ijsmr
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Abstract: The widespread use of smartphone applications (apps) provides a promising new platform for medical research and healthcare decision making. Given the need to help guide clinical discussions about the appropriateness of breast cancer screening in the presence of competing risks among older women, we proposed to incorporate the Fine-Gray prediction model, which offers more intuitive clinical interpretation of risk in the presence of competing risks, into a smartphone-based decision aid application. Clinicians can input the woman’s characteristics and medical history, and the app will output prediction estimates of both types of events (i.e. death from breast cancer and competing risk events) given the presence or absence of breast cancer screening. This prototype was built using drag-and-drop visual programming tools provided by the free, cloud-based software “MIT App Inventor for Android.” It will be intended for clinicians to use in the context of patients’ values to decide whether screening is appropriate for an individual. Our analysis indicated that screening was beneficial to survival, and that older women benefited less from screening due to the increasing incidence of non-breast-cancer competing risk deaths as age increased. The algorithm we implemented for the app provides instant probability estimates that help quantify screening benefits as a function of age, and comorbidity burden. Keywords: Smartphone applications, App Inventor, competing risks, breast cancer, screening.Download Full Article |
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Abstract: Background: Natural experiments in former smokers are an important criterion for inferring causality between smoking and tooth loss. We examined how former smoking influenced risk estimate of tooth loss incidence. Methods: Records of dental check-ups of the work cohort were examined. The sample consisted of data from 1,724 workers recorded at the ages of 40 years and 50 years, and this was analyzed for tooth loss incidence during a 10-year period. Former smokers were categorized into two groups based on whether they quit smoking before or during the observational period. Variables used for adjustment were age, sex, oral and overall health behavior, dental visit, and number of existing teeth immediately prior to observation. Results: The prevalence of tooth loss incidence and number of teeth lost during the observational period were both higher in current smokers than in never smokers (33.7% vs. 23.9% and 0.83 vs. 0.42, respectively). Incident odds ratio of tooth loss in long-term quitters relative to never smokers was not significant and less than one (incident odds ratio 0.85, 95% confidence interval 0.56–1.29). Incident odds ratios of short-term quitters and current smokers were both significant, though short-term quitters exhibited higher values (1.72, 1.15–2.55) than current smokers (1.48, 1.10–2.00). Conclusions: The causal interpretation is strengthened by attenuation of the risk in long-term quitters. However, additional factors may influence the risk estimates of former smokers, suggesting potential limitations of a natural experiment for inferring causal association between smoking and tooth loss. Keywords: Natural experiment, Smoking, Tooth loss, Cohort study, Causal inference. Download Full Article |
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Abstract: Background: Poor performance of risk prediction models in a new setting is common. Recalibration methods aim to improve the prediction performance of a model in a validation population, however the extent of its application in the validation of diabetes risk prediction models is not yet known. Methods: We critically reviewed published validation studies of diabetes prediction models, selected from five recent comprehensive systematic reviews and database searches. Common recalibration techniques applied were described and the extent to which recalibration and impacts were reported analysed. Results: Of the 236 validations identified, 22.9% (n = 54) undertook recalibration on existent models in the validation population. The publication of these studies was consistent from 2008. Only incident diabetes risk prediction models were validated, and the most commonly validated Framingham offspring simple clinical risk model was the most recalibrated of the models, in 4 studies (7.4%). Conclusions: This review highlights the lack of attempt by validation studies to improve the performance of the existent models in new settings. Model validation is a fruitless exercise if the model is not recalibrated or updated to allow for greater accuracy. This halts the possible implementation of an existent model into routine clinical care. The use of recalibration procedures should be encouraged in all validation studies, to correct for the anticipated drop in model performance. Keywords: Risk prediction, diabetes, update, recalibration, validation. Download Full Article |
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Abstract: The daily number of hospital admissions due to mosquito-borne diseases can vary greatly. This variability can be explained by different factors such as season of the year, temperature and pollution levels, among others. In this paper, we propose a new class of non-homogeneous Poisson processes which incorporates seasonality factors to more realistically fit data related to rare events, and in particular we show how the modifications applied to the special NHPP intensity function improve the analysis and fit of daily hospital admissions, due to dengue in Ribeirão Preto, São Paulo state, Brazil. Keywords: Hospital admissions, seasonal disease behavior, non-homogeneous Poisson processes, dengue infection, cyclical process.Download Full Article |
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Abstract: In medical studies, the longitudinal data sets obtained from more than one response variables and covariates are mostly analyzed to investigate the change in repeated measurements of each subject at different time points. In this study, the usability of multivariate models in the analysis of these kind of data sets is investigated, because it provides the joint analysis of multiple response variables over time and enables researchers to examine both the correlations of response variables and autocorrelation between measurements from each response variable over time. It has been shown that different parameter estimation methods affect the results in the analysis of multivariate unbalanced longitudinal data. We investigated that autocorrelation structure over time between measurements from same response variable should be truly specified. We also illustrated and compared the simpler, more standard models for fixed effects with multivariate models provided by SAS on a real-life data set in the joint analysis of two response variables. Results show that misspecification of autocorrelation structures has a negative impact on the parameter estimates and parameter estimation method should become of interest. Keywords: Multivariate longitudinal data, mixed models, covariance structures.Download Full Article |