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We consider modelling and testing for `interaction' between a continuous covariate X and a categorical covariate C in a regression model. Here C represents two treatment arms in a parallel-group clinical trial and X is a prognostic factor which may influence response to treatment. Usually X is...
Persistent link: https://www.econbiz.de/10005103071
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
Regression models play a central role in epidemiology and clinical studies. In epidemiology the emphasis is typically either on determining whether a given risk factor affects the outcome of interest (adjusted for confounders), or on estimating a dose/response curve for a given factor, again...
Persistent link: https://www.econbiz.de/10005074223
Most survival data are analyzed by using the Cox proportional hazards model (in Stata: the stcox command). Almost by definition, a proportion of the observations will be right-censored. Analysis of covariate effects in the Cox model is couched in terms of (log) hazard ratios, and the...
Persistent link: https://www.econbiz.de/10005074243
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
There has been a considerable growth of interest among Stata users and more widely in the practical use of multiple imputation as a principled route to the analysis of datasets with missing covariate values. Sophisticated Stata software (ice) is available for creating multiply imputed datasets....
Persistent link: https://www.econbiz.de/10005102769
The existing Stata command bstrap takes a user-defined program and calculates normal approximation, percentile and bias- corrected percentile bootstrap confidence intervals. However, these intervals are not the most accurate available. In this article, we describe a new command, bci, which...
Persistent link: https://www.econbiz.de/10005103067
The publication of Royston (2004)'s Stata implementation of the MICE method for multiple imputation of missing values has stimulated much interest, comment and further development of the software. In this talk I will describe enhancements of what used to be called mvis.ado and is now known as...
Persistent link: https://www.econbiz.de/10005053362