Estimating measurement error in blood pressure, using structural equations modelling

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dc.contributor.advisorLombard, Carlen_ZA
dc.contributor.advisorMuller, Chrisen_ZA
dc.contributor.authorKepe, Lulama Patricken_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Statistical and Actuarial Science.en_ZA
dc.date.accessioned2012-08-27T11:35:37Z
dc.date.available2012-08-27T11:35:37Z
dc.date.issued2004
dc.descriptionThesis (MSc)--Stellenbosch University, 2004.en_ZA
dc.description.abstractENGLISH ABSTRACT: Any branch in science experiences measurement error to some extent. This maybe due to conditions under which measurements are taken, which may include the subject, the observer, the measurement instrument, and data collection method. The inexactness (error) can be reduced to some extent through the study design, but at some level further reduction becomes difficult or impractical. It then becomes important to determine or evaluate the magnitude of measurement error and perhaps evaluate its effect on the investigated relationships. All this is particularly true for blood pressure measurement. The gold standard for measunng blood pressure (BP) is a 24-hour ambulatory measurement. However, this technology is not available in Primary Care Clinics in South Africa and a set of three mercury-based BP measurements is the norm for a clinic visit. The quality of the standard combination of the repeated measurements can be improved by modelling the measurement error of each of the diastolic and systolic measurements and determining optimal weights for the combination of measurements, which will give a better estimate of the patient's true BP. The optimal weights can be determined through the method of structural equations modelling (SEM) which allows a richer model than the standard repeated measures ANOVA. They are less restrictive and give more detail than the traditional approaches. Structural equations modelling which is a special case of covariance structure modelling has proven to be useful in social sciences over the years. Their appeal stem from the fact that they includes multiple regression and factor analysis as special cases. Multi-type multi-time (MTMT) models are a specific type of structural equations models that suit the modelling of BP measurements. These designs (MTMT models) constitute a variant of repeated measurement designs and are based on Campbell and Fiske's (1959) suggestion that the quality of methods (time in our case) can be determined by comparing them with other methods in order to reveal both the systematic and random errors. MTMT models also showed superiority over other data analysis methods because of their accommodation of the theory of BP. In particular they proved to be a strong alternative to be considered for the analysis of BP measurement whenever repeated measures are available even when such measures do not constitute equivalent replicates. This thesis focuses on SEM and its application to BP studies conducted in a community survey of Mamre and the Mitchells Plain hypertensive clinic population.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Elke vertakking van die wetenskap is tot 'n minder of meerdere mate onderhewig aan metingsfout. Dit is die gevolg van die omstandighede waaronder metings gemaak word soos die eenheid wat gemeet word, die waarnemer, die meetinstrument en die data versamelingsmetode. Die metingsfout kan verminder word deur die studie ontwerp maar op 'n sekere punt is verdere verbetering in presisie moeilik en onprakties. Dit is dan belangrik om die omvang ven die metingsfout te bepaal en om die effek hiervan op verwantskappe te ondersoek. Hierdie aspekte is veral waar vir die meting van bloeddruk by die mens. Die goue standaard vir die meet van bloeddruk is 'n 24-uur deurlopenee meting. Hierdie tegnologie is egter nie in primêre gesondheidsklinieke in Suid-Afrika beskikbaar nie en 'n stel van drie kwik gebasseerde bloedrukmetings is die norm by 'n kliniek besoek. Die kwaliteit van die standard kombinasie van die herhaalde metings kan verbeter word deur die modellering van die metingsfout van diastoliese en sistoliese bloeddruk metings. Die bepaling van optimale gewigte vir die lineêre kombinasie van die metings lei tot 'n beter skatting van die pasiënt se ware bloedruk. Die gewigte kan berekening word met die metode van strukturele vergelykings modellering (SVM) wat 'n ryker klas van modelle bied as die standaard herhaalde metings analise van variansie modelle. Dié model het minder beperkings en gee dus meer informasie as die tradisionele benaderings. Strukurele vergelykings modellering wat 'n spesial geval van kovariansie strukturele modellering is, is oor die jare nuttig aangewend in die sosiale wetenskap. Die aanhang is die gevolg van die feit dat meervoudige lineêre regressie en faktor analise ook spesiale gevalle van die metode is. Meervoudige-tipe meervoudige-tyd (MTMT) modelle is 'n spesifieke strukturele vergelykings model wat die modellering van bloedruk pas. Hierdie tipe model is 'n variant van die herhaalde metings ontwerp en is gebaseer op Campbell en Fiske (1959) se voorstel dat die kwaliteit van verskillende metodes bepaal kan word deur dit met ander metodes te vergelyk om sodoende sistematiese en stogastiese foute te onderskei. Die MTMT model pas ook goed in by die onderliggende fisiologies aspekte van bloedruk en die meting daarvan. Dit is dus 'n goeie alternatief vir studies waar die herhaalde metings nie ekwivalente replikate is nie. Hierdie tesis fokus op die strukturele vergelykings model en die toepassing daarvan in hipertensie studies uitgevoer in die Mamre gemeenskap en 'n hipertensie kliniek populasie in Mitchells Plain.af_ZA
dc.format.extent148 p.
dc.identifier.urihttp://hdl.handle.net/10019.1/53739
dc.language.isoen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectBlood pressure -- Measurementen_ZA
dc.subjectError analysis (Mathematics)en_ZA
dc.subjectDissertations -- Statisticsen_ZA
dc.titleEstimating measurement error in blood pressure, using structural equations modellingen_ZA
dc.typeThesisen_ZA
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