University of Jyväskylä

Dissertation: 20.11. M.Sc. Jaakko Reinikainen (Faculty of Mathematics and Science, Statistics)

Start date: Nov 20, 2015 01:00 PM

End date: Nov 20, 2015 04:00 PM

Location: Mattilanniemi, MaA211

M.Sc. Jaakko Reinikainen defends his doctoral dissertation in Statistics ”Efficient design and modeling strategies for follow-up studies with time-varying covariates”. Opponent Professor Marie Reilly (Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institute, Sweden) and custos Professor Juha Karvanen (University of Jyväskylä). The doctoral dissertation is held in English.

Abstract

Jaakko Reinikainen, Kuvaaja Katja Valtonen
Jaakko Reinikainen, Kuvaaja Katja Valtonen

M.Sc. Jaakko Reinikainen defends his doctoral dissertation in Statistics ”Efficient design and modeling strategies for follow-up studies with time-varying covariates”. Opponent Professor Marie Reilly (Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institute, Sweden) and custos Professor Juha Karvanen (University of Jyväskylä). The doctoral dissertation is held in English.

Epidemiological studies can often be designed in several ways, some of which may be more optimal than others. Possible designs may differ in the required resources or the ability to provide reliable answers to the questions under study. In addition, once the data are collected, the selected modeling approach may affect how efficiently the data are utilized.

The purpose of this dissertation is to investigate efficient designs and analysis methods in follow-up studies with longitudinal measurements. A key question is how to select optimally a subcohort for a new longitudinal covariate measurement if we cannot afford to measure the entire cohort. Another key question we consider is how to determine the reasonable number of longitudinal measurements. Different ways to utilize longitudinal covariate measurements in modeling cardiovascular disease (CVD) mortality are also studied.
This work demonstrates that the cost-efficiency of follow-up designs can be improved by careful planning. The proposed method for selecting optimal subcohorts is shown to outperform simple random sampling and it is demonstrated how the number of longitudinal measurements can be determined using simulated data and data from previous similar studies. The results also indicate that individual-level changes and cumulative averages of classical risk factors are good predictors of CVD mortality.

The dissertation is published in the series University of Jyväskylä, Department of Mathematics and Statistics, Report 153, Jyväskylä 2015, ISSN 1457-8905, ISBN 978-951-39-6315-6. It is available at the University Library’s Publications Unit, +358 (0)40 805 3825, myynti@library.jyu.fi

Further information:

Jaakko Reinikainen, jaakko.o.reinikainen@jyu.fi, puh. 0440366896

Communications Officer Anitta Kananen, tiedotus@jyu.fi, puh. +358 40 805 4142

 

More information

Jaakko Reinikainen
jaakko.o.reinikainen@student.jyu.fi
044 036 6896
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