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Background There has been a large influx of COVID-19 seroprevalence studies, but comparability between the seroprevalence estimates has been an issue because of heterogeneities in testing platforms and study methodology. One potential source of heterogeneity is the response or participation rate. Methods We conducted a review of participation rates (PR) in SARS-CoV-2 seroprevalence studies collected by SeroTracker and examined their effect on the validity of study conclusions. PR was calculated as the count of participants for whom the investigators had collected a valid sample, divided by the number of people invited to participate in the study. A multivariable beta generalized linear model with logit link was fitted to determine if the PR of international household and community-based seroprevalence studies was associated with the factors of interest, from 1 December 2019 to 10 March 2021. Results We identified 90 papers based on screening and were able to calculate the PR for 35 out of 90 papers (39%), with a median PR of 70% and an interquartile range of 40.92; 61% of the studies did not report PR. Conclusions Many SARS-CoV-2 seroprevalence studies do not report PR. It is unclear what the median PR rate would be had a larger portion not had limitations in reporting. Low participation rates indicate limited representativeness of results. Non-probabilistic sampling frames were associated with higher participation rates but may be less representative. Standardized definitions of participation rate and data reporting necessary for the PR calculations are essential for understanding the representativeness of seroprevalence estimates in the population of interest.

SummaryNaive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.

Count data are ubiquitous in ecology and the Poisson generalized linear model (GLM) is commonly used to model the association between counts and explanatory variables of interest. When fitting this model to the data, one typically proceeds by first confirming that the data is not overdispersed and that there is no excess of zeros. If the data appear to be overdispersed or if there is any zero-inflation, key assumptions of the Poison GLM may be violated and researchers will then typically consider alternatives to the Poison GLM. An important question is whether the potential model selection bias introduced by this data-driven multi-stage procedure merits concern. In this paper, we conduct a large-scale simulation study to investigate the potential consequences of model selection bias that can arise in the simple scenario of analyzing a sample of potentially overdispersed, potentially zero-heavy, count data.

Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios (HRs) can result in biased findings when the proportional hazards (PH) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare results.The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials (RCTs) in terms of progression-free survival (PFS) and overall survival (OS): 1) 1-step or 2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials (FP), 3) NMAs with restricted cubic splines (RCS) for baseline hazard, and 4) restricted mean survival NMA.For PFS, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (FP and RCS) fit better to the data than the other approaches. Estimated HRs obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for OS, non-PH NMA methods captured this uncertainty in the relative treatment effects over time.When the PH assumption is questionable in a subset of the RCTs we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for η ^ 2 and R2 , respectively. A simulation study is conducted to examine the Type I error rates and statistical power of the tests, and a comparison is made with an alternative Bayesian testing approach. The results indicate that the proposed non-inferiority test is a potentially useful tool for 'testing the null'.

Recently, several researchers have claimed that conclusions obtained from a Bayes factor (or the posterior odds) may contradict those obtained from Bayesian posterior estimation. In this short paper, we wish to point out that no such "incompatibility" exists if one is willing to consistently define one's priors and posteriors. The key for compatibility is that the (implied) prior model odds used for testing are the same as those used for estimation. Our recommendation is simple: If one reports a Bayes factor comparing two models, then one should also report posterior estimates which appropriately acknowledge the uncertainty with regards to which of the two models is correct., Comment: 12 pages

In order to determine whether or not an effect is absent based on a statistical test, the recommended frequentist tool is the equivalence test. Typically, it is expected that an appropriate equivalence margin has been specified before any data are observed. Unfortunately, this can be a difficult task. If the margin is too small, then the test's power will be substantially reduced. If the margin is too large, any claims of equivalence will be meaningless. Moreover, it remains unclear how defining the margin afterwards will bias one's results. In this short article, we consider a series of hypothetical scenarios in which the margin is defined post-hoc or is otherwise considered controversial. We also review a number of relevant, potentially problematic actual studies from the clinical trials research, with the aim of motivating a critical discussion as to what is acceptable and desirable in the reporting and interpretation of equivalence tests.

