Evidence-based health-care decision making requires comparisons of all relevant competing interventions. In the absence of randomized, controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best choice(s) of treatment. Mixed treatment comparisons, a special case of network meta-analysis, combine direct and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than a traditional meta-analysis. This report from the ISPOR Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on the interpretation of indirect treatment comparisons and network meta-analysis to assist policymakers and health-care professionals in using its findings for decision making. We start with an overview of how networks of randomized, controlled trials allow multiple treatment comparisons of competing interventions. Next, an introduction to the synthesis of the available evidence with a focus on terminology, assumptions, validity, and statistical methods is provided, followed by advice on critically reviewing and interpreting an indirect treatment comparison or network meta-analysis to inform decision making. We finish with a discussion of what to do if there are no direct or indirect treatment comparisons of randomized, controlled trials possible and a health-care decision still needs to be made.

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.

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ObjectiveDue to extended application of pharmacogenetic and pharmacogenomic screening (PGx) tests it is important to assess whether they provide good value for money. This review provides an update of the literature.MethodsA literature search was performed in PubMed and papers published between August 2010 and September 2014, investigating the cost-effectiveness of PGx screening tests, were included. Papers from 2000 until July 2010 were included via two previous systematic reviews. Studies’ overall quality was assessed with the Quality of Health Economic Studies (QHES) instrument.ResultsWe found 38 studies, which combined with the previous 42 studies resulted in a total of 80 included studies. An average QHES score of 76 was found. Since 2010, more studies were funded by pharmaceutical companies. Most recent studies performed cost-utility analysis, univariate and probabilistic sensitivity analyses, and discussed limitations of their economic evaluations. Most studies indicated favorable cost-effectiveness. Majority of evaluations did not provide information regarding the intrinsic value of the PGx test. There were considerable differences in the costs for PGx testing. Reporting of the direction and magnitude of bias on the cost-effectiveness estimates as well as motivation for the chosen economic model and perspective were frequently missing.ConclusionsApplication of PGx tests was mostly found to be a cost-effective or cost-saving strategy. We found that only the minority of recent pharmacoeconomic evaluations assessed the intrinsic value of the PGx tests. There was an increase in the number of studies and in the reporting of quality associated characteristics. To improve future evaluations, scenario analysis including a broad range of PGx tests costs and equal costs of comparator drugs to assess the intrinsic value of the PGx tests, are recommended. In addition, robust clinical evidence regarding PGx tests’ efficacy remains of utmost importance.

The fields of pharmacogenetics and pharmacogenomics have become important practical tools to progress goals in medical and pharmaceutical research and development. As more screening tests are being developed, with some already used in clinical practice, consideration of cost-effectiveness implications is important. A systematic review was performed on the content of and adherence to pharmacoeconomic guidelines of recent pharmacoeconomic analyses performed in the field of pharmacogenetics and pharmacogenomics. Economic analyses of screening strategies for genetic variations, which were evidence-based and assumed to be associated with drug efficacy or safety, were included in the review. The 20 papers included cover a variety of healthcare issues, including screening tests on several cytochrome P450 (CYP) enzyme genes, thiopurine S-methyltransferase (TMPT) and angiotensin-converting enzyme (ACE) insertion deletion (ACE I/D) polymorphisms. Most economic analyses reported that genetic screening was cost effective and often even clearly dominated existing non-screening strategies. However, we found a lack of standardization regarding aspects such as the perspective of the analysis, factors included in the sensitivity analysis and the applied discount rates. In particular, an important limitation of several studies related to the failure to provide a sufficient evidence-based rationale for an association between genotype and phenotype. Future economic analyses should be conducted utilizing correct methods, with adherence to guidelines and including extensive sensitivity analyses. Most importantly, genetic screening strategies should be based on good evidence-based rationales. For these goals, we provide a list of recommendations for good pharmacoeconomic practice deemed useful in the fields of pharmacogenetics and pharmacogenomics, regardless of country and origin of the economic analysis.

Our analyses suggest the potentially favorable cost-effectiveness of population-based screening for albuminuria in the general Dutch population. The results offer health care decision-makers new tools for considering actual implementation of such screening.

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