Reported effectiveness of COVID-19 monovalent booster vaccines and hybrid immunity against mild and severe Omicron disease in adults: A systematic review and meta-regression analysis.

Background: Waning of COVID-19 vaccine efficacy/effectiveness (VE) has been observed across settings and epidemiological contexts. We conducted a systematic review of COVID-19 VE studies and performed a meta-regression analysis to improve understanding of determinants of waning. Methods: Systematic review of PubMed, medRxiv and the WHO-International Vaccine Access Center database summarizing VE studies on 31 December 2022. Studies were those presenting primary adult VE data from hybrid immunity or third/fourth mRNA COVID-19 monovalent vaccine doses [due to limited data with other vaccines] against Omicron, compared with unvaccinated individuals or individuals eligible for corresponding booster doses but who did not receive them. We used meta-regression models, adjusting for confounders, with weeks since vaccination as a restricted cubic spline, to estimate VE over time since vaccination. Results: We identified 55 eligible studies reporting 269 VE estimates. Most estimates (180/269; 67 %) described effectiveness of third dose vaccination; with 48 (18 %) and 41 (15 %) describing hybrid immunity and fourth dose effectiveness, respectively, mostly (200; 74 %) derived from test-negative design studies. Most estimates (176/269; 65 %) reported VE compared with unvaccinated comparison groups. Estimated VE against mild outcomes declined following third dose vaccination from 62 % (95 % CI: 58 % - 66 %) after 4 weeks to 48 % (41 % - 55 %) after 20 weeks. Fourth dose VE against mild COVID-19 declined from 48 % (41 % - 56 %) after 4 weeks to 47 % (19 % - 65 %) after 20 weeks. VE for severe outcomes was higher and declined in the three-dose group from 90 % (87 % - 92 %) after 4 weeks to 70 % (65 - 74 %) after 20 weeks. Conclusions: Time-since vaccination is an important determinant of booster dose VE, a finding which may support seasonal COVID-19 booster doses. Integration of VE and immunological parameters - and longer-term data including from other vaccine types - are needed to better-understand determinants of clinical protection.

WHO Global Situational Alert System: a mixed methods multistage approach to identify country-level COVID-19 alerts.

BACKGROUND: Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched. METHODS: The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of 'High' or above were added to an operational watchlist and assistance was deployed as needed. RESULTS: Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%-67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available. CONCLUSIONS: The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

Vaccine effectiveness of one, two, and three doses of BNT162b2 and CoronaVac against COVID-19 in Hong Kong: a population-based observational study.

BACKGROUND: Hong Kong maintained low circulation of SARS-CoV-2 until a major community epidemic of the omicron (B.1.1.529) sublineage BA.2 began in January, 2022. Both mRNA (BNT162b2 [Fosun Pharma-BioNTech]) and inactivated CoronaVac (Sinovac, Beijing, China) vaccines are widely available; however, vaccination coverage has been low, particularly in older adults aged 70 years or older. We aimed to assess vaccine effectiveness in this predominantly infection-naive population. METHODS: In this observational study, we used individual-level case data on mild or moderate, severe or fatal, and fatal disease in patients hospitalised with COVID-19 along with census information and coverage data of BNT162b2 and CoronaVac. We used a negative binomial model, adjusting for age, sex, and calendar day to estimate vaccine effectiveness of one, two, and three doses of both BNT162b2 and CoronaVac vaccines, and relative effectiveness by number of doses and vaccine type. FINDINGS: Between Dec 31, 2020, and March 16, 2022, 13·2 million vaccine doses were administered in Hong Kong's 7·4-million population. We analysed data from confirmed cases with mild or moderate (n=5566), severe or fatal (n=8875), and fatal (n=6866) COVID-19. Two doses of either vaccine protected against severe disease and death within 28 days of a positive test, with higher effectiveness among adults aged 60 years or older with BNT162b2 (vaccine effectiveness 89·3% [95% CI 86·6-91·6]) compared with CoronaVac (69·9% [64·4-74·6]). Three doses of either vaccine offered very high levels of protection against severe or fatal outcomes (97·9% [97·3-98·4]). INTERPRETATION: Third doses of either BNT162b2 or CoronaVac provide substantial additional protection against severe COVID-19 and should be prioritised, particularly in older adults older than 60 years and others in high-risk populations who received CoronaVac primary schedules. Longer follow-up is needed to assess duration of protection across different vaccine platforms and schedules. FUNDING: COVID-19 Vaccines Evaluation Program, Chinese Center for Disease Control and Prevention.