Introduction Pooling (or combining) and analysing observational, longitudinal data at the individual level facilitates inference through increased sample sizes, allowing for joint estimation of study- and individual-level exposure variables, and better enabling the assessment of rare exposures and diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in the health sciences in recent years. The adoption of so-called “causal” methods to account for both/either measured and/or unmeasured confounders is an important addition to the methodological toolkit for understanding the distribution, progression, and consequences of infectious diseases (IDs) and interventions on IDs. In the face of the Covid-19 pandemic and in the absence of systematic randomization of exposures or interventions, the value of these methods is even more apparent. Yet to our knowledge, no studies have assessed how causal methods involving pooling individual-level, observational, longitudinal data are being applied in ID-related research. In this systematic review, we assess how these methods are used and reported in ID-related research over the last 10 years. Findings will facilitate evaluation of trends of causal methods for ID research and lead to concrete recommendations for how to apply these methods where gaps in methodological rigor are identified. Methods and analysis We will apply MeSH and text terms to identify relevant studies from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, and Web of Science. Eligible studies are those that apply causal methods to account for confounding when assessing the effects of an intervention or exposure on an ID-related outcome using pooled, individual-level data from 2 or more longitudinal, observational studies. Titles, abstracts, and full-text articles, will be independently screened by two reviewers using Covidence software. Discrepancies will be resolved by a third reviewer. This systematic review protocol has been registered with PROSPERO (CRD42020204104).

Estimating the COVID-19 infection fatality rate (IFR) has proven to be particularly challenging –and rather controversial– due to the fact that both the data on deaths and the data on the number of individuals infected are subject to many different biases. We consider a Bayesian evidence synthesis approach which, while simple enough for researchers to understand and use, accounts for many important sources of uncertainty inherent in both the seroprevalence and mortality data. With the understanding that the results of one’s evidence synthesis analysis may be largely driven by which studies are included and which are excluded, we conduct two separate parallel analyses based on two lists of eligible studies obtained from two different research teams. The results from both analyses are rather similar. With the first analysis, we estimate the COVID-19 IFR to be 0.31% (95% credible interval of (0.16%, 0.53%)) for a typical community-dwelling population where 9% of the population is aged over 65 years and where the gross-domestic product at purchasing-power parity (GDP at PPP) per capita is $17.8k (the approximate worldwide average). With the second analysis, we obtain 0.32% (95% credible interval of (0.19%, 0.47%)). Our results suggest that, as one might expect, lower IFRs are associated with younger populations (and may also be associated with wealthier populations). For a typical community-dwelling population with the age and wealth of the United States we obtain IFR estimates of 0.43% and 0.41%; and with the age and wealth of the European Union, we obtain IFR estimates of 0.67% and 0.51%.Above all, what’s needed is humility in the face of an intricately evolving body of evidence. The pandemic could well drift or shift into something that defies our best efforts to model and characterize it.Siddhartha Mukherjee,The New YorkerFebruary 22, 2021

When the outcome of interest is semicontinuous and collected longitudinally, efficient testing can be difficult. Daily rainfall data is an excellent example which we use to illustrate the various c...

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for

In response to growing concern about the reliability and reproducibility of published science, researchers have proposed adopting measures of greater statistical stringency, including suggestions to require larger sample sizes and to lower the highly criticized p

Summary The objectives were to determine whether epicardial fat (EAT) is subject to modification, and whether various strategies accomplish this end point and the relationship between weight loss and EAT. A systematic review of the literature following meta-analysis guidelines was conducted using the search strategy ‘epicardial fat’ OR ‘epicardial adipose tissue’ AND ‘diet’ OR ‘exercise’ OR ‘bariatric surgery (BS)’ OR ‘change in body weight’ limited to humans. Eleven articles were identified with 12 intervention approaches of which eight studies showed a statistically significant reduction in EAT. A random-effects meta-analysis suggests an overall significant reduction of 1.12 standardized units (95% CI = [−1.71, −0.54], P value

We introduce a publication policy that incorporates conditional equivalence testing (CET), a two-stage testing scheme in which standard NHST is followed conditionally by testing for equivalence. The idea of CET is carefully considered as it has the potential to address recent concerns about reproducibility and the limited publication of null results. In this paper we detail the implementation of CET, investigate similarities with a Bayesian testing scheme, and outline the basis for how a scientific journal could proceed to reduce publication bias while remaining relevant., 37 pages, 9 figures

