Semiparametric methods for multiple exposure mismeasurement and a bivariate outcome in HIV vaccine trials

Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and prone to missingness and mismeasurement. We discuss study designs that collect detailed exposure information from only a small subset of participants while collecting crude exposure information from all participants and treat estimation of vaccine efficacy in the missing data/measurement error framework. We extend the discordant partner design for HIV vaccine trials of Golm, Halloran, and Longini (1998, Statistics in Medicine, 17, 2335-2352.) to the more complex augmented trial design of Longini, Datta, and Halloran (1996, Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 13, 440-447) and Datta, Halloran, and Longini (1998, Statistics in Medicine 17, 185-200). The model for this design includes three exposure covariates and both univariate and bivariate outcomes. We adapt recently developed semiparametric missing data methods of Reilly and Pepe (1995, Biometrika 82, 299 314), Carroll and Wand (1991, Journal of the Royal Statistical Society, Series B 53, 573-585), and Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the augmented vaccine trial design. We demonstrate with simulated HIV vaccine trial data the improvements in bias and efficiency when combining the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility and infectiousness. We show that the semiparametric methods estimate both efficacy parameters without bias when the good exposure information is either missing completely at random or missing at random. The pseudolikelihood method of Carroll and Wand (1991) and Pepe and Fleming (1991) was the more efficient of the two semiparametric methods.     

Estimating the Influenza Vaccine Effectiveness against Medically Attended Influenza in Clinical Settings: A Hospital-Based Case-Control Study with a Rapid Diagnostic Test in Japan

Background

Influenza vaccine effectiveness (VE) studies are usually conducted by specialized agencies and require time and resources. The objective of this study was to estimate the influenza VE against medically attended influenza using a test-negative case-control design with rapid influenza diagnostic tests (RIDT) in a clinical setting.

Methods

A prospective study was conducted at a community hospital in Nagasaki, western Japan during the 2010/11 influenza season. All outpatients aged 15 years and older with influenza-like illnesses (ILI) who had undergone RIDT were enrolled. A test-negative case-control design was applied to estimate the VEs: the cases were ILI patients with positive RIDT results and the controls were ILI patients with negative RIDT results. Information on patient characteristics, including vaccination histories, was collected using questionnaires and medical records.

Results

Between December 2010 and April 2011, 526 ILI patients were tested with RIDT, and 476 were eligible for the analysis. The overall VE estimate against medically attended influenza was 47.6%, after adjusting for the patients'' age groups, presence of chronic conditions, month of visit, and smoking and alcohol use. The seasonal influenza vaccine reduced the risk of medically attended influenza by 60.9% for patients less than 50 years of age, but a significant reduction was not observed for patients 50 years of age and older. A sensitivity analysis provided similar figures.

Conclusion

The test-negative case-control study using RIDT provided moderate influenza VE consistent with other reports. Utilizing the commonly used RIDT to estimate VE provides rapid assessment of VE; however, it may require validation with more specific endpoint.     

Estimates of pandemic influenza vaccine effectiveness in Europe, 2009-2010: results of Influenza Monitoring Vaccine Effectiveness in Europe (I-MOVE) multicentre case-control study

Background

A multicentre case-control study based on sentinel practitioner surveillance networks from seven European countries was undertaken to estimate the effectiveness of 2009–2010 pandemic and seasonal influenza vaccines against medically attended influenza-like illness (ILI) laboratory-confirmed as pandemic influenza A (H1N1) (pH1N1).

Methods and Findings

Sentinel practitioners swabbed ILI patients using systematic sampling. We included in the study patients meeting the European ILI case definition with onset of symptoms >14 days after the start of national pandemic vaccination campaigns. We compared pH1N1 cases to influenza laboratory-negative controls. A valid vaccination corresponded to >14 days between receiving a dose of vaccine and symptom onset. We estimated pooled vaccine effectiveness (VE) as 1 minus the odds ratio with the study site as a fixed effect. Using logistic regression, we adjusted VE for potential confounding factors (age group, sex, month of onset, chronic diseases and related hospitalizations, smoking history, seasonal influenza vaccinations, practitioner visits in previous year). We conducted a complete case analysis excluding individuals with missing values and a multiple multivariate imputation to estimate missing values. The multivariate imputation (n = 2902) adjusted pandemic VE (PIVE) estimates were 71.9% (95% confidence interval [CI] 45.6–85.5) overall; 78.4% (95% CI 54.4–89.8) in patients <65 years; and 72.9% (95% CI 39.8–87.8) in individuals without chronic disease. The complete case (n = 1,502) adjusted PIVE were 66.0% (95% CI 23.9–84.8), 71.3% (95% CI 29.1–88.4), and 70.2% (95% CI 19.4–89.0), respectively. The adjusted PIVE was 66.0% (95% CI −69.9 to 93.2) if vaccinated 8–14 days before ILI onset. The adjusted 2009–2010 seasonal influenza VE was 9.9% (95% CI −65.2 to 50.9).

