Biomedical subjects

M G Kenward

Publications and source records attributed to M G Kenward.

5 recordsLinked to original sources

PubMed1992

Alternative approaches to the analysis of binary and categorical repeated measurements.

The purpose of this paper is to describe, illustrate, and compare a number of different approaches to the analysis of repeated binary and categorical data. These approaches include empirical generalized least squares and generalized estimating equations, as well as traditional log-linear modeling methods. It is shown that the interpretation of the parameters in the various models depends critically on the type of model fitted. In particular, we contrast the population-averaged and subject-specific models. Two example data sets are used to illustrate the approaches, and throughout we concentrate on methods that can be easily implemented.

Clinical Trials as Topic↗

PubMed1991

The analysis of categorical data from cross-over trials using a latent variable model.

A latent variable model for categorical data is described. The nuisance parameters in the model can be eliminated from the analysis through the use of a conditional likelihood and this leads to an analysis based on a log-linear model. It is shown how the structure of a cross-over trial allows considerable simplification in the formulation of this model and routine application of well-known statistical packages. The method is illustrated by data from a three-period cross-over trial on the relief of primary dysmenorrhea using the GLIM and SAS packages.

Analysis of Variance↗

PubMed1987

The analysis of data from 2 x 2 cross-over trials with baseline measurements.

An account is given of the analysis of data from 2 x 2 cross-over trials which include baseline measurements. We show that most of the previously proposed methods can be incorporated into a general framework of least-squares estimation with a simple linear model. A simple analysis based on ordinary least-squares estimators is described which can be used with either two-sample t-tests and confidence intervals or with the corresponding non-parametric procedures. It is shown how the use of generalized least-squares estimators is equivalent to the use of covariance adjustment. These methods require no assumptions about the covariance structure of the measurements from each subject. The results of assessing the covariance structure present in examples of data from a number of trials are summarized. These results suggest that previously proposed simple covariance structures are unlikely to be appropriate in general.

Analysis of Variance↗

PubMed1985

The use of fitted higher-order polynomial coefficients as covariates in the analysis of growth curves.

For orthogonal polynomials fitted to repeated measurements, a computer simulation study is used to investigate the effect of selecting higher-order coefficients as covariates in order to minimise the estimated variance of lower-order coefficients. Under the assumption that the repeated measurements have certain autoregressive covariance structures, it is seen that the gain in precision due to covariance adjustment can be largely illusory and that the resulting estimates of variances can grossly underestimate the true variances. The consequences of using all higher-order coefficients as covariates is also examined and seen to produce a gain in precision in certain circumstances.

Aging↗

PubMedDate not supplied

Modelling binary data from a three-period cross-over trial.

A new method of analysing binary data from a three-treatment, three-period cross-over trial is described. This method is based on a log-linear model and mirrors the analysis of continuous data. It is an extension of the method we introduced recently for the analysis of binary data from a two-treatment, two-period cross-over trial. We illustrate our method using data from a trial which compared two analgesics and a placebo for the relief of primary dysmenorrhea.

Analgesics↗