Application of Probabilistic Multiple-Bias Analyses to a Cohort- and a Case-Control Study on the Association between <i>Pandemrix</i>^™and Narcolepsy

Abstract

<div><p>Background</p><p>An increase in narcolepsy cases was observed in Finland and Sweden towards the end of the 2009 H1N1 influenza pandemic. Preliminary observational studies suggested a temporal link with the pandemic influenza vaccine <i>Pandemrix</i>™, leading to a number of additional studies across Europe. Given the public health urgency, these studies used readily available retrospective data from various sources. The potential for bias in such settings was generally acknowledged. Although generally advocated by key opinion leaders and international health authorities, no systematic quantitative assessment of the potential joint impact of biases was undertaken in any of these studies.</p><p>Methods</p><p>We applied bias-level multiple-bias analyses to two of the published narcolepsy studies: a pediatric cohort study from Finland and a case-control study from France. In particular, we developed Monte Carlo simulation models to evaluate a potential cascade of biases, including confounding by age, by indication and by natural H1N1 infection, selection bias, disease- and exposure misclassification. All bias parameters were evidence-based to the extent possible.</p><p>Results</p><p>Given the assumptions used for confounding, selection bias and misclassification, the Finnish rate ratio of 13.78 (95% CI: 5.72–28.11) reduced to a median value of 6.06 (2.5^th- 97.5^th percentile: 2.49–15.1) and the French odds ratio of 5.43 (95% CI: 2.6–10.08) to 1.85 (2.5^th—97.5^th percentile: 0.85–4.08).</p><p>Conclusion</p><p>We illustrate multiple-bias analyses using two studies on the <i>Pandemrix</i>^™-narcolepsy association and advocate their use to better understand the robustness of study findings. Based on our multiple-bias models, the observed <i>Pandemrix</i>^™-narcolepsy association consistently persists in the Finnish study. For the French study, the results of our multiple-bias models were inconclusive.</p></div