Conclusions

Our results suggest good protection of the pandemic monovalent vaccine against medically attended pH1N1 and no effect of the 2009–2010 seasonal influenza vaccine. However, the late availability of the pandemic vaccine and subsequent limited coverage with this vaccine hampered our ability to study vaccine benefits during the outbreak period. Future studies should include estimation of the effectiveness of the new trivalent vaccine in the upcoming 2010–2011 season, when vaccination will occur before the influenza season starts. Please see later in the article for the Editors'' Summary     

Influenza Vaccine Effectiveness in the Tropics: Moderate Protection in a Case Test-Negative Analysis of a Hospital-Based Surveillance Population in Bangkok between August 2009 and January 2013

Influenza in the tropics occurs year round with peaks that correspond variably to temperate regions. However, data on influenza vaccine effectiveness (VE) in the tropics is sparse. We report on the effectiveness of influenza vaccine to prevent medically attended laboratory confirmed influenza from sentinel surveillance conducted at a Thai military medical facility in Bangkok, Thailand from August 2009 to January 2013. Patients ≥6 months old presenting with influenza-like illness underwent combined nasal/throat swabs which were tested by influenza RT-PCR. A case test-negative study design was used to evaluate VE. Of 2999 samples available for analysis,1059 (35.3%) were PCR-positive (cases) and 1940 (64.6%) were PCR-negative (test-negative controls). Five hundred and seven (16.9%) of these patients reported being vaccinated within the previous 12 months. Periods of high and low influenza activity were defined based on publicly available Thai Ministry of Public Health data. Overall VE adjusted for age and epiweek was found to be 50.1% (95%CI: 35.0, 61.9%). The May to April adjusted VE for year 2010, 2011 and 2012 was 57.7% (95%CI: 33.7, 73.8%), 57.1% (95% CI: 35.2, 68.3%) and 37.6% (95% CI: 3.5, 62.9%).During high influenza activity in years with the same vaccine formulation, the adjusted VE was 54.9% (95%CI: 38.9, 66.9%). VE appeared to be much higher during high versus low influenza activity periods. The adjusted point estimate for VE was highest in the 18–49 year age group (76.6%) followed by 6–23 months (58.1%) and 2–17 years (52.5%). Adjusted estimates were not done for those ≥50 years of age due to small numbers. VE in patients with underlying disease was 75.5% compared to 48.0% in those without. Our findings demonstrate moderate protection by influenza vaccination and support the utility of influenza vaccination in the tropics including in very young children and those with underlying disease.     

I-MOVE multi-centre case control study 2010-11: overall and stratified estimates of influenza vaccine effectiveness in Europe

Background

In the third season of I-MOVE (Influenza Monitoring Vaccine Effectiveness in Europe), we undertook a multicentre case-control study based on sentinel practitioner surveillance networks in eight European Union (EU) member states to estimate 2010/11 influenza vaccine effectiveness (VE) against medically-attended influenza-like illness (ILI) laboratory-confirmed as influenza.

Methods

Using systematic sampling, practitioners swabbed ILI/ARI patients within seven days of symptom onset. We compared influenza-positive to influenza laboratory-negative patients among those meeting the EU ILI case definition. A valid vaccination corresponded to > 14 days between receiving a dose of vaccine and symptom onset. We used multiple imputation with chained equations to estimate missing values. Using logistic regression with study as fixed effect we calculated influenza VE adjusting for potential confounders. We estimated influenza VE overall, by influenza type, age group and among the target group for vaccination.

Results

We included 2019 cases and 2391 controls in the analysis. Adjusted VE was 52% (95% CI 30-67) overall (N = 4410), 55% (95% CI 29-72) against A(H1N1) and 50% (95% CI 14-71) against influenza B. Adjusted VE against all influenza subtypes was 66% (95% CI 15-86), 41% (95% CI -3-66) and 60% (95% CI 17-81) among those aged 0-14, 15-59 and ≥60 respectively. Among target groups for vaccination (N = 1004), VE was 56% (95% CI 34-71) overall, 59% (95% CI 32-75) against A(H1N1) and 63% (95% CI 31-81) against influenza B.

Conclusions

Results suggest moderate protection from 2010-11 trivalent influenza vaccines against medically-attended ILI laboratory-confirmed as influenza across Europe. Adjusted and stratified influenza VE estimates are possible with the large sample size of this multi-centre case-control. I-MOVE shows how a network can provide precise summary VE measures across Europe.     

Effectiveness of Trivalent Inactivated Influenza Vaccine in Children Estimated by a Test-Negative Case-Control Design Study Based on Influenza Rapid Diagnostic Test Results

We assessed vaccine effectiveness (VE) against medically attended, laboratory-confirmed influenza in children 6 months to 15 years of age in 22 hospitals in Japan during the 2013–14 season. Our study was conducted according to a test-negative case-control design based on influenza rapid diagnostic test (IRDT) results. Outpatients who came to our clinics with a fever of 38°C or over and had undergone an IRDT were enrolled in this study. Patients with positive IRDT results were recorded as cases, and patients with negative results were recorded as controls. Between November 2013 and March 2014, a total of 4727 pediatric patients (6 months to 15 years of age) were enrolled: 876 were positive for influenza A, 66 for A(H1N1)pdm09 and in the other 810 the subtype was unknown; 1405 were positive for influenza B; and 2445 were negative for influenza. Overall VE was 46% (95% confidence interval [CI], 39–52). Adjusted VE against influenza A, influenza A(H1N1)pdm09, and influenza B was 63% (95% CI, 56–69), 77% (95% CI, 59–87), and 26% (95% CI, 14–36), respectively. Influenza vaccine was not effective against either influenza A or influenza B in infants 6 to 11 months of age. Two doses of influenza vaccine provided better protection against influenza A infection than a single dose did. VE against hospitalization influenza A infection was 76%. Influenza vaccine was effective against influenza A, especially against influenza A(H1N1)pdm09, but was much less effective against influenza B.     

A computational framework for influenza antigenic cartography

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.     

Assessment of the variability in influenza A(H1N1) vaccine effectiveness estimates dependent on outcome and methodological approach

BACKGROUND: Estimation of Influenza vaccine effectiveness (VE) varies with study design, clinical outcome considered and statistical methodology used. By estimating VE using differing outcomes and statistical methods on the same cohort of individuals the variability in the estimates produced can be better understood. The Pandemic Influenza Primary Care Reporting (PIPeR) cohort of approximately 193,000 individuals was used to estimate pandemic VE in Scotland during season 2009-10. VE results for three outcomes; influenza related consultations, virological confirmed influenza and death were considered. Use of individualised records allowed all models to be adjusted for age, sex, deprivation, risk status relating to chronic illnesses, seasonal vaccination status and a marker of the individual's propensity to consult. For the consultation and death outcomes, VE was calculated by comparing consultation rates in the unvaccinated and vaccinated groups, adjusted for the listed factors, using both Cox and Poisson regression models. For the consultation outcome, the unvaccinated group was split into individuals before vaccination and those never vaccinated to allow for potential differences in the health seeking behaviour of these groups. For the virology outcome estimates were calculated using a generalised additive logistic regression model. All models were adjusted for time. Vaccine effect was demonstrated for the influenza-like illness consultation outcome using the Cox model (VE=49% 95% CI (19%, 67%)) with lower estimates from the model splitting the before and never vaccinated groups (VE=34.2% with 95% CI (-0.5%, 58.9%)). Vaccine effect was also illustrated for overall mortality (VE=40% (95% CI 18%, 56%)) and a virological confirmed subset of symptomatic individuals (VE=60% (95% CI -38%, 89%)). CONCLUSIONS: This study illustrates positive point estimates of Influenza VE across methodology and outcome for a single cohort of individuals during season 2009-10. Understanding of potential differences between approaches aids interpretation of VE results in future seasons.     

Influenza Epidemiology and Vaccine Effectiveness among Patients with Influenza-Like Illness,Viral Watch Sentinel Sites,South Africa, 2005–2009

Background

There is limited data on the epidemiology of influenza and few published estimates of influenza vaccine effectiveness (VE) from Africa. In April 2009, a new influenza virus strain infecting humans was identified and rapidly spread globally. We compared the characteristics of patients ill with influenza A(H1N1)pdm09 virus to those ill with seasonal influenza and estimated influenza vaccine effectiveness during five influenza seasons (2005–2009) in South Africa.

