Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates

Lääketieteen parissa perinteiset kyselytutkimukset ovat yhä suosittuja, jonka myötä myös järjestysasteikollisten muuttujien analyysia suoritetaan paljon. Modernin teknologian kehittyminen näkyy kuitenkin myös tällä tieteensaralla, kun mittaustekniikoiden kehittyessä funktionaalisen datan määrä on ka...

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Main Author: Markkanen, Merri-Lotta
Other Authors: Matemaattis-luonnontieteellinen tiedekunta, Faculty of Sciences, Matematiikan ja tilastotieteen laitos, Department of Mathematics and Statistics, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:eng
Published: 2023
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/88490
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author Markkanen, Merri-Lotta
author2 Matemaattis-luonnontieteellinen tiedekunta Faculty of Sciences Matematiikan ja tilastotieteen laitos Department of Mathematics and Statistics Jyväskylän yliopisto University of Jyväskylä
author_facet Markkanen, Merri-Lotta Matemaattis-luonnontieteellinen tiedekunta Faculty of Sciences Matematiikan ja tilastotieteen laitos Department of Mathematics and Statistics Jyväskylän yliopisto University of Jyväskylä Markkanen, Merri-Lotta Matemaattis-luonnontieteellinen tiedekunta Faculty of Sciences Matematiikan ja tilastotieteen laitos Department of Mathematics and Statistics Jyväskylän yliopisto University of Jyväskylä
author_sort Markkanen, Merri-Lotta
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description Lääketieteen parissa perinteiset kyselytutkimukset ovat yhä suosittuja, jonka myötä myös järjestysasteikollisten muuttujien analyysia suoritetaan paljon. Modernin teknologian kehittyminen näkyy kuitenkin myös tällä tieteensaralla, kun mittaustekniikoiden kehittyessä funktionaalisen datan määrä on kasvanut. Tämän myötä järjestysasteikollisten muuttujienkin analyysissa yhä useammin hyödynnetään funktionaalista dataa, mm. järjestysasteikollisen muuttujan mallintamisessa. Tässä pro gradu-tutkielmassa halutaan vertailla kolmea erilaista menetelmää järjestysasteikollisen muuttujan mallintamiseksi funktionaalisten muuttujien avulla. Aineistona käytetään Tampereen yliopistollisen sairaalan hemodynamiikan tutkimusryhmältä saatua aineistoa, josta halutaan mallintaa henkilön itse määrittelemä terveydentila tutkimuksessa mitattujen hemodynaamisten ja funktionaalisten muuttujien avulla. Sovitettaviksi malleiksi ollaan valittu kumulatiivinen logistinen regressiomalli verrannollisuusoletuksella, osittainen kumulatiivinen logistinen regressiomalli verrannollisuusoletuksella ja funktionaalinen ordinaalinen logistinen regressiomalli. Kahden ensimmäisen mallin kohdalla kovariaattien funktionaalisuus sivuutetaan suorittamalla funktionaalisille muuttujille pääkomponenttianalyysi. Mallien sovitus ja analyysien toteutus tehdään R-ohjelmalla. Saatujen tulosten perusteella parhaiten aineistoon sopii osittainen kumulatiivinen logistinen regressiomalli johtuen siitä, että sen sisältämät oletukset eivät ole yhtä tiukat kuin kahdella muulla mallilla. Malleista huonoiten aineistoon tulosten perusteella istuu funktionaalinen ordinaalinen logistinen regressio, joka on malleista tuorein ja näyttää vaativan vielä myös kehitystyötä, esim. kovariaattien valinnan suhteen. Surveys, as well as ordinal variable analysis, are commonly used in the medical field. The development of modern technology has also resulted in the development of measurement techniques and the rise of functional data in medicine. It means that functional data is becoming more often used in the analysis of ordinal variables, such as modeling ordinal variables with functional covariates. In this pro gradu -thesis, three strategies for modeling ordinal variables with functional variables are compared. This is carried out by fitting the models to data obtained from Tampere University Hospital's haemodynamic research group and modeling people's self-assessed health state using functional and haemodynamic variables. Compared models will be he proportional odds model, partial proportional odds model, and functional ordinal logistic regression model. With the first two models, principal component analysis is applied to haemodynamic variables, and their functionality is ignored. The R program is used for model fitting and analysis. Based on the results, the partial proportional odds model appears to be best fit for the data, because it does not have as strict assumptions as other models. Worst fit seems to be functional ordinal logistic regression model, which is newer model than others and it seems that it needs more developing, for example in the case of choosing of covariates.
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spellingShingle Markkanen, Merri-Lotta Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates proportional odds model partial proportional odds model principal component analysis functional ordinal logistic regression model Tilastotiede Statistics 4043 regressioanalyysi tilastomenetelmät tilastotiede tilastolliset mallit regression analysis statistical methods statistics (discipline) statistical models
title Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
title_full Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
title_fullStr Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
title_full_unstemmed Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
title_short Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
title_sort comparison of three ordinal logistic regression methods for predicting person s self assessed health status with functional haemodynamic covariates
title_txtP Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates
topic proportional odds model partial proportional odds model principal component analysis functional ordinal logistic regression model Tilastotiede Statistics 4043 regressioanalyysi tilastomenetelmät tilastotiede tilastolliset mallit regression analysis statistical methods statistics (discipline) statistical models
topic_facet 4043 Statistics Tilastotiede functional ordinal logistic regression model partial proportional odds model principal component analysis proportional odds model regressioanalyysi regression analysis statistical methods statistical models statistics (discipline) tilastolliset mallit tilastomenetelmät tilastotiede
url https://jyx.jyu.fi/handle/123456789/88490 http://www.urn.fi/URN:NBN:fi:jyu-202308034605
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