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Since its introduction to a wondering public in 1972, the Cox proportional hazards regression model has become an overwhelmingly popular tool in the analysis of censored survival data. However, some features of the Cox model may cause problems for the analyst or an interpreter of the data. They...
Persistent link: https://www.econbiz.de/10009442279
Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to...
Persistent link: https://www.econbiz.de/10009468835
Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to...
Persistent link: https://www.econbiz.de/10009485310
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Royston (2014, Stata Journal 14: 738–755) explained how a popular application of the Cox proportional hazards model "is to develop a multivariable prediction model, often a prognostic model to predict the future clinical outcome of patients with a particular disorder from 'baseline' factors...
Persistent link: https://www.econbiz.de/10011265698
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Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in...
Persistent link: https://www.econbiz.de/10010547850
In an era in which doctors and patients aspire to personalized medicine, detecting and modeling interactions between covariates or between covariates and treatment is becoming increasingly important. In observational studies, for example, in epidemiology, interactions are known as effect...
Persistent link: https://www.econbiz.de/10010551113
Michael Mitchell’s Data Management Using Stata comprehensively covers data-management tasks, from those a beginning statistician would need to those hard-to-verbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.
Persistent link: https://www.econbiz.de/10009274502