Exposure misclassification bias in the estimation of vaccine effectiveness

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naive estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.Peer reviewe

Methods for generating and evaluating synthetic longitudinal patient data: a systematic review

The proliferation of data in recent years has led to the advancement and utilization of various statistical and deep learning techniques, thus expediting research and development activities. However, not all industries have benefited equally from the surge in data availability, partly due to legal restrictions on data usage and privacy regulations, such as in medicine. To address this issue, various statistical disclosure and privacy-preserving methods have been proposed, including the use of synthetic data generation. Synthetic data are generated based on some existing data, with the aim of replicating them as closely as possible and acting as a proxy for real sensitive data. This paper presents a systematic review of methods for generating and evaluating synthetic longitudinal patient data, a prevalent data type in medicine. The review adheres to the PRISMA guidelines and covers literature from five databases until the end of 2022. The paper describes 17 methods, ranging from traditional simulation techniques to modern deep learning methods. The collected information includes, but is not limited to, method type, source code availability, and approaches used to assess resemblance, utility, and privacy. Furthermore, the paper discusses practical guidelines and key considerations for developing synthetic longitudinal data generation methods

Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes – applications to influenza vaccine effectiveness

Background: Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios. Methods: Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data. Results: The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small. Conclusions: The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.Peer reviewe

Spotlight influenza : Estimation of influenza vaccine effectiveness in elderly people with assessment of residual confounding by negative control outcomes, Finland, 2012/13 to 2019/20

Publisher Copyright: © This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence and indicate if changes were made.Background: Cohort studies on vaccine effectiveness are prone to confounding bias if the distribution of risk factors is unbalanced between vaccinated and unvaccinated study subjects. Aim: We aimed to estimate influenza vaccine effectiveness in the elderly population in Finland by controlling for a sufficient set of confounders based on routinely available register data. Methods: For each of the eight consecutive influenza seasons from 2012/13 through 2019/20, we conducted a cohort study comparing the hazards of laboratory-confirmed influenza in vaccinated and unvaccinated people aged 65-100 years using individual-level medical and demographic data. Vaccine effectiveness was estimated as 1 minus the hazard ratio adjusted for the confounders age, sex, vaccination history, nights hospitalised in the past and presence of underlying chronic conditions. To assess the adequacy of the selected set of confounders, we estimated hazard ratios of off-season hospitalisation for acute respiratory infection as a negative control outcome. Results: Each analysed cohort comprised around 1 million subjects, of whom 37% to 49% were vaccinated. Vaccine effectiveness against laboratory-confirmed influenza ranged from 16% (95% confidence interval (CI): 12-19) to 48% (95% CI: 41-54). More than 80% of the laboratory-confirmed cases were hospitalised. The adjusted off-season hazard ratio estimates varied between 1.00 (95% CI: 0.94-1.05) and 1.08 (95% CI: 1.01-1.15), indicating that residual confounding was absent or negligible. Conclusion: Seasonal influenza vaccination reduces the hazard of severe influenza disease in vaccinated elderly people. Data about age, sex, vaccination history and utilisation of hospital care proved sufficient to control confounding.Peer reviewe

Estimation of marriage incidence rates by combining two cross-sectional retrospective designs : Event history analysis of two dependent processes

Peer reviewe

Effectiveness of 2 Influenza Vaccines in Nationwide Cohorts of Finnish 2-Year-Old Children in the Seasons 2015-2016 Through 2017-2018

