Search for dissertations about: "Repeated measures"
Showing result 1 - 5 of 161 swedish dissertations containing the words Repeated measures.
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1. Recollection of Repeated Events : Difficulties and Possibilities
Abstract : Survey based research about self-reported incidents and legal investigations concerning sexual abuse, terrorism, and refugee status determination often involves reporting about self-experienced events that are similar to each other and has occurred repeatedly. Such repeated events tend to be recalled in a general manner and as a cluster of events. READ MORE
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2. Small Area Estimation for Multivariate Repeated Measures Data
Abstract : This thesis considers Small Area Estimation with a main focus on estimation and prediction theory for repeated measures data. The demand for small area statistics is for both cross-sectional and repeated measures data. READ MORE
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3. Searching for causal effects of road traffic safety interventions : applications of the interrupted time series design
Abstract : Traffic-related injuries represent a global public health problem, and contribute largely to mortality and years lived with disability worldwide. Over the course of the last decades, improvements to road traffic safety and injury surveillance systems have resulted in a shift in focus from the prevention of motor vehicle accidents to the control of injury events involving vulnerable road users (VRUs), such as cyclists and moped riders. READ MORE
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4. Nonlinear Quantile Regression for Longitudinal Data
Abstract : The overall objective of the two papers in this thesis is to examine the properties of the weighted nonlinear quantile regression estimator for the analysis of longitudinal data. To this end, the question of which weights to be used, the bias of the estimator and the possibility to calculate confidence intervals has to be examined. READ MORE
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5. Pharmacometric Investigations of Prediction Precision and Advances of Models for Composite Scale Data
Abstract : Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. READ MORE