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A menu-driven facility for complex sample size calculation in randomized controlled trials with a survival or a binary outcome AgEcon
Royston, Patrick; Babiker, Abdel.
We present a menu-driven Stata program for the calculation of sample size or power for complex clinical trials with a survival time or a binary outcome. The features supported include up to six treatment arms, an arbitrary time-to-event distribution, fixed or time-varying hazard ratios, unequal patient allocation, loss to follow-up, staggered patient entry, and crossover of patients from their allocated treatment to an alternative treatment. The computations of sample size and power are based on the logrank test and are done according to the asymptotic distribution of the logrank test statistic, adjusted appropriately for the design features.
Tipo: Journal Article Palavras-chave: Randomized controlled trials; Survival analysis; Logrank test; Experimental design; Research Methods/ Statistical Methods.
Ano: 2002 URL: http://purl.umn.edu/115958
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A menu-driven facility for complex sample size calculation in randomized controlled trials with a survival or a binary outcome: Update AgEcon
Barthel, Friederike Maria-Sophie; Royston, Patrick; Babiker, Abdel.
Royston and Babiker (2002) presented a menu-driven Stata program for the calculation of sample size or power for complex clinical trial designs under a survival time or binary outcome. In the present article, the package is updated to Stata 8 under the new name ART. Furthermore, the program has been extended to incorporate noninferiority designs and provides more detailed output. This package is the only realistic sample size tool for survival studies available in Stata.
Tipo: Journal Article Palavras-chave: Sample size; Power; Randomized controlled trial; Multiarm designs; Survival analysis; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117503
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A multivariable scatterplot smoother AgEcon
Royston, Patrick; Cox, Nicholas J..
We present an extension of Sasieni, Royston, and Cox’s bivariate smoother running to the multivariable context. The software aims to provide a picture of the relation between a response variable and each of several continuous predictors simultaneously. This may be a valuable tool in exploratory data analysis, before constructing a more formal multiple regression model.
Tipo: Journal Article Palavras-chave: Mrunning; Running; Scatterplot smoothing; Multivariable regression analysis; Running line; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117529
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A new framework for managing and analyzing multiply imputed data in Stata AgEcon
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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Explained variation for survival models AgEcon
Royston, Patrick.
This article introduces a new measure of explained variation for use with censored survival data. It is a modified version of a measure previously described by John O’Quigley and colleagues, itself a modification of Nagelkerke’s earlier proposal for a general index of determination. I describe Stata programs str2ph, which implements the new measure, and str2d, which implements a measure proposed in 2004 by Royston and Sauerbrei. I provide examples with real data.
Tipo: Journal Article Palavras-chave: Censored survival data; Regression models; Index of determination; Explained variation; Explained randomness; Information gain; Research Methods/ Statistical Methods.
Ano: 2006 URL: http://purl.umn.edu/117558
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Flexible parametric alternatives to the Cox model, and more AgEcon
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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Flexible parametric alternatives to the Cox model: update AgEcon
Royston, Patrick.
Royston (2001) and Royston and Parmar (2002) introduced flexible parametric models for survival analysis, implemented in Stata through the ado-file stpm (Royston 2001). In the present article, stpm is updated to Stata 8.1 and has been shown to work correctly with Stata 8.2. To increase the reliability of the estimation procedure, the basis functions of the splines used to approximate the baseline distribution function have been orthogonalized.
Tipo: Journal Article Palavras-chave: Parametric survival analysis; Proportional hazards; Proportional odds; Regression splines; Orthogonal basis functions; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116215
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Graphical representation of interactions AgEcon
Barthel, Friederike Maria-Sophie; Royston, Patrick.
We provide a program to illustrate interactions between treatment and covariates or between two covariates by using forest plots under either the Cox proportional hazards or the logistic regression model. The program is flexible in both the possibility of illustrating more than one interaction at a time and variable specifications of scale.
Tipo: Journal Article Palavras-chave: Fintplot; Interaction; Forest plot; Randomized controlled trial; Survival analysis; Logistic regression; Research Methods/ Statistical Methods.
Ano: 2006 URL: http://purl.umn.edu/117585
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Multiple imputation of missing values AgEcon
Royston, Patrick.
Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained...
Tipo: Journal Article Palavras-chave: Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116244
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Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring AgEcon
Royston, Patrick.
Multiple imputation of missing data continues to be a topic of considerable interest and importance to applied researchers. In this article, the ice package for multiple imputation is further updated. Special attention in this article is paid to imputing interval-censored observations, and a suggestion to use imputation of right-censored survival data to elucidate covariate effects graphically.
Tipo: Article Palavras-chave: Ice; Uvis; Micombine; Ice_reformat; Multiple imputation; Interval censoring; Visualization; Censored survival data; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119290
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Multiple imputation of missing values: update AgEcon
Royston, Patrick.
This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis, the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described.
Tipo: Journal Article Palavras-chave: Ice; Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117511
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Multiple imputation of missing values: Update of ice AgEcon
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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Multivariable modeling with cubic regression splines: A principled approach AgEcon
Royston, Patrick; Sauerbrei, Willi.
Spline functions provide a useful and flexible basis for modeling relationships with continuous predictors. However, to limit instability and provide sensible regression models in the multivariable setting, a principled approach to model selection and function estimation is important. Here the multivariable fractional polynomials approach to model building is transferred to regression splines. The essential features are specifying a maximum acceptable complexity for each continuous function and applying a closed-test approach to each continuous predictor to simplify the model where possible. Important adjuncts are an initial choice of scale for continuous predictors (linear or logarithmic), which often helps one to generate realistic, parsimonious final...
Tipo: Article Palavras-chave: Mvrs; Uvrs; Splinegen; Multivariable analysis; Continuous predictor; Regression spline; Model building; Goodness of fit; Choice of scale; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119254
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Profile likelihood for estimation and confidence intervals AgEcon
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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Sort a list of items AgEcon
Royston, Patrick.
The command listsort for sorting the contents of a set of local macros is introduced and illustrated.
Tipo: Journal Article Palavras-chave: Sorting; Local macros; Research Methods/ Statistical Methods.
Ano: 2001 URL: http://purl.umn.edu/115941
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Stata at 20: a personal view AgEcon
Royston, Patrick.
Tipo: Journal Article Palavras-chave: Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117498
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Stata tip 11: The nolog option with maximum-likelihood modeling commands AgEcon
Royston, Patrick.
Tipo: Journal Article Palavras-chave: Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116255
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Stata tip 19: A way to leaner, faster graphs AgEcon
Royston, Patrick.
Tipo: Journal Article Palavras-chave: Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117519
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Stata tip 7: Copying and pasting under Windows AgEcon
Driver, Shannon; Royston, Patrick.
Tipo: Journal Article Palavras-chave: Research Methods/ Statistical Methods.
Ano: 2004 URL: http://purl.umn.edu/116240
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Using Aalen's linear hazards model to investigate time-varying effects in the proportional hazards regression model AgEcon
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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