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  • Patients treated for a specific disease are monitored over time and several markers are measured (blood pressure, viral load, ...), i.e., longitudinal profiles (evolution of markers over time) are obtained. Based on the evolution in time of such markers a prediction rule might be envisaged to predict the patient's future clinical response (e.g., completely cured/partially cured/no improvement) - categorical outcome. Existing prediction approaches are based on a two-stage procedure where in the first stage linear mixed model where one assumes a normal distribution of markers is estimated and in the second stage prediction is performed using the estimated model for markers. The main objective of the project is to improve upon existing prediction approaches in several directions: (1) use a non-linear model instead of the linear model for the evolution of markers over time, (2) relax classicaly used normality assumption which is not always relevant, (3) avoid a two-stage procedure and develop a joint model for the evolution of markers over time and a categorical outcome. (en)
  • Sdružené statistické modely pro vývoj specifických (např. klinických) ukazatelů v čase (longitudinální profily) a kategoriální odezvy (např. odezva na terapii). Zvláště zobecnění některých klasicky používaných předpokladů (linearita, normální rozdělení). Vývoj souvisejícího software spolu s teorií. (cs)
Title
  • Multivariate longitudinal models to predict a categorical outcome (en)
  • Vícerozměrné longitudinální modely k predikci kategoriální odezvy (cs)
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  • zobecněný lineární smíšený model; učící data; normální směs; ordinální odezva; opakovaná pozorování (en)
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