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.

Estimation of Relative Vaccine Effectiveness in Influenza: A Systematic Review of Methodology.

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.

Improving power in PSA response analyses of metastatic castration-resistant prostate cancer trials.

BACKGROUND: To determine how much an augmented analysis approach could improve the efficiency of prostate-specific antigen (PSA) response analyses in clinical practice. PSA response rates are commonly used outcome measures in metastatic castration-resistant prostate cancer (mCRPC) trial reports. PSA response is evaluated by comparing continuous PSA data (e.g., change from baseline) to a threshold (e.g., 50% reduction). Consequently, information in the continuous data is discarded. Recent papers have proposed an augmented approach that retains the conventional response rate, but employs the continuous data to improve precision of estimation. METHODS: A literature review identified published prostate cancer trials that included a waterfall plot of continuous PSA data. This continuous data was extracted to enable the conventional and augmented approaches to be compared. RESULTS: Sixty-four articles, reporting results for 78 mCRPC treatment arms, were re-analysed. The median efficiency gain from using the augmented analysis, in terms of the implied increase to the sample size of the original study, was 103.2% (IQR [89.8,190.9%]). CONCLUSIONS: Augmented PSA response analysis requires no additional data to be collected and can be performed easily using available software. It improves precision of estimation to a degree that is equivalent to a substantial sample size increase. The implication of this work is that prostate cancer trials using PSA response as a primary endpoint could be delivered with fewer participants and, therefore, more rapidly with reduced cost.

The World Health Organization's public health intelligence activities during the COVID-19 pandemic response, December 2019 to December 2021.

The coronavirus disease (COVID-19) presented a unique opportunity for the World Health Organization (WHO) to utilise public health intelligence (PHI) for pandemic response. WHO systematically captured mainly unstructured information (e.g. media articles, listservs, community-based reporting) for public health intelligence purposes. WHO used the Epidemic Intelligence from Open Sources (EIOS) system as one of the information sources for PHI. The processes and scope for PHI were adapted as the pandemic evolved and tailored to regional response needs. During the early months of the pandemic, media monitoring complemented official case and death reporting through the International Health Regulations mechanism and triggered alerts. As the pandemic evolved, PHI activities prioritised identifying epidemiological trends to supplement the information available through indicator-based surveillance reported to WHO. The PHI scope evolved over time to include vaccine introduction, emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, unusual clinical manifestations and upsurges in cases, hospitalisation and death incidences at subnational levels. Triaging the unprecedented high volume of information challenged surveillance activities but was managed by collaborative information sharing. The evolution of PHI activities using multiple sources in WHO's response to the COVID-19 pandemic illustrates the future directions in which PHI methodologies could be developed and used.

Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial.

BACKGROUND: Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders and are therefore highly inefficient, resulting in low power. We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we developed free, user-friendly software available in a web-based interface and as R packages. The software consists of two programs: one that supports the analysis of responder endpoints; the second that facilitates sample size estimation. We demonstrate the use of the software to conduct the analysis with both the augmented and standard analysis method using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus. RESULTS: The software outputs similar point estimates with smaller confidence intervals for the odds ratio, risk ratio and risk difference estimators using the augmented approach. The sample size required in each arm for a future trial using the novel approach based on the MUSE data is 50 versus 135 for the standard method, translating to a reduction in required sample size of approximately 63%. CONCLUSIONS: We encourage trialists to use the software demonstrated to implement the augmented methodology in future studies to improve efficiency.

Employing a latent variable framework to improve efficiency in composite endpoint analysis.

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.

Analysis of responder-based endpoints: improving power through utilising continuous components.

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.

Cognitive Behavioral Therapy for antipsychotic-free schizophrenia spectrum disorders: Does therapy dose influence outcome?

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.

Improving the analysis of composite endpoints in rare disease trials.

BACKGROUND: Composite 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. METHODS: We 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. RESULTS: For 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. CONCLUSIONS: The 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.