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| Registros recuperados: 20 | |
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| Carlin, John B.; Galati, John C.; Royston, Patrick. |
| A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data... |
| Tipo: Article |
Palavras-chave: Mim; Mimstack; Ice; Micombine; Miset; Mifit; Multiple imputation; Missing data; Missing at random; Research Methods/ Statistical Methods. |
| Ano: 2008 |
URL: http://purl.umn.edu/120928 |
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| Royston, Patrick. |
| 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 include the restrictive assumption of proportional hazards for covariate effects, and “loss” (non-estimation) of the baseline hazard function induced by conditioning on event times. In medicine, the hazard function is often of fundamental interest since it represents an important aspect of the time course of the disease in question. In the present article, the Stata implementation of a class of flexible parametric survival models recently proposed by Royston and... |
| Tipo: Journal Article |
Palavras-chave: Parametric survival analysis; Hazard function; Proportional hazards; Proportional odds; Research Methods/ Statistical Methods. |
| Ano: 2001 |
URL: http://purl.umn.edu/115931 |
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| Royston, Patrick. |
| Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivariate imputation of missing values under missing-at-random (MAR) assumptions. In a second article, Royston (2005) described ice, an upgrade incorporating various improvements and changes to the software based on personal experience, discussion with colleagues, and user requests. This article describes an update to ice. The changes are less substantial but nevertheless important enough to warrant a brief explanation. The major modification is that the default method of imputing missing values in ice is now by sampling from the posterior predictive distribution rather than by predicted mean matching. The ice system comprises five ado-files: ice, micombine,... |
| Tipo: Journal Article |
Palavras-chave: Ice; Uvis; Multiple imputation; Missing values; Update; Research Methods/ Statistical Methods. |
| Ano: 2005 |
URL: http://purl.umn.edu/117543 |
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| Royston, Patrick. |
| Normal-based confidence intervals for a parameter of interest are inaccurate when the sampling distribution of the estimate is nonnormal. The technique known as profile likelihood can produce confidence intervals with better coverage. It may be used when the model includes only the variable of interest or several other variables in addition. Profile-likelihood confidence intervals are particularly useful in nonlinear models. The command pllf computes and plots the maximum likelihood estimate and profile likelihood–based confidence interval for one parameter in a wide variety of regression models. |
| Tipo: Article |
Palavras-chave: Pllf; Profile likelihood; Confidence interval; Nonnormality; Nonlinear model; Research Methods/ Statistical Methods. |
| Ano: 2007 |
URL: http://purl.umn.edu/119282 |
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| Hosmer, David W.; Royston, Patrick. |
| In this paper, we describe a new Stata command, stlh, which estimates and tests for the significance of the time-varying regression coefficients in Aalen’s linear hazards model; see Aalen (1989). We see two potential uses for this command. One may use it as an alternative to a proportional hazards or other nonlinear hazards regression model analysis to describe the effects of covariates on survival time. A second application is to use the command to supplement a proportional hazards regression model analysis to assist in detecting and then describing the nature of time-varying effects of covariates through plots of the estimated cumulative regression coefficients, with confidence bands, from Aalen’s model. We illustrate the use of the command to perform... |
| Tipo: Journal Article |
Palavras-chave: Survival analysis; Survival-time regression models; Time-to-event analysis; Research Methods/ Statistical Methods. |
| Ano: 2002 |
URL: http://purl.umn.edu/116019 |
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| Registros recuperados: 20 | |
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