4 research outputs found
A structural Time Series Model with Markov Switching.
We propose an innovations form of the structural model underlying exponential smoothing that is further augmented by a latent Markov switching process. A particular case of the new model is the local level model with a switching drift, where the switching component describes the change between high and low growth rate periods. This new model is used to analyse the US business cycle using US Quarterly real GNP data. Model parameters are estimated using a Gibbs sampling algorithm and subsequently used for forecasting purposes. In addition, the stability of the new model is tested against Hamilton's model over a range of observation periods.Structural models, Markov switching regime, Gibbs sampling Business cycle.
Efficacy and Immune Mechanisms of Cetuximab for the Treatment of Metastatic Colorectal Cancer
Cetuximab is a chimeric immunoglobulin G1 mono-clonal antibody that targets the ligand-binding domain of the epidermal growth factor receptor and inhibits downstream intra- cellular signals. Research has shown that etuximab can stimulate the autoimmune system and produce antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity reactions, which can recruit cytotoxic lymphocytes to attack and kill cancer cells. Cetuximab is mainly indicated for patients with epidermal growth factor receptor-positive metastatic colorectal cancer who fail to respond to both irinotecan- and oxaliplatin-based regimens. The efficacy and safety of cetuximab as monotherapy or in combination with other treatment options were evaluated in a series of phase II and phase III trials. Identifying the clinical and molecular markers that can predict which patient groups may best benefit from cetuximab treatment is key to improving patient outcomes and avoiding unnecessary toxicities and costs. Herein, we discuss the mechanisms of action by which cetuximab exerts its antitumor effects, as well as the possible clinical and molecular markers that may help predict therapeutic benefits for patients with metastatic colorectal cancer