Background. From 2015-2016 through 2017-2018, injectable, trivalent inactivated influenza vaccines (IIV3) and a nasal spray, tetravalent live-attenuated influenza vaccine (LAIV4) were used in parallel in Finland. To understand how well vaccination with each vaccine type protected children against influenza under real-life conditions, vaccine effectiveness in 2-year-olds was estimated for all 3 seasons. Methods. Each season, a nationwide register-based cohort study was conducted. The study population comprised 60 088, 60 860, and 60 345 children in 2015-2016, 2016-2017, and 2017-2018, respectively. Laboratory-confirmed influenza was the study outcome. Seasonal influenza vaccination with either LAIV4 or IIV3 was the time-dependent exposure of interest. Vaccine effectiveness was defined as 1 minus the hazard ratio comparing vaccinated with unvaccinated children. Results. From 2015-2016 through 2017-2018, the effectiveness of LAIV4 against influenza of any virus type was estimated at 54.2% (95% confidence interval, 32.2-69.0%), 20.3% (-12.7%, 43.6%), and 30.5% (10.9-45.9%); the corresponding effectiveness of IIV3 was 77.2% (48.9-89.8%), 24.5% (-29.8%, 56.1%), and -20.1% (-61.5%, 10.7%). Neither influenza vaccine clearly excelled in protecting children. The LAIV4 effectiveness against type B was greater than against type A and greater than the IIV3 effectiveness against type B. Conclusions. To understand how influenza vaccines could be improved, vaccine effectiveness must be analyzed by vaccine and virus type. Effectiveness estimates also expressing overall protection levels are needed to guide individual and programmatic decision-making processes. Supported by this analysis, the vaccination program in Finland now recommends LAIV4 and injectable, tetravalent inactivated influenza vaccines replacing IIV3.Peer reviewe

Koronaepidemian mallinnuksen lähtökohdat, tavoitteet ja ongelmat

Terveyden ja hyvinvoinnin laitoksessa (THL) tehtiin keväällä 2020 matemaattisiin malleihin perustuvia skenaarioita SARS-CoV-2-epidemian kulusta olettamalla, että viruksen tartuttavuuslukuun voidaan vaikuttaa eriasteisilla rajaamistoimilla. Lyhyen aikavälin ennusteita viruksen aiheuttaman sairaanhoidon tarpeesta on tehty maaliskuusta lähtien. Esittelemme viruksen tartuttavuusluvun arviointia ja merkitystä epidemian seurannassa sekä kartoitamme immuniteetin roolia epidemian muokkaajana. Tautitaakan ja sairaanhoidon ennusteet perustuvat tartuntojen ilmaantuvuuden mallintamiseen sekä arvioon yksittäiseen tartuntaan liittyvästä vakavan sairastumisen riskistä. Ajantasainen tieto on tärkeää ja matemaattisilla malleilla on monia käyttötarkoituksia. Puutteellinen ymmärrys SARS-CoV-2:n tuottamasta immuniteetista vaikeuttaa toistaiseksi epidemian kulun ennustamista. Mallien tärkein hyöty on auttaa selvittämään tartuntojen, immuniteetin ja epidemian rajaamistoimien yhteistä dynamiikkaa

Spotlight influenza: Estimation of influenza vaccine effectiveness in elderly people with assessment of residual confounding by negative control outcomes, Finland, 2012/13 to 2019/20

BackgroundCohort studies on vaccine effectiveness are prone to confounding bias if the distribution of risk factors is unbalanced between vaccinated and unvaccinated study subjects.AimWe aimed to estimate influenza vaccine effectiveness in the elderly population in Finland by controlling for a sufficient set of confounders based on routinely available register data.MethodsFor each of the eight consecutive influenza seasons from 2012/13 through 2019/20, we conducted a cohort study comparing the hazards of laboratory-confirmed influenza in vaccinated and unvaccinated people aged 65–100 years using individual-level medical and demographic data. Vaccine effectiveness was estimated as 1 minus the hazard ratio adjusted for the confounders age, sex, vaccination history, nights hospitalised in the past and presence of underlying chronic conditions. To assess the adequacy of the selected set of confounders, we estimated hazard ratios of off-season hospitalisation for acute respiratory infection as a negative control outcome.ResultsEach analysed cohort comprised around 1 million subjects, of whom 37% to 49% were vaccinated. Vaccine effectiveness against laboratory-confirmed influenza ranged from 16% (95% confidence interval (CI): 12–19) to 48% (95% CI: 41–54). More than 80% of the laboratory-confirmed cases were hospitalised. The adjusted off-season hazard ratio estimates varied between 1.00 (95% CI: 0.94–1.05) and 1.08 (95% CI: 1.01–1.15), indicating that residual confounding was absent or negligible.ConclusionSeasonal influenza vaccination reduces the hazard of severe influenza disease in vaccinated elderly people. Data about age, sex, vaccination history and utilisation of hospital care proved sufficient to control confounding.</p

Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes-applications to influenza vaccine effectiveness

Background: Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios.Methods: Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data.Results: The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small.Conclusions: The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.</div