SummaryBackgroundSince the 1918 influenza pandemic, non-randomised studies and small clinical trials have suggested that convalescent plasma or anti-influenza hyperimmune intravenous immunoglobulin (hIVIG) might have clinical benefit for patients with influenza infection, but definitive data do not exist. We aimed to evaluate the safety and efficacy of hIVIG in a randomised controlled trial.MethodsThis randomised, double-blind, placebo-controlled trial was planned for 45 hospitals in Argentina, Australia, Denmark, Greece, Mexico, Spain, Thailand, UK, and the USA over five influenza seasons from 2013–14 to 2017–18. Adults (≥18 years of age) were admitted for hospital treatment with laboratory-confirmed influenza A or B infection and were randomly assigned (1:1) to receive standard care plus either a single 500-mL infusion of high-titre hIVIG (0·25 g/kg bodyweight, 24·75 g maximum; hIVIG group) or saline placebo (placebo group). Eligible patients had a National Early Warning score of 2 points or greater at the time of screening and their symptoms began no more than 7 days before randomisation. Pregnant and breastfeeding women were excluded, as well as any patients for whom the treatment would present a health risk. Separate randomisation schedules were generated for each participating clinical site using permuted block randomisation. Treatment assignments were obtained using a web-based application by the site pharmacist who then masked the solution for infusion. Patients and investigators were masked to study treatment. The primary endpoint was a six-category ordinal outcome of clinical status at day 7, ranging in severity from death to resumption of normal activities after discharge. The choice of day 7 was based on haemagglutination inhibition titres from a pilot study. It was analysed with a proportional odds model, using all six categories to estimate a common odds ratio (OR). An OR greater than 1 indicated that, for a given category, patients in the hIVIG group were more likely to be in a better category than those in the placebo group. Prespecified primary analyses for safety and efficacy were based on patients who received an infusion and for whom eligibility could be confirmed. This trial is registered with ClinicalTrials.gov, NCT02287467.Findings313 patients were enrolled in 34 sites between Dec 11, 2014, and May 28, 2018. We also used data from 16 patients enrolled at seven of the 34 sites during the pilot study between Jan 15, 2014, and April 10, 2014. 168 patients were randomly assigned to the hIVIG group and 161 to the placebo group. 21 patients were excluded (12 from the hIVIG group and 9 from the placebo group) because they did not receive an infusion or their eligibility could not be confirmed. Thus, 308 were included in the primary analysis. hIVIG treatment produced a robust rise in haemagglutination inhibition titres against influenza A and smaller rises in influenza B titres. Based on the proportional odds model, the OR on day 7 was 1·25 (95% CI 0·79–1·97; p=0·33). In subgroup analyses for the pr...

Background/Aims A single best endpoint for evaluating treatments of severe influenza requiring hospitalization has not been identified. A novel six-category ordinal endpoint of patient status is being used in a randomized controlled trial (FLU-Intravenous Immunoglobulin - FLU-IVIG) of intravenous immunoglobulin. We systematically examine four factors regarding the use of this ordinal endpoint that may affect power from fitting a proportional odds model: (1) deviations from the proportional odds assumption which result in the same overall treatment effect as specified in the FLU-IVIG protocol and which result in a diminished overall treatment effect, (2) deviations from the distribution of the placebo group assumed in the FLU-IVIG design, (3) the effect of patient misclassification among the six categories, and (4) the number of categories of the ordinal endpoint. We also consider interactions between the treatment effect (i.e. factor 1) and each other factor. Methods We conducted a Monte Carlo simulation study to assess the effect of each factor. To study factor 1, we developed an algorithm for deriving distributions of the ordinal endpoint in the two treatment groups that deviated from proportional odds while maintaining the same overall treatment effect. For factor 2, we considered placebo group distributions which were more or less skewed than the one specified in the FLU-IVIG protocol by adding or subtracting a constant from the cumulative log odds. To assess factor 3, we added misclassification between adjacent pairs of categories that depend on subjective patient/clinician assessments. For factor 4, we collapsed some categories into single categories. Results Deviations from proportional odds reduced power at most from 80% to 77% given the same overall treatment effect as specified in the FLU-IVIG protocol. Misclassification and collapsing categories can reduce power by over 40 and 10 percentage points, respectively, when they affect categories with many patients and a discernible treatment effect. But collapsing categories that contain no treatment effect can raise power by over 20 percentage points. Differences in the distribution of the placebo group can raise power by over 20 percentage points or reduce power by over 40 percentage points depending on how patients are shifted to portions of the ordinal endpoint with a large treatment effect. Conclusion Provided that the overall treatment effect is maintained, deviations from proportional odds marginally reduce power. However, deviations from proportional odds can modify the effect of misclassification, the number of categories, and the distribution of the placebo group on power. In general, adjacent pairs of categories with many patients should be kept separate to help ensure that power is maintained at the pre-specified level.