Methods

Epidemiological data and throat and/or nasal swabs were collected from patients with influenza-like illness (ILI) at sentinel sites. Samples were tested for seasonal influenza viruses using culture, haemagglutination inhibition tests and/or polymerase chain reaction (PCR) and for influenza A(H1N1)pdm09 by real-time PCR. For the vaccine effectiveness (VE) analysis we considered patients testing positive for influenza A and/or B as cases and those testing negative for influenza as controls. Age-adjusted VE was calculated as 1-odds ratio for influenza in vaccinated and non-vaccinated individuals.

Results

From 2005 through 2009 we identified 3,717 influenza case-patients. The median age was significantly lower among patients infected with influenza A(H1N1)pdm09 virus than those with seasonal influenza, 17 and 27 years respectively (p<0.001). The vaccine coverage during the influenza season ranged from 3.4% in 2009 to 5.1% in 2006 and was higher in the ≥50 years (range 6.9% in 2008 to 13.2% in 2006) than in the <50 years age group (range 2.2% in 2007 to 3.7% in 2006). The age-adjusted VE estimates for seasonal influenza were 48.6% (4.9%, 73.2%); −14.2% (−9.7%, 34.8%); 12.0% (−70.4%, 55.4%); 67.4% (12.4%, 90.3%) and 29.6% (−21.5%, 60.1%) from 2005 to 2009 respectively. For the A(H1N1)pdm09 season, the efficacy of seasonal vaccine was −6.4% (−93.5%, 43.3%).

Conclusion

Influenza vaccine demonstrated a significant protective effect in two of the five years evaluated. Low vaccine coverage may have reduced power to estimate vaccine effectiveness.     

An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout

We explore a Bayesian approach to selection of variables that represent fixed and random effects in modeling of longitudinal binary outcomes with missing data caused by dropouts. We show via analytic results for a simple example that nonignorable missing data lead to biased parameter estimates. This bias results in selection of wrong effects asymptotically, which we can confirm via simulations for more complex settings. By jointly modeling the longitudinal binary data with the dropout process that possibly leads to nonignorable missing data, we are able to correct the bias in estimation and selection. Mixture priors with a point mass at zero are used to facilitate variable selection. We illustrate the proposed approach using a clinical trial for acute ischemic stroke.     

Surveillance and Vaccine Effectiveness of an Influenza Epidemic Predominated by Vaccine-Mismatched Influenza B/Yamagata-Lineage Viruses in Taiwan, 2011?12 Season

Introduction

The 2011−12 trivalent influenza vaccine contains a strain of influenza B/Victoria-lineage viruses. Despite free provision of influenza vaccine among target populations, an epidemic predominated by influenza B/Yamagata-lineage viruses occurred during the 2011−12 season in Taiwan. We characterized this vaccine-mismatched epidemic and estimated influenza vaccine effectiveness (VE).

Methods

Influenza activity was monitored through sentinel viral surveillance, emergency department (ED) and outpatient influenza-like illness (ILI) syndromic surveillance, and case-based surveillance of influenza with complications and deaths. VE against laboratory-confirmed influenza was evaluated through a case-control study on ILI patients enrolled into sentinel viral surveillance. Logistic regression was used to estimate VE adjusted for confounding factors.

Results

During July 2011−June 2012, influenza B accounted for 2,382 (72.5%) of 3,285 influenza-positive respiratory specimens. Of 329 influenza B viral isolates with antigen characterization, 287 (87.2%) were B/Yamagata-lineage viruses. Proportions of ED and outpatient visits being ILI-related increased from November 2011 to January 2012. Of 1,704 confirmed cases of influenza with complications, including 154 (9.0%) deaths, influenza B accounted for 1,034 (60.7%) of the confirmed cases and 103 (66.9%) of the deaths. Reporting rates of confirmed influenza with complications and deaths were 73.5 and 6.6 per 1,000,000, respectively, highest among those aged ≥65 years, 50−64 years, 3−6 years, and 0−2 years. Adjusted VE was −31% (95% CI: −80, 4) against all influenza, 54% (95% CI: 3, 78) against influenza A, and −66% (95% CI: −132, −18) against influenza B.

Conclusions

This influenza epidemic in Taiwan was predominated by B/Yamagata-lineage viruses unprotected by the 2011−12 trivalent vaccine. The morbidity and mortality of this vaccine-mismatched epidemic warrants careful consideration of introducing a quadrivalent influenza vaccine that includes strains of both B lineages.     

