• Autostrata: Improved Automatic Stratification for Coarsened Exact Matching 

      Arnes, Jo Inge; Hapfelmeier, Alexander; Horsch, Alexander (Chapter; Bokkapittel, 2022-08-22)
      We commonly adjust for confounding factors in analytical observational epidemiologyto reduce biases that distort the results. Stratification and matching are standard methods for reducing confounder bias. Coarsened exact matching (CEM) is a recent method using stratification to coarsen variables into categorical variables to enable exact matching of exposed and nonexposed ...
    • The Beauty of Complex Designs 

      Arnes, Jo Inge; Bongo, Lars Ailo (Chapter; Bokkapittel, 2020-12-08)
      The increasing use of omics data in epidemiology enables many novel study designs, but also introduces challenges for data analysis. We describe the possibilities for systems epidemiological designs in the Norwegian Women and Cancer (NOWAC) study and show how the complexity of NOWAC enables many beautiful new study designs. We discuss the challenges of implementing designs and analyzing data. Finally, ...
    • Cloudless Friend-to-Friend Middleware for Smartphones 

      Arnes, Jo Inge; Karlsen, Randi (Peer reviewed; Chapter; Bokkapittel, 2019-11-13)
      Using smartphones for peer-to-peer communication over the Internet is difficult without the aid of centralized services. These centralized services, which usually reside in the cloud, are necessary for brokering communication between peers, and all communication must pass through them. A reason for this is that smartphones lack publicly reachable IP addresses. Also, because people carry their ...