Baseline sleep characteristics were explored for 71 U.S. military service members with mild traumatic brain injury (mTBI) enrolled in a post-concussive syndrome clinical trial. The Pittsburgh Sleep Quality Index (PSQI), sleep diary, several disorder-specific questionnaires, actigraphy and polysomnographic nap were collected. Almost all (97%) reported ongoing sleep problems. The mean global PSQI score was 13.5 (SD=3.8) and 87% met insomnia criteria. Sleep maintenance efficiency was 79.1% for PSQI, 82.7% for sleep diary and 90.5% for actigraphy; total sleep time was 288, 302 and 400 minutes, respectively. There was no correlation between actigraphy and subjective questionnaires. Overall, 70% met hypersomnia conditions, 70% were at high risk for obstructive sleep apnea (OSA), 32% were symptomatic for restless legs syndrome, and 6% reported cataplexy. Nearly half (44%) reported coexisting insomnia, hypersomnia and high OSA risk. Participants with post-traumatic stress disorder (PTSD) had higher PSQI scores and increased OSA risk. Older participants and those with higher aggression, anxiety or depression also had increased OSA risk. The results confirm poor sleep quality in mTBI with insomnia, hypersomnia, and OSA risk higher than previously reported, and imply sleep disorders in mTBI may be underdiagnosed or exacerbated by comorbid PTSD.

Objectives Regular measurement of glycated hemoglobin (A1C) is logistically demanding. Home blotter-paper collection offers an alternative. This study tested the viability of at-home blotter-paper A1C measurement. Methods Objective: compare accuracy of A1C levels collected on blotter paper at home (home-blotter) and blotter-paper collection in laboratory (lab-blotter) with venous A1C (routine measurement). Agreement was assessed by Pearson correlation, Lin concordance correlation coefficient (CCC), positive and negative predictive values (PPVs, NPVs) and Bland-Altman plots and associated statistics. Results Home-blotter, lab-blotter and venous A1C correlated strongly (0.93, 0.93). Home- and lab-blotter results were upwardly biased (0.387%, 0.1%). Bias increased with time. Bias correction provided agreement for both blotters (CCC >0.9); blotters correctly identifying levels above 7% (53 mmol/mol) were 100% for corrected home-blotters and 87% (95% confidence interval) for corrected lab-blotters. NPVs (% blotters correctly identifying levels of 7% or lower [53 mmol/mol]) were 100% for corrected home-blotters and 83% for corrected lab-blotters. After correction, >92% of corrected blotters had errors of 8% or less. Of our subjects, 88.5% found home sampling preferable to routine laboratory sampling. Conclusions Home-blotter collection is an alternative to routine collection.

Stormwater catch basins form part of artificial drainage systems in urban areas and can provide larval habitat for mosquito vector species of West Nile virus (WNv), such as Culex pipiens Linnaeus (Diptera: Culicidae). We evaluated the impact of management techniques and targeted applications of larvicide on larval populations of this potential WNv mosquito vector species in catch basins from the Lower Mainland of Vancouver and on Vancouver Island of British Columbia, Canada. A mixed effects logistic regression model described the relationship between larval pre- sence and larvicide treatment while controlling for other parameters. Parameter estimates showed that larvicide treatment reduced the odds of larvae presence by a factor of ,7.23. The model also revealed relationships between larval presence and water temperature and adjacent land use but larvicide treatment consistently reduced the presence of larvae regardless of these other factors. This knowledge can now be used to prioritise and target control efforts to most efficiently reduce WNv mosquito vector populations, and most effectively reduce the risk of WNv transmission to humans. A similar research strategy could be applied to emerging threats from other potential mosquito vectors of disease around the world, to help lower the incidence of mosquito-borne disease. Resume´—Les reseaux artificiels de drainage dans les zones urbaines comprennent des bassins de captage des eaux pluviales qui peuvent servir d'habitats pour les larves de moustiques vecteurs du virus du Nil occidental (WNv), tels que Culex pipiens Linnaeus (Diptera: Culicidae). Nous evaluons les impacts des techniques d'amenagement et des e cibles de larvicide sur les populations de larves de cette espece, un moustique vecteur potentiel du WNv, dans des bassins de captage dans les basses terres de la region de Vancouver et sur l'ile de Vancouver en Colombie-Britannique, Canada. Un modele de regression logistique aeffets mixtes permet de decrire la relation entre la presence de larves et le traitement au larvicide, tout en tenant compte des autres variables. Les estimations des variables montrent que le traitement au larvicide reduit la probabilited e presence de larves par un facteur de l'ordre de 7,23. Le modele montre aussi une relation entre la presence de larves, d'une part, et la temperature de l'eau et l'utilisation des terres adjacentes, d'autre part; cependant, le traitement au larvicide reduit toujours la presence des larves, quels que soient les autres facteurs. Ces informations peuvent servir aetablir des priorites et fixer des cibles dans les programmes de lutte pour reduire le plus efficacement possible les populations de moustiques vecteurs du WNv et diminuer ainsi le risque de transmission du WNv aux humains. On pourrait utiliser une strategie de recherche similaire pour e les nouvelles menaces que representent d'autres moustiques vecteurs potentiels de maladies sur la planete afin de diminuer l'incidence des maladies transmises par les moustiques.

