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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
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
In an era in which doctors and patients aspire to personalized medicine and more sophisticated risk estimation, detecting and modeling interactions between covariates or between covariates and treatment is increasingly important. In observational studies (for example, in epidemiology),...
Persistent link: https://www.econbiz.de/10010575194
There is increasing interest in the medical world in the possibility of tailoring treatment to the individual patient. Statistically, the relevant task is to identify interactions between covariates and treatments, such that the patient’s value of a given covariate influences how strongly (or...
Persistent link: https://www.econbiz.de/10004982799
Royston and Parmar (2002, Statistics in Medicine 21: 2175 – 2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1:1-28). In this article, we introduce a new command, stpm2, that extends the...
Persistent link: https://www.econbiz.de/10004982802
This tutorial aims to cover many of the aspects of the flexible parametric alternatives to the Cox model that we have been developing.
Persistent link: https://www.econbiz.de/10005041774
We present an update of mim, a program for managing multiply im- puted datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due...
Persistent link: https://www.econbiz.de/10004964302
All doctors treating patients with Breast Cancer know which key variables indicate a good prognosis and which values decrease the chances of surviving. However because of complex interactions between the variables and survival doctors cannot give an individualized prognosis to a patient. The...
Persistent link: https://www.econbiz.de/10005101331
Persistent link: https://www.econbiz.de/10005101342
Cox proportional-hazard regression has been essentially the automatic choice of analysis tool for modeling survival data in medical studies. However, the Cox model has several intrinsic features that may cause problems for the analyst or an interpreter of the data. These include the necessity of...
Persistent link: https://www.econbiz.de/10005102733