Patrick Royston
Biographic Data
| ID | 8877198 |
|---|---|
| NAME | Patrick Royston |
| GIVEN NAMES | Patrick |
| FAMILY NAME | Royston |
| SIGNATURE | ROYSTON P |
| AFFILIATIONS | MRC Clinical Trials Unit at UCL |
| VERIFIED | No |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1980 |
| LATEST PUBLICATION YEAR | 2014 |
| H-INDEX | 0 |
Tuning multiple imputation by predictive mean matching and local residual draws
BACKGROUND: Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local res…
Multiple Imputation by Chained Equations (Mice)
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 Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of th…
Multiple imputation using chained equations
Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the l…
Multiple imputation for missing data in epidemiological and clinical research
Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
Multiple Imputation of Missing Values
We present an update of mim, a program for managing multiply imputed 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 to the sampling variability of the imputation process) in parameter estimates and in related quantities, and a general routine for combining any scalar estima…
Multiple Imputation of Missing Values
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 by chained equations (also known as fully conditional specification) is further updated. Special attention is paid to categorical variables. The relationship between ice and the new multiple-imputation system in Stata 11 is clarified.
A New Framework for Managing and Analyzing Multiply Imputed Data in Stata
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 stac…
Dichotomizing continuous predictors in multiple regression
In medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power and residual confounding. In addition, the use of a d…
The cost of dichotomising continuous variables
Myeloid-derived suppressor cells (MDSCs) are present in elevated numbers in TB patients and have been found to be permissive for Mycobacterium tuberculosis ( Mtb ) proliferation. To determine whether depletion of MDSCs may improve host control of TB, we used a novel diphtheria toxin-based fusion protein known as DABIL-4 that targets and depletes IL-4-receptor positive cells. We show that DABIL-4 depletes both PMN-MDSCs and M-MDSC in the mouse TB …
Multiple Imputation of Missing Values
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…
Multiple Imputation of Missing Values
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 us…
Multiple Imputation of Missing Values
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. H…
Regression Using Fractional Polynomials of Continuous Covariates
Patrick Royston, Douglas G. Altman, Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 43, No. 3 (1994), pp. 429-467
An evaluation of the bioself 110 electronic fertility indicator as a contraceptive aid
The Bioself 110 is a hand-held electronic device that combines the BBT and calendar methods of fertility regulation for planning or preventing pregnancy. A pilot study was undertaken in three centers in the United Kingdom to evaluate the Bioself 110 as a contraceptive aid. This paper deals with 1238 cycles from 131 women. Only one unplanned pregnancy occurred where a volunteer correctly used the Bioself 110 and had intercourse on a supposedly saf…
The Long-Term Effect of Energy Intake on Salivation, Hunger, and Appetite Ratings, and Estimates of Energy Intake in Obese Patients
The relationship between salivation, hunger, appetite, and estimates of energy intake was examined in female obese inpatients undergoing a course of weight reduction. On a constant low-energy intake (800 kcal/day) salivation, hunger, and appetite scores all declined. Estimates of energy intake did not. Sixteen different subjects were fed 1000 kcal/day for seven days, 500 kcal/day for seven days, and 1000 kcal/day for seven days. After acclimatiza…
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The Long-Term Effect of Energy Intake on Salivation, Hunger, and Appetite Ratings, and Estimates of Energy Intake in Obese Patients
The relationship between salivation, hunger, appetite, and estimates of energy intake was examined in female obese inpatients undergoing a course of weight reduction. On a constant low-energy intake (800 kcal/day) salivation, hunger, and appetite scores all declined. Estimates of energy intake did not. Sixteen different subjects were fed 1000 kcal/day for seven days, 500 kcal/day for seven days, and 1000 kcal/day for seven days. After acclimatiza…
An evaluation of the bioself 110 electronic fertility indicator as a contraceptive aid
The Bioself 110 is a hand-held electronic device that combines the BBT and calendar methods of fertility regulation for planning or preventing pregnancy. A pilot study was undertaken in three centers in the United Kingdom to evaluate the Bioself 110 as a contraceptive aid. This paper deals with 1238 cycles from 131 women. Only one unplanned pregnancy occurred where a volunteer correctly used the Bioself 110 and had intercourse on a supposedly saf…
Regression Using Fractional Polynomials of Continuous Covariates
Patrick Royston, Douglas G. Altman, Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 43, No. 3 (1994), pp. 429-467
Multiple Imputation of Missing Values
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. H…
Multiple Imputation of Missing Values
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…
Multiple Imputation of Missing Values
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 us…
Dichotomizing continuous predictors in multiple regression
In medical research, continuous variables are often converted into categorical variables by grouping values into two or more categories. We consider in detail issues pertaining to creating just two groups, a common approach in clinical research. We argue that the simplicity achieved is gained at a cost; dichotomization may create rather than avoid problems, notably a considerable loss of power and residual confounding. In addition, the use of a d…
The cost of dichotomising continuous variables
Myeloid-derived suppressor cells (MDSCs) are present in elevated numbers in TB patients and have been found to be permissive for Mycobacterium tuberculosis ( Mtb ) proliferation. To determine whether depletion of MDSCs may improve host control of TB, we used a novel diphtheria toxin-based fusion protein known as DABIL-4 that targets and depletes IL-4-receptor positive cells. We show that DABIL-4 depletes both PMN-MDSCs and M-MDSC in the mouse TB …
A New Framework for Managing and Analyzing Multiply Imputed Data in Stata
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 stac…
Multiple imputation for missing data in epidemiological and clinical research
Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
Multiple Imputation of Missing Values
We present an update of mim, a program for managing multiply imputed 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 to the sampling variability of the imputation process) in parameter estimates and in related quantities, and a general routine for combining any scalar estima…
Multiple Imputation of Missing Values
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 by chained equations (also known as fully conditional specification) is further updated. Special attention is paid to categorical variables. The relationship between ice and the new multiple-imputation system in Stata 11 is clarified.
Multiple Imputation by Chained Equations (Mice)
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 Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of th…
Multiple imputation using chained equations
Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the l…
Tuning multiple imputation by predictive mean matching and local residual draws
BACKGROUND: Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local res…
Computer Science (14 works) · Statistical Methods and Bayesian Inference (11 works) · Imputation (statistics) (10 works) · Missing data (10 works) · Data mining (9 works) · Mathematics (9 works) · Statistical Methods and Inference (9 works) · Statistics (9 works) · Econometrics (6 works) · Machine learning (6 works)