In this study, we explore the potential of a commercially available mechanical mosquito control device, the Liberty Plus Mosquito Magnet (hereafter referred to as Mosquito Magnet), to reduce the abundance of adult nuisance mosquito populations in public recreational areas. Mosquitoes were trapped on 2 replicate sites close to a campground at Brae Island Regional Park near Fort Langley, British Columbia, Canada. Each site comprised a treatment (Mosquito Magnets used) and control subsection (Mosquito Magnets not used). Mosquito numbers were assessed before and after the treatment period in both subsections at each site with Centers for Disease Control and Prevention (CDC) black light traps. Although nearly 200,000 mosquitoes from 14 different species were collected over 366 trap-nights from May 31 to July 31, 2008, the majority of those identified were Aedes sticticus (68%) and Ae. vexans (22%)-2 of the most notorious nuisance mosquito species in British Columbia. The number of mosquitoes captured by CDC black light traps increased overall during the study period due to natural seasonal variation. Nevertheless, a significant treatment effect (P = 0.0389) was associated with an average decrease of about 32% in the average number of adult mosquitoes collected per day. These results strongly suggest that Mosquito Magnets can reduce the abundance of nuisance mosquitoes, potentially reducing the biting pressure on the public, and providing another tool in mosquito control operations.

The image of Alexander Herzen in the English-speaking world has been shaped by such scholars as Isaiah Berlin, E.H. Carr, and Aileen Kelly. This discourse of English “Herzenism” – in which Herzen figures as a post-war liberal in nineteenth-century clothing or, alternately, as a romantic idealist eccentric – inspired playwright Tom Stoppard to write the plays that form his Coast of Utopia trilogy (2002), which feature Herzen as their central character. Thomas Campbell's analysis of the trilogy's middle play, Shipwreck, in which Herzen's “family drama” is restaged by Stoppard, reveals how his reading of English Herzen scholars informs the play, which wittily reinforces the “message” of the English Herzen about the dangers of philosophical idealism and historicism. Herzen the exile thus serves as a double for Stoppard, who (like Berlin) found a new identity after escaping from a regime supposedly generated by historicism run amok. As Campbell argues, the play also shows how the “other shore” never reached by Herzen and his comrades – the post-ideological, neoliberal utopia achieved by present-day transatlantic society – is forced by its contradictions and intellectual poverty to rehearse the ideological conflicts that gave birth to it, and to examine them for traces of a better order.

Objective To assess the effect of patient use of an online patient portal on diabetes outcomes. Methods Patients included were those with diabetes who were newly referred to a Vancouver-based tertiary care diabetologist between April 2008 and October 2012. Each patient was assessed by the diabetologist, received initial diabetes education and was referred, as necessary, for further education and self-management training. All patients who provided an e-mail address at registration were invited to open an online patient portal account. The portal provided access to diabetes education material, personal laboratory values and a messaging system allowing communication with the diabetologist and staff. Patients who logged in 1 or more times were defined as portal users (n=50); patients who never logged in to the portal were defined as non-users (n=107). A1C was measured at 2 time points: at baseline (i.e. initial, in-clinic visit) and at last follow up (visit no less than 6 months and no more than 2 years after the initial visit). Because usership is self-selected, propensity score matching was used to create comparable user/non-user groups based on available baseline covariates. Results Compared to non-users, a higher proportion of users achieved A1C ≤7% at follow up (56% vs. 32%) (p=0.031). Conclusion Accessing an online patient portal is associated with improved glycemic control.

For testing the efficacy of a treatment in a clinical trial with survival data, the Cox proportional hazards (PH) model is the well-accepted, conventional tool. When using this model, one typically proceeds by confirming that the required PH assumption holds true. If the PH assumption fails to hold, there are many options available, proposed as alternatives to the Cox PH model. An important question which arises is whether the potential bias introduced by this sequential model fitting procedure merits concern and, if so, what are effective mechanisms for correction. We investigate by means of simulation study and draw attention to the considerable drawbacks, with regard to power, of a simple resampling technique, the permutation adjustment, a natural recourse for addressing such challenges. We also consider a recently proposed two-stage testing strategy (2008) for ameliorating these effects. Copyright © 2013 John Wiley & Sons, Ltd.

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.