A linear mixed model to estimate COVID-19-induced excess mortality

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.     

Calorimetric aspects of the heat activation of spores of Phycomyces blakesleeanus

When Phycomyces spores are heated in a differential scanning calorimeter (DSC) an exothermic process situated in the activation temperature range is detected. This process does not depend on oxygen and is absent in scans of activated spores. After activation the protein denaturation profile is also changed but this effect seems to depend on oxygen. Acetate activates spores in about 10 min, however longer treatment with this chemical is needed to affect the exothermic peak. The effects of some other chemicals that influence the activation mechanism, like D₂O and FeCl₂, are also reported.     

A characterization of missingness at random in a generalized shared‐parameter joint modeling framework for longitudinal and time‐to‐event data,and sensitivity analysis

We consider a conceptual correspondence between the missing data setting, and joint modeling of longitudinal and time‐to‐event outcomes. Based on this, we formulate an extended shared random effects joint model. Based on this, we provide a characterization of missing at random, which is in line with that in the missing data setting. The ideas are illustrated using data from a study on liver cirrhosis, contrasting the new framework with conventional joint models.