Biomedical subjects

M G Kenward

Publications and source records attributed to M G Kenward.

24 records · Page 2Linked to original sources

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↗

PubMed1971

An approach to the design of wheelchairs for young users.

Sixty six physically handicapped children between the ages of 5 and 16 took part in an anthropometric study of young wheelchair users. All those involved had at one time used a wheelchair regularly, and only 9 in the study habitually walked. Using a standard body measuring instrument, 13 measurements of bodily dimensions were taken with the children sitting in their usual chairs. An assessment was also made of the degree of support afforded by the backrest. Five dimensions normally used in prescribing wheelchairs to patients were used as a basis for working out the sizes of the various sections of a wheelchair, such as seat, backrest, armrest and frame size. The raw data indicated that appreciable inconsistencies in body shape existed which were not allowed for in present wheelchair design. A new approach is proposed in which wheelchairs would be assembled from interlocking sections of variable size . This arrangement would allow wheelchairs to be fitted more completely to the individual.

Journal Article↗

PubMedDate not supplied

Missing data perspectives of the fluvoxamine data set: a review.

Fitting models to incomplete categorical data requires more care than fitting models to the complete data counterparts, not only in the setting of missing data that are non-randomly missing, but even in the familiar missing at random setting. Various aspects of this point of view have been considered in the literature. We review it using data from a multi-centre trial on the relief of psychiatric symptoms. First, it is shown how the usual expected information matrix (referred to as naive information) is biased even under a missing at random mechanism. Second, issues that arise under non-random missingness assumptions are illustrated. It is argued that at least some of these problems can be avoided using contextual information.

Data Interpretation, Statistical↗

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↗