Browsing by Author "Pepler, Pieter Theo"

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    The identification and application of common principal components
    (Stellenbosch : Stellenbosch University, 2014-12) Pepler, Pieter Theo; Uys, Daniel W.; Nel, D. G.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH ABSTRACT: When estimating the covariance matrices of two or more populations, the covariance matrices are often assumed to be either equal or completely unrelated. The common principal components (CPC) model provides an alternative which is situated between these two extreme assumptions: The assumption is made that the population covariance matrices share the same set of eigenvectors, but have di erent sets of eigenvalues. An important question in the application of the CPC model is to determine whether it is appropriate for the data under consideration. Flury (1988) proposed two methods, based on likelihood estimation, to address this question. However, the assumption of multivariate normality is untenable for many real data sets, making the application of these parametric methods questionable. A number of non-parametric methods, based on bootstrap replications of eigenvectors, is proposed to select an appropriate common eigenvector model for two population covariance matrices. Using simulation experiments, it is shown that the proposed selection methods outperform the existing parametric selection methods. If appropriate, the CPC model can provide covariance matrix estimators that are less biased than when assuming equality of the covariance matrices, and of which the elements have smaller standard errors than the elements of the ordinary unbiased covariance matrix estimators. A regularised covariance matrix estimator under the CPC model is proposed, and Monte Carlo simulation results show that it provides more accurate estimates of the population covariance matrices than the competing covariance matrix estimators. Covariance matrix estimation forms an integral part of many multivariate statistical methods. Applications of the CPC model in discriminant analysis, biplots and regression analysis are investigated. It is shown that, in cases where the CPC model is appropriate, CPC discriminant analysis provides signi cantly smaller misclassi cation error rates than both ordinary quadratic discriminant analysis and linear discriminant analysis. A framework for the comparison of di erent types of biplots for data with distinct groups is developed, and CPC biplots constructed from common eigenvectors are compared to other types of principal component biplots using this framework. A subset of data from the Vermont Oxford Network (VON), of infants admitted to participating neonatal intensive care units in South Africa and Namibia during 2009, is analysed using the CPC model. It is shown that the proposed non-parametric methodology o ers an improvement over the known parametric methods in the analysis of this data set which originated from a non-normally distributed multivariate population. CPC regression is compared to principal component regression and partial least squares regression in the tting of models to predict neonatal mortality and length of stay for infants in the VON data set. The tted regression models, using readily available day-of-admission data, can be used by medical sta and hospital administrators to counsel parents and improve the allocation of medical care resources. Predicted values from these models can also be used in benchmarking exercises to assess the performance of neonatal intensive care units in the Southern African context, as part of larger quality improvement programmes.
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    An observational audit of pain scores post-orthopaedic surgery at a level two state hospital in Cape Town
    (MedPharm Publications, 2014-05) Hauser, Neil David; Dyer, Robert; Pepler, Pieter Theo; Rolfe, Deborah A.
    Objectives: The aim was to determine whether postoperative pain is satisfactorily controlled in patients undergoing orthopaedic surgery at a level two state hospital in Cape Town. Design: Two observational audits were performed 12 months apart as part of a full audit cycle. Setting and subjects: In view of perceived poor postoperative pain control, an audit was performed of acute postoperative pain scores, anaesthesia techniques, and patient satisfaction with pain control. Orthopaedic patients undergoing surgical procedures at a level two state hospital in Cape Town were enrolled in the two audits. Patient groups included both patients admitted to the hospital and day-cases. Outcome measures: Patients admitted to hospital following major surgery, rated their perceived pain over 48 hours, using a visual analogue scale (VAS). Day-case patients scored their pain in hospital, and were then contacted telephonically after 24 hours, and if required, after 48 hours. A VAS score ≥ 4 was regarded as unacceptable. The interventions employed after the first audit were: pain rounds, staff education and training, increased postoperative epidural time, patient-controlled analgesia pumps and indwelling femoral catheters following total knee replacement. Results: Data were analysed from 71 patients in each audit. Mean VAS scores were unacceptable 12 and 24 hours after major surgery (range 4 - 5.1 in audit 1). Following the introduction of the aforementioned interventions, the mean pain scores were < 4 at every time point measurement, and significantly lower than in audit 1 at most assessment times (p < 0.05). Patient satisfaction with pain control improved from 32.4% in audit 1 to 54.9% in audit 2. Conclusion: Acute postoperative pain is an important clinical problem in orthopaedic surgery. Following the demonstration of unacceptable postoperative pain scores in the first audit, specific interventions were shown to significantly improve pain control in the follow-up audit.
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    Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units : a Southern African retrospective study
    (Faculty of Medicine, Makerere University, 2012-06) Pepler, Pieter Theo; Uys, Daniel Wilhelm; Nel, Daniel Gerhardus
    Objectives: To predict neonatal mortality and length of stay (LOS) from readily available perinatal data for neonatal intensive care unit (NICU) admissions in Southern African private hospitals. Methods: Retrospective observational study using perinatal data from a large multicentre sample. Fifteen participating NICU centres in the Medi-Clinic private hospital group in Southern Africa. We used 2376 infants born between 1 January - 31 December 2008 to build the regression models, and a further 1 578 infants born between 1 January - 31 December 2007 to test the models. Outcome measures were mortality and length of hospital stay for NICU admissions. Results: Of the infants included in the 2008 dataset, ninety-one (3.8%) died after being admitted to NICU centres. The median LOS for non-transferred survivors was 11 days. An analysis of the structural peculiarities of the data showed high correlations between groups of the perinatal variables pertaining to the size and Apgar scores of the newborn infants, respectively. The logistic regression model to predict neonatal mortality had a good fit (AUC: 0.8507, misclassification rate: 13.6%), but the low positive predictive value of this model reduces its usefulness. The poisson log-linear model to predict LOS had a good fit (predicted R2: 0.7027). Conclusions: Apgar score at one minute, birth weight, and delivery mode significantly influence the odds of neonatal death and are associated with significant effects on LOS.