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Abstract: Background: Progression of a cancer disease and dying without progression can be understood as competing risks. The Cause-Specific Hazards Model and the Fine and Gray model on cumulative incidences are common statistical models to handle this problem. The pseudo value approach by Andersen and Klein is also able to cope with competing risks. It is still unclear which model suits best in which situation. Methods:For a simulated dataset and a real data example of ovarian cancer patients who are exposed to progression and death the three models are examined. We compare the three models with regards to interpretation and modeling requirements. Results:In this study,the parameter estimates for the competing risks are similar from the Cause-Specific Hazards Model and the Fine and Gray model. The pseudo value approach yields divergent results which are heavily dependent on modeling details. Conclusions:The investigated approaches do not exclude each other but moreover complement one another. The pseudo value approach is an alternative that circumvents proportionality assumptions. As in all survival analyses, situations with low event rates should be interpreted carefully. Keywords: Multistate Models, pseudo values, cause-specific hazards, cumulative incidence, Fine and Gray model.Download Full Article |
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Abstract: Both maternal and paternal disease history can be important predictors of the risk of common conditions such as heart disease or cancer because of shared environmental and genetic risk factors. Sometimes maternal and paternal history can have remarkably different effects on offspring’s status. The results are often affected by how the maternal and paternal disease histories are quantified. We proposed using the log-rank score (LRS) to investigate the separate effect of maternal and paternal history on diseases, which takes parental disease status and theage of theirdisease onset into account. Through simulation studies, we compared the performance of the maternal and paternal LRS with simple binary indicators under two different mechanisms of unbalanced parental effects. We applied the LRS to a national cohort study to further segregate family risks for heart diseases. We demonstrated using the LRS rather than binary indicators can improve the prediction of disease risks and better discriminate the paternal and maternal histories. In the real study, we found that the risk forstroke is closely related with maternal history but not with paternal history and that maternal and paternal disease history have similar impacton the onset of myocardial infarction. Keywords: Family history, stroke, risk score, maternal effect, imprinting.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: Generalized linear models based on Poisson regression are commonly applied to count data for area morbidity outcomes, focused on modelling the conditional mean of the response as a function of a set of risk factors. Mean regression models may be sensitive to outliers and provide no information on other distributional features of the response. We consider instead a Poisson lognormal hierarchical approach to quantile regression of spatially configured count data, allowing for observed risk factors and spatially correlated unobserved risk factors. This technique has the advantage that a profile of the relative outcome risk across quantiles can be obtained, including estimates of uncertainty (e.g. the uncertainty attaching to 2.5% or 5% relative risk quantiles). An application involves counts of emergency hospitalisations for self-harm for 6791 small areas in England. Known risk factors are area deprivation, a measure of social fragmentation and a measure of rural status. It is shown that impact of these predictors varies between quantiles, and that hierarchical quantile regression generally produces narrower risk intervals, except for outlier areas, and leads to a higher number of areas being classed as high risk. Keywords: Hierarchical quantile regression. Relative risk. Risk intervals. Elevated risk. Self-harm. /td> |
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Abstract: Reliability of structures has been discussed by several authors using probability models. Some of the early researches have been discussed by Birnbaum (1956) [1] in which two independent variables X and Y are defined as “Strength” and “Stress” respectively. This research is an extension of Mann- Whitney paper (1947) [2] on P(Y<X). Beg (1979c, 1980b, 1980c) [3-5] estimated reliability i.e R = P(Y<X), by taking two parameter Pareto and Power function distributions. Gupta and Gupta (1990) [6] have found point estimates of R=P(aX> bY) by Maximum likelihood and MVUE of R. In the present paper we have considered R=P(Y<X) where X and Y independently follow Lindley distribution. The MLE and Moment estimators of the distribution and then that of R have been found. A simulation study has been done to estimate biasedness and Confidence interval of R. Keywords: Reliability R = P(Y<X), Lindley distribution, Maximum Likelihood Estimator, Moment Estimator. Download Full Article |