When literature-based meta-analyses involve outcomes with skewed distributions, the best available data can sometimes be a mixture of results presented on the raw scale and results presented on the logarithmic scale. We review and develop methods for transforming between these results for two-group studies, such as clinical trials and prospective or cross-sectional epidemiological studies. These allow meta-analyses to be conducted using all studies and on a common scale. The methods can also be used to produce a meta-analysis of ratios of geometric means when skewed data are reported on the raw scale for every study. We compare three methods, two of which have alternative standard error formulae, in an application and in a series of simulation studies. We conclude that an approach based on a log-normal assumption for the raw data is reasonably robust to different true distributions; and we provide new standard error approximations for this method. An assumption can be made that the standard deviations in the two groups are equal. This increases precision of the estimates, but if incorrect can lead to very misleading results. Copyright © 2008 John Wiley & Sons, Ltd.

Meta-analyses are fundamental tools for collating and synthesizing large amounts of information, and graphical displays have become the principal tool for presenting the results of multiple studies of the same research question. We review standard and proposed graphical displays for presentation of meta-analytic data, and offer our recommendations on how they might be presented to provide the most useful and user-friendly illustrations. We concentrate on graphs that specifically aim to present similar sorts of univariate results from multiple studies. We start with forest plots and funnel plots, and proceed to Galbraith (or radial) plots, L'Abbé (and related) plots, further plots useful for investigating heterogeneity, plots useful for model diagnostics and plots for illustrating likelihoods and Bayesian meta-analyses. Copyright © 2010 John Wiley & Sons, Ltd.

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Complement C5 inhibition is the standard of care (SoC) for patients with paroxysmal nocturnal hemoglobinuria (PNH) with significant clinical symptoms. Constant and complete suppression of the terminal complement pathway and the high serum concentration of C5 pose challenges to drug development that result in IV-only treatment options. Crovalimab, a sequential monoclonal antibody recycling technology antibody was engineered for extended self-administered subcutaneous dosing of small volumes in diseases amenable for C5 inhibition. A 3-part open-label adaptive phase 1/2 trial was conducted to assess safety, pharmacokinetics, pharmacodynamics, and exploratory efficacy in healthy volunteers (part 1), as well as in complement blockade–naive (part 2) and C5 inhibitor–treated (part 3) PNH patients. Twenty-nine patients were included in part 2 (n = 10) and part 3 (n = 19). Crovalimab concentrations exceeded the prespecified 100-µg/mL level and resulted in complete and sustained terminal complement pathway inhibition in treatment-naive and C5 inhibitor–pretreated PNH patients. Hemolytic activity and free C5 levels were suppressed below clinically relevant thresholds (liposome assay <10 U/mL and <50 ng/mL, respectively). Safety was consistent with the known profile of C5 inhibition. As expected, formation of drug-target-drug complexes was observed in all 19 patients switching to crovalimab, manifesting as transient mild or moderate vasculitic skin reactions in 2 of 19 participants. Both events resolved under continued treatment with crovalimab. Subcutaneous crovalimab (680 mg; 4 mL), administered once every 4 weeks, provides complete and sustained terminal complement pathway inhibition in patients with PNH, warranting further clinical development (ClinicalTrials.gov identifier, NCT03157635).

Meta-analyses of clinical trials with continuous outcome data typically report the effect of an intervention as either a mean difference or a standardized mean difference. These results can be difficult to interpret, and re-expressing the effect size in terms of risk may facilitate understanding and applicability. We describe three methods for obtaining risks in such situations. Two of these methods involve direct transformation of a standardized mean difference to an odds ratio. The third entails estimation of risks in the two groups for a specific cut point. We extend this third approach to a completed meta-analysis by expressing the finding in the format of a single 'meta-study'. We compare the methods in two examples of meta-analyses and in a series of simulation studies that examine their properties in individual studies and in meta-analyses. These simulations show that the methods for expressing meta-analysis results from continuous outcomes are sensitive to underlying distributions, sample sizes and cut points but are remarkably robust to the presence of heterogeneity across studies. We offer suggestions of situations in which the various methods may safely be applied. In particular, if the underlying distribution is approximately normal, then estimation of risks for a specific cut point may be used for large sample sizes; direct transformations may be preferable otherwise. However, if the standard deviations in the two groups are notably different, then none of the methods have good properties. Furthermore, absolute risks are safely estimated after direct transformation only if they are in the region of 20% to 80%.