials yielded larger estimates of effect. The results of controlled trials with adequate and inadequate/unclear concealment of allocation sometimes differed. When differences occurred, most often trials with inadequate or unclear allocation concealment yielded larger estimates of effects relative to controlled trials with adequate allocation concealment. However, it is not generally possible to predict the magnitude, or even the direction, of possible selection biases and consequent distortions of treatment effects from studies with non-random allocation or controlled trials with inadequate or unclear allocation concealment.

Economic evidence in decision-making process in laboratory medicine.

Laboratory data play a pivotal role in the clinical decision-making process. Major transformations have occurred in laboratory medicine in recent decades. To face the economic pressures, hospital laboratories are forced to enhance efficiency. Decisions on policy and practice take place at many levels. However, decision-making often does not follow Evidence Based Laboratory Medicine principles. Also, the literature shows limited influence of economic evaluations on health care decisions and diagnostic processes. Several barriers to the use of economic evaluation in decision-making process have been identified, and guidelines tend to focus on issues of effectiveness and have not explicitly considered broader issues, particularly cost. As an example, we analyzed recommendations on the use of brain natriuretic peptide (BNP) or N-terminal fragment of the prohormone BNP (NT-proBNP) in patients with chronic heart failure. All guidelines recommend the use of BNP if available. Nevertheless, none included economic data explicitly, even if economic information exists in the literature. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group, propose using a balance sheet approach, one way of helping decision makers to explicitly consider resource use along with other outcomes when making recommendations. Key aspects of GRADE, such as the explicit presentation of information and the quality evaluation of the economic data can help overcome barriers in the use of economic evaluations in the decision-making in process. This approach can help to give health decision makers, clinical guideline panels and patients, a better appreciation of the overall health benefits, harms and costs of laboratory tests.