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.

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The International Index of Erectile Function (IIEF) is a widely used, multi-dimensional self-report instrument for the evaluation of male sexual function. It is has been recommended as a primary endpoint for clinical trials of erectile dysfunction (ED) and for diagnostic evaluation of ED severity. The IIEF was developed in conjunction with the clinical trial program for sildenafil, and has since been adopted as the 'gold standard' measure for efficacy assessment in clinical trials of ED. It has been linguistically validated in 32 languages and used as a primary endpoint in more than 50 clinical trials. This review summarizes early stages in the psychometric validation of the instrument, its subsequent adoption in randomized clinical trials with sildenafil and other ED therapies, and its use in classifying ED severity and prevalence. The IIEF meets psychometric criteria for test reliability and validity, has a high degree of sensitivity and specificity, and correlates well with other measures of treatment outcome. It has demonstrated consistent and robust treatment responsiveness in studies in USA, Europe and Asia, as well as in a wide range of etiological subgroups. Although only one direct comparator trial has been performed to date, the IIEF is also sensitive to therapeutic effects with treatment agents other than sildenafil. A severity classification for ED has recently been developed, in addition to a brief screening version of the instrument. This review includes the strengths as well as limitations of the IIEF, along with some potential areas for future research.

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.

Background:The 21-item Three-Factor Eating Questionnaire (TFEQ-R21) is a scale that measures three domains of eating behavior: cognitive restraint (CR), uncontrolled eating (UE) and emotional eating (EE). Objectives: To assess the factor structure and reliability of TFEQ-R21 (and if necessary, refine the structure) in diverse populations of obese and non-obese individuals. Design: Data were obtained from obese adults in a United States/Canadian clinical trial (n ¼ 1741), and overweight, obese and normal weight adults in a US web-based survey (n ¼ 1275). Confirmatory factor analyses were employed to investigate the structure of TFEQ-R21 using baseline data from the clinical trial. The model was refined to obtain adequate fit and internal consistency. The refined model was then tested using the web-based data. Relationships between TFEQ domains and body mass index (BMI) were examined in both populations. Results: Clinical data indicated that TFEQ-R21 needed refinement. Three items were removed from the CR domain, producing the revised version TFEQ-R18V2 (Comparative Fit Index (CFI) ¼ 0.91). Testing TFEQ-R18V2 in the web-based sample supported the revised structure (CFI ¼ 0.96; Cronbach's coefficient a of 0.78-0.94). Associations with BMI were small. In the clinical study, the CR domain showed a significant and negative association with BMI. On the basis of the web-based survey, it was shown that the relationship between BMI and CR is population-dependent (obese versus non-obese, healthy versus diabetics). Conclusions: In two independent datasets, the TFEQ-R18V2 showed robust factor structure and good reliability. It may provide a useful tool for characterizing UE, CR and EE.