Browsing by Author "Masconi, Katya L."

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    Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa
    (Public Library of Science, 2019-02-07) Masconi, Katya L.; Matsha, Tandi E.; Erasmus, Rajiv T.; Kengne, Andre P.
    Background: Prediction model updating methods are aimed at improving the prediction performance of a model in a new setting. This study sought to critically assess the impact of updating techniques when applying existent prevalent diabetes prediction models to a population different to the one in which they were developed, evaluating the performance in the mixed-ancestry population of South Africa. Methods: The study sample consisted of 1256 mixed-ancestry individuals from the Cape Town Bellville-South cohort, of which 173 were excluded due to previously diagnosed diabetes and 162 individuals had undiagnosed diabetes. The primary outcome, undiagnosed diabetes, was based on an oral glucose tolerance test. Model updating techniques and prediction models were identified via recent systematic reviews. Model performance was assessed using the C-statistic and expected/observed (E/O) events rates ratio. Results: Intercept adjustment and logistic calibration improved calibration across all five models (Cambridge, Kuwaiti, Omani, Rotterdam and Simplified Finnish diabetes risk models). This was improved further by model revision, where likelihood ratio tests showed that the effect of body mass index, waist circumference and family history of diabetes required additional adjustment (Omani, Rotterdam and Finnish models). However, discrimination was poor following internal validation of these models. Re-estimation of the regression coefficients did not increase performance, while the addition of new variables resulted in the highest discriminatory and calibration performance combination for the models it was undertaken in. Conclusions: While the discriminatory performance of the original existent models during external validation were higher, calibration was poor. The highest performing models, based on discrimination and calibration, were the Omani diabetes model following model revision, and the Cambridge diabetes risk model following the addition of waist circumference as a predictor. However, while more extensive methods incorporating development population information were superior over simpler methods, the increase in model performance was not great enough for recommendation.
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    Effects of different missing data imputation techniques on the performance of undiagnosed diabetes risk prediction models in a mixed-ancestry population of South Africa
    (Public Library of Science, 2016) Masconi, Katya L.; Matsha, Tandi E.; Erasmus, Rajiv T.; Kengne, Andre P.
    Background: Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation. Methods: Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models’ discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment. Results: The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4%) had missing data. Family history had the highest proportion of missing data (25%). Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals). Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods. Conclusions: Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation.