![]() RCTs may substantially underestimate vaccine impact on the population level, because vaccinating infants with PCV protects not only the vaccinated but also unvaccinated children and adults against invasive pneumococcal diseases (IPD) and pneumonia 14– 16.Īddressing the limitation of RCTs in vaccine impact estimation means finding a suitable unvaccinated comparison population, which is difficult, if not impossible, to identify. The efficacy measured in RCTs is different from the actual vaccine impact on a population level – that is, the reduction of disease burden in a population consisting of vaccinated and unvaccinated individuals in comparison with an otherwise similar but universally unvaccinated population 13 – after PCV introduction. invasive diseases and, to a lesser extent, pneumonia 12) by comparing vaccinated groups to unvaccinated groups. Randomized controlled trials (RCTs) demonstrated the efficacy of PCVs against diseases (e.g. Unlike previous anti-pneumococcal vaccines that merely reduced the risk of disease 9, PCVs also protect against carriage of vaccine serotypes and can therefore contribute to herd immunity 10, 11. Following the widespread adoption of a 7-valent PCV (PCV7) into national childhood immunization programs, PCVs of higher valency – PCV10 and PCV13 – have been introduced 5, 6 while the third generation PCVs with even higher valency – PCV15 and PCV20 – are recently licenced 7, 8. In 2019, the GBD study also identified lower respiratory infections including pneumonia to be the leading contributor to disability-adjusted life years (DALY) among children and the elderly globally 3.Īnti-pneumococcal vaccines were developed to combat pneumococcal infections and the most widely used ones are pneumococcal conjugate vaccines (PCVs), in which several types of the pneumococcus’ capsular polysaccharide are conjugated to carrier proteins to elicit immunity against a subset among around 100 serotypes of pneumococcus 4. The Global Burden of Disease (GBD) study found that pneumococcal pneumonia was the most common cause of lower respiratory infection morbidity and mortality worldwide, causing 1 200 000 deaths in 2016 2. Although it typically colonizes the human nasopharynx asymptomatically, it can disseminate to cause a diverse array of diseases that ranges from mild (such as sinusitis and otitis media) to more severe infections (such as pneumonia) and invasive diseases (such as meningitis and septicemia) 1. The bacterium Streptococcus pneumoniae (the pneumococcus) poses a substantial health burden globally. The LASSO method is accurate, easily implementable, and can be applied to study the impact of PCVs and of other vaccines. We then applied LASSO to real-world data and found that it yielded estimates of vaccine impact similar to SC. We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between outcome and all control variables was non-causal. We first used a simulation study to test the performance of LASSO regression and established methods including the synthetic control (SC) approach. Here we present a new approach to estimate PCVs’ impact – using LASSO regression to predict the counterfactual outcome for vaccine impact inference. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint – like all-cause pneumonia – is non-specific. In the process, it also carries out selection of the feature groups.The pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia. In that case, pcLasso shrinks each group-wiseĬomponent of the solution toward the leading principal components of that ![]() If the features are pre-assigned to groups (such as cell-pathways, assays or "principal components lasso" ("pcLasso"). Principal components of the feature matrix. ![]() ![]() Quadratic penalty that shrinks the coefficient vector toward the leading The method combines the lasso ($\ell_1$) sparsity penalty with a Kenneth Tay and 1 other authors Download PDF Abstract: We propose a new method for supervised learning, especially suited to wideĭata where the number of features is much greater than the number of Download a PDF of the paper titled Principal component-guided sparse regression, by J.
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