The COVID-19 pandemic has led to excess mortality worldwide. Notably, the reported numbers of excess deaths are different from the numbers of deaths from COVID-19. Evaluating pandemic-related mortality should therefore not only be based on cause-of-death data but also on external life tables to enable calculation of population-based measures of the difference between observed and expected...
Regression models for the hazard function have been proposed on both a multiplicative and an additive scale. In medical research the former is often suitable, but in some instances, it is more biologically plausible to assume an additive effect on the mortality rate. The best-known example is in population-based cancer patient survival, where the presence of cancer is assumed to have an...
In many medical applications of event-history analysis, individuals may experience several intermediate events before death, and a non-negligible proportion of deaths is unrelated to the disease under study. While standard multi-state models evaluate the occurrence of different events over time, they do not explicitly model mortality from disease-related causes and from other (population)...
Relative survival techniques are often used to assess excess mortality in a specific study population by splitting observed mortality into background and excess components. These methods have been widely used to estimate cancer-specific mortality without the need for precise cause-of-death data for cancer patients. However, applying these techniques to other settings, such as pandemics or...
