Anastasios Apsemidis

Decisions based upon pairwise comparisons of multiple treatments are naturally performed in terms of the mean survival of the selected study arms or functions thereof. However, synthesis of treatment comparisons is usually performed on surrogates of the mean survival, such as hazard ratios or restricted mean survival times. Thus, network meta-analysis techniques may suffer from the limitations of these approaches, such as incorrect proportional hazards assumption or short-term follow-up periods. We propose a Bayesian framework for the network meta-analysis of the main outcome informing the decision, the mean survival of a treatment. Its derivation involves extrapolation of the observed survival curves. We use methods for stable extrapolation that integrate long term evidence based upon mortality projections. Extrapolations are performed using flexible poly-hazard parametric models and M-spline-based methods. We assess the computational and statistical efficiency of different techniques using a simulation study and apply the developed methods to two real data sets. The proposed method is formulated within a decision theoretic framework for cost-effectiveness analyses, where the `best' treatment is to be selected and incorporating the associated cost information is straightforward.

Wastewater data can be very useful for epidemic control during a disease outbreak and proper synthesis of different sources of information can be integrated towards an alerting system, that can be used for decision support. Wastewater data are considered to be of high quality, since they do not depend on testing and can take into account asymptomatic cases. However, little effort has been given into utilizing such information in statistical process control procedures, usually aimed at industrial problems. In this article, we demonstrate how wastewater data can be utilized in health surveillance and propose a statistical framework that can act as a decision support tool. Specifically, we analyze SARS-CoV-2 wastewater data from Austria, constructing summary variables to implicitly describe the Covid-19 prevalence and, based on them, we assess the effectiveness of the current sampling strategy of Austria. We propose a framework of a statistical process monitoring system to aid epidemic management procedures in case SARS-CoV-2 concentration gets dangerously high.