Search for dissertations about: "Covariate selection"
Showing result 1 - 5 of 10 swedish dissertations containing the words Covariate selection.
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1. Covariate Model Building in Nonlinear Mixed Effects Models
Abstract : Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. READ MORE
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2. Covariate selection and propensity score specification in causal inference
Abstract : This thesis makes contributions to the statistical research field of causal inference in observational studies. The results obtained are directly applicable in many scientific fields where effects of treatments are investigated and yet controlled experiments are difficult or impossible to implement. READ MORE
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3. Causal inference and case-control studies with applications related to childhood diabetes
Abstract : This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored. READ MORE
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4. Methods for handling model misspecification in pharmacometric model-based approaches for the analysis of longitudinal clinical trial data
Abstract : Pharmacometrics model-based analyses proved to be increasingly useful in drug discovery and development. This usefulness was made evident by the formalisation of the model-integrated drug discovery and development framework (MID3). READ MORE
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5. Valid causal inference in high-dimensional and complex settings
Abstract : The objective of this thesis is to consider some challenges that arise when conducting causal inference based on observational data. High dimensionality can occur when it is necessary to adjust for many covariates, and flexible models must be used to meet convergence assumptions. The latter may require the use of a novel machine learning estimator. READ MORE