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Background: Hong Kong maintained extremely low circulation of SARS-CoV-2 until a major community epidemic of Omicron BA.2 starting in January 2022. Both mRNA BNT162b2 (BioNTech/Fosun Pharma) and inactivated CoronaVac (Sinovac) vaccines are widely available, however coverage has remained low in older adults. Vaccine effectiveness in this predominantly infection-naive population is unknown. Methods: We used individual-level case data on mild/moderate, severe/fatal and fatal hospitalized COVID-19 from December 31, 2021 to March 8, 2022, along with census information and coverage data of BNT162b2 and CoronaVac. We used a negative binomial model, adjusting for age and calendar day to estimate vaccine effectiveness of one, two and three dose schedules of both vaccines, and relative effectiveness by number of doses and vaccine type. Findings: A total of 12.7 million vaccine doses were administered in the 7.3 million population of Hong Kong, and we analyzed data from confirmed cases with mild/moderate (N=5,474), severe/fatal (N=5,294) and fatal (N=4,093) COVID-19. Two doses of either vaccine protected against severe disease and death, with higher effectiveness among adults ≥60 years with BNT162b2 (VE: 88.2%, 95% confidence interval, CI: 84.4%, 91.1%) compared to CoronaVac (VE: 74.1%, 95% CI: 67.8%, 79.2%). Three doses of either vaccine offered very high levels of protection against severe outcomes (VE: 98.1%, 95% CI: 97.1%, 98.8%). Interpretation: Third doses of either BNT162b2 or CoronaVac provide substantial additional protection against severe COVID-19 and should be prioritized, particularly in older adults who received CoronaVac primary schedules. Longer follow-up is needed to assess persistence of different vaccine platforms and schedules.

BackgroundComposite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections.MethodsWe re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth’s adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison.ResultsFor the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6–8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17–18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power.ConclusionsThe augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them.Electronic supplementary materialThe online version of this article (10.1186/s13023-018-0819-1) contains supplementary material, which is available to authorized users.

Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.

Background: When new vaccine components or platforms are developed, they will typically need to demonstrate noninferiority or superiority over existing products, resulting in the assessment of relative vaccine effectiveness (rVE). This review aims to identify how rVE evaluation is being performed in studies of influenza to inform a more standardized approach. Methods: We conducted a systematic search on PubMed, Google Scholar, and Web of Science for studies reporting rVE comparing vaccine components, dose, or vaccination schedules. We screened titles, abstracts, full texts, and references to identify relevant articles. We extracted information on the study design, relative comparison made, and the definition and statistical approach used to estimate rVE in each study. Results: We identified 63 articles assessing rVE in influenza virus. Studies compared multiple vaccine components (n = 38), two or more doses of the same vaccine (n = 17), or vaccination timing or history (n = 9). One study compared a range of vaccine components and doses. Nearly two-thirds of all studies controlled for age, and nearly half for comorbidities, region, and sex. Assessment of 12 studies presenting both absolute and relative effect estimates suggested proportionality in the effects, resulting in implications for the interpretation of rVE effects. Conclusions: Approaches to rVE evaluation in practice is highly varied, with improvements in reporting required in many cases. Extensive consideration of methodologic issues relating to rVE is needed, including the stability of estimates and the impact of confounding structure on the validity of rVE estimates.

Background: Clinical trials and other studies commonly assess the effectiveness of an intervention through the use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by > 30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied. Methods: We reviewed a database of core outcome sets (COSs) that covered physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power. Results: Out of the 287 COSs reviewed, we identified 67 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Clinical areas that had particularly high numbers were rheumatology (11 clinical disorders identified), non-solid tumour oncology (10 identified), neurology (9 identified) and cardiovascular (8 identified). Conclusions: The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoints.

This study investigated the effect of "dose" and the components of Cognitive Behavioral Therapy (CBT) on treatment effects. It is a secondary analysis of the ACTION (Assessment of Cognitive Therapy Instead of Neuroleptics) trial which investigated CBT for people with schizophrenia spectrum disorders that chose not to take antipsychotic medication. Using instrumental variable methods, we found a "dose-response" such that each CBT session attended, reduced the primary outcome measure (the PANSS total score) by approximately 0.6 points (95% CI -1.20 to -0.06, p = 0.031). This suggests that length of therapy is important for those that receive CBT in the absence of antipsychotic medication. Secondly, using principal stratification we examined the process variables that modified treatment effects. Findings revealed that those who received a longitudinal formulation in the first 4 sessions of CBT had poorer treatment effects than those who did not, however this finding was not statistically significant (95% CI -37.244, 6.677, p = 0.173). However, it is important to note that these findings were evident in an exploratory analysis with a small sample. Future larger scale studies are needed to help understand components of effective treatment.