Calculating sample size for studies with expected all-or-none nonadherence and selection bias

Summary .  We develop sample size formulas for studies aiming to test mean differences between a treatment and control group when all-or-none nonadherence (noncompliance) and selection bias are expected. Recent work by Fay, Halloran, and Follmann (2007, Biometrics 63, 465–474) addressed the increased variances within groups defined by treatment assignment when nonadherence occurs, compared to the scenario of full adherence, under the assumption of no selection bias. In this article, we extend the authors' approach to allow selection bias in the form of systematic differences in means and variances among latent adherence subgroups. We illustrate the approach by performing sample size calculations to plan clinical trials with and without pilot adherence data. Sample size formulas and tests for normally distributed outcomes are also developed in a Web Appendix that account for uncertainty of estimates from external or internal pilot data.     

Effectiveness of A(H1N1)pdm09 Influenza Vaccine in Adults Recommended for Annual Influenza Vaccination

Introduction

Because of variability in published A(H1N1)pdm09 influenza vaccine effectiveness estimates, we conducted a study in the adults belonging to the risk groups to assess the A(H1N1)pdm09 MF59-adjuvanted influenza vaccine effectiveness.

Methods

VE against influenza and/or pneumonia was assessed in the cohort study (n>25000), and vaccine effectiveness against laboratory-confirmed A(H1N1)pdm09 influenza was assessed in a matched case-control study (16 pairs). Odds ratios (OR) and their 95% confidence intervals (95% CI) were calculated by using multivariate logistic regression; vaccine effectiveness was estimated as (1-odds ratio)*100%.

Results

Vaccine effectiveness against laboratory-confirmed A(H1N1)pdm09 influenza and influenza and/or pneumonia was 98% (84–100%) and 33% (2–54%) respectively. The vaccine did not prevent influenza and/or pneumonia in 18–59 years old subjects, and was 49% (16–69%) effective in 60 years and older subjects.

Conclusions

Even though we cannot entirely rule out that selection bias, residual confounding and/or cross-protection has played a role, the present results indicate that the MF59-adjuvanted A(H1N1)pdm09 influenza vaccine has been effective in preventing laboratory-confirmed A(H1N1)pdm09 influenza and influenza and/or pneumonia, the latter notably in 60 years and older subjects.     

Detailed Report on 2014/15 Influenza Virus Characteristics,and Estimates on Influenza Virus Vaccine Effectiveness from Austria’s Sentinel Physician Surveillance Network

Background

Influenza vaccine effectiveness (VE) is influenced by the antigenic similarity between vaccine- and circulating strains.

Material and Methods

This paper presents data obtained by the Austrian sentinel surveillance system on the evolution of influenza viruses during the season 2014/15 and its impact on influenza vaccine effectiveness in primary care in Austria as estimated by a test-negative case control design. VE estimates were performed for each influenza virus type/subtype, stratified by underlying diseases and adjusted for age, sex and calendar week of infection.

Results

Detailed genetic and antigenic analyses showed that circulating A(H3N2) viruses were genetically distinct from the 2014/15 A(H3N2) vaccine component indicating a profound vaccine mismatch. The Influenza A(H1N1)pdm09 viruses were antigenically conserved and matched the respective vaccine component. Influenza B viruses were lineage-matched B/Yamagata viruses with a clade-level variation. Consistent with substantial vaccine mismatch for the A(H3N2) viruses a crude overall VE of only 47% was estimated, whereas the VE estimates for A(H1N1)pdm09 were 84% and for influenza B viruses 70%. Increased VE estimates were obtained after stratification by underlying diseases and adjustment for the covariates sex and age, whereby the adjustment for the calendar week of infection was the covariate exerting the highest influence on adjusted VE estimates.

Conclusion

In summary, VE data obtained in this study underscore the importance to perform VE estimates in the context of detailed characterization of the contributing viruses and also demonstrate that the calendar week of influenza virus infection is the most important confounder of VE estimates.     

GEE with Gaussian estimation of the correlations when data are incomplete

This paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parameters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression parameters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.     

Effectiveness of MF59? Adjuvanted Influenza A(H1N1)pdm09 Vaccine in Risk Groups in the Netherlands

Background

The aim of the present study was to estimate the effectiveness of the MF59™-adjuvanted influenza A(H1N1)pdm09 vaccine against medically attended influenza-like illness and RT-PCR confirmed influenza in the at-risk population and persons over 60 in the Netherlands.