Background/aims The Food and Drug Administration recommends research into developing well-defined and reliable endpoints to evaluate treatments for severe influenza requiring hospitalization. A novel 6-category ordinal endpoint of patient health status after 7 days that ranges from death to hospital discharge with resumption of normal activities is being used in a randomized placebo-controlled trial of intravenous immunoglobulin (IVIG) for severe influenza (FLU-IVIG). We compare the power of the ordinal endpoint under a proportional odds model to other types of endpoints as a function of various trial parameters. Methods We used closed-form analysis and empirical simulation to compare the power of the ordinal endpoint to time-to-event, longitudinal, and binary endpoints. In the simulation setting, we varied the treatment effect and the distribution of the placebo group across the follow-up period with consideration of adjustment for baseline health status. Results In the analytic setting, ordinal endpoints of high granularity provided greater power than time-to-event endpoints when most patients in the placebo group had either naturally progressed to the category of hospital discharge by day 7 or were far from hospital discharge on day 7. In the simulation setting, adjustment for baseline health status universally raised power for the proportional odds model. Across different placebo group distributions of the ordinal endpoint regardless of adjustment for baseline health status, only time-to-event endpoints yielded higher power than the ordinal endpoint for certain treatment effects. Conclusions In this case study, the FLU-IVIG ordinal endpoint provided greater power than time-to-event, binary, and longitudinal endpoints for most scenarios of the treatment effect and placebo group distribution, including the target population studied for FLU-IVIG. The ordinal endpoint was only surpassed by the time-to-event endpoint when many patients in the placebo group were on the cusp of hospital discharge on day 7 and the follow-up period for the time-to-event endpoint was extended to allow for additional events. Our general approach for evaluating the power of several potential endpoints for an influenza trial can be used for designing other influenza trials with different target populations and for other trials in other disease areas.

Background/AimThe CONSORT (Consolidated Standards of Reporting Trials) statement discourages reporting statistical tests of baseline differences between groups in randomised controlled trials (RCTs). However, this practice is still common in many medical fields. Our aim was to determine the prevalence of this practice in leading sports medicine journals.MethodsWe conducted a comprehensive search in Medline through PubMed to identify RCTs published in the years 2005 and 2015 from 10 high-impact sports medicine journals. Two reviewers independently confirmed the trial design and reached consensus on which articles contained statistical tests of baseline differences.ResultsOur search strategy identified a total of 324 RCTs, with 85 from the year 2005 and 239 from the year 2015. Overall, 64.8% of studies (95% CI (59.6, 70.0)) reported statistical tests of baseline differences; broken down by year, this percentage was 67.1% in 2005 (95% CI (57.1, 77.1)) and 64.0% in 2015 (95% CI (57.9, 70.1)).ConclusionsAlthough discouraged by the CONSORT statement, statistical testing of baseline differences remains highly prevalent in sports medicine RCTs. Statistical testing of baseline differences can mislead authors; for example, by failing to identify meaningful baseline differences in small studies. Journals that ask authors to follow the CONSORT statement guidelines should recognise that many manuscripts are ignoring the recommendation against statistical testing of baseline differences.