Methods

We conducted a retrospective cohort study in a Dutch based GP medical record database between 30 November 2009 and 1 March 2010 to estimate the vaccine effectiveness against influenza-like illness. Within the cohort we nested a test negative case-control study to estimate the effectiveness against laboratory confirmed influenza.

Results

The crude effectiveness in preventing diagnosed or possible influenza-like illness was 17.3% (95%CI: −8.5%–36.9%). Of the measured covariates, age, the severity of disease and health seeking behaviour through devised proxies confounded the association between vaccination and influenza-like illness. The adjusted vaccine effectiveness was 20.8% (95%CI: −5.4%, 40.5%) and varied significantly by age, being highest in adults up to 50 years (59%, 95%CI: 23%, 78%), and non-detectable in adults over 50 years. The number of cases in the nested case control study was too limited to validly estimate the VE against confirmed influenza.

Conclusions

With our study we demonstrated that the approach of combining a cohort study in a primary health care database with field sampling is a feasible and useful option to monitor VE of influenza vaccines in the future.     

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event data are partially observed

Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal-validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing-risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions.     

Field effectiveness of pandemic and 2009-2010 seasonal vaccines against 2009-2010 A(H1N1) influenza: estimations from surveillance data in France

Background

In this study, we assess how effective pandemic and trivalent 2009-2010 seasonal vaccines were in preventing influenza-like illness (ILI) during the 2009 A(H1N1) pandemic in France. We also compare vaccine effectiveness against ILI versus laboratory-confirmed pandemic A(H1N1) influenza, and assess the possible bias caused by using non-specific endpoints and observational data.

Methodology and Principal Findings

We estimated vaccine effectiveness by using the following formula: VE  =  (PPV-PCV)/(PPV(1-PCV)) × 100%, where PPV is the proportion vaccinated in the population and PCV the proportion of vaccinated influenza cases. People were considered vaccinated three weeks after receiving a dose of vaccine. ILI and pandemic A(H1N1) laboratory-confirmed cases were obtained from two surveillance networks of general practitioners. During the epidemic, 99.7% of influenza isolates were pandemic A(H1N1). Pandemic and seasonal vaccine uptakes in the population were obtained from the National Health Insurance database and by telephonic surveys, respectively. Effectiveness estimates were adjusted by age and week. The presence of residual biases was explored by calculating vaccine effectiveness after the influenza period. The effectiveness of pandemic vaccines in preventing ILI was 52% (95% confidence interval: 30–69) during the pandemic and 33% (4–55) after. It was 86% (56–98) against confirmed influenza. The effectiveness of seasonal vaccines against ILI was 61% (56–66) during the pandemic and 19% (−10–41) after. It was 60% (41–74) against confirmed influenza.

Conclusions

The effectiveness of pandemic vaccines in preventing confirmed pandemic A(H1N1) influenza on the field was high, consistently with published findings. It was significantly lower against ILI. This is unsurprising since not all ILI cases are caused by influenza. Trivalent 2009-2010 seasonal vaccines had a statistically significant effectiveness in preventing ILI and confirmed pandemic influenza, but were not better in preventing confirmed pandemic influenza than in preventing ILI. This lack of difference might be indicative of selection bias.     

2010—2012年度季节性流行性感冒疫苗对6～59月龄儿童保护效果的病例对照研究

目的评价2010—2012年度季节性流行性感冒疫苗(流感疫苗)对儿童的上市后保护效果。方法选择2010—2012年度6~59月龄的实验室诊断流感病例为病例组,在儿童预防接种信息系统中随机选择健康儿童为对照组,进行1∶2匹配的病例对照研究,采用条件Logistic回归计算保护效果。结果研究中共纳入1 255组研究对象。2010—2011和2011—2012年度,流感疫苗对6~59月龄儿童完全免疫的保护效果分别为73.2%(95%可信限(CI):52.2~85.0)和52.9%(95%CI:42.1~61.7),对6~35月龄儿童的保护效果优于36~59月龄儿童,完全免疫的保护效果优于部分免疫。流感疫苗的保护效果从1~3月的68.9%(95%CI:57.5~77.2)衰减至4~6月的48.4%(95%CI,33.8~59.7)。结论流感疫苗对6~59月龄儿童具有一定的保护效果,儿童接种疫苗后可以减少52.9%~73.2%的流感病例,建议儿童每年及时进行全程免疫。     

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.