An Example of the Use of Extended Cross-Over Designs in the Comparison of NPH Insulin Mixtures

Ciminera, Joseph L.; Wolfe, Elwood K.

doi:10.2307/3001435

Cited by 13 publications

(9 citation statements)

References 1 publication

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“…However, all models seem to fit data well with respect to the mean response profile. The conclusion by Putt andChinchilli (1999), andCinemera andWolfe (1953), was that treatment B resulted in a slightly larger linear slope than treatment A. This in agreement with the results of Model 1b, Model 2 ( τ |λ = 0) and Model 3a-3b, and the magnitudes of the treatment difference (A-B) are also in the same range.…”

Section: A Numerical Examplesupporting

confidence: 79%

“…To illustrate the results in Sections 3.1-3.5, a study published by Ciminera and Wolfe (1953) (re-printed by Kenward and Jones (1987) and re-analyzed by Putt and Chinchilli (1999)), is analyzed. The aim of this is to demonstrate the models and the calculations of the maximum likelihood estimates without making any formal statistical inference.…”

Section: A Numerical Examplementioning

confidence: 99%

“…Furthermore, a model with a structured dispersion matrix is defined for the AB:BA design, and a spe-cific linear transformation is used to derive estimators in a convenient way. To illustrate numerically, maximum likelihood estimates are calculated for an ABAB:BABA study published by Ciminera and Wolfe (1953) and later reanalyzed by Putt and Chinchilli (1999).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of cross-over designs via growth curve models

Åsenblad

Rosen

2006

Journal of Statistical Planning and Inference

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Section: A Numerical Examplesupporting

confidence: 79%

Section: A Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of cross-over designs via growth curve models

Åsenblad

Rosen

2006

Journal of Statistical Planning and Inference

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“…Ciminera and Wolfe (1953) give data on the comparison of neutral protamine Hagedorn insulin mixtures. Two mixtures of insulin, the standard (A) and one containing 5% less protamine (B), were tested on rabbits in a crossover design.…”

Section: Examplementioning

confidence: 99%

Multivariate Elliptically Contoured Distributions for Repeated Measurements

Lindsey

1999

Biometrics

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The multivariate power exponential distribution, a member of the multivariate elliptically contoured family, provides a useful generalization of the multivariate normal distribution for the modeling of repeated measurements. Both light and heavy tailed distributions are included. The covariance matrix retains its interpretation so that it can easily be structured for serial dependence and several levels of variance components. A crossover trial on insulin applied to rabbits, with a series of repeated measurements within each period, is analyzed by means of this distribution using autocorrelation and two levels of variance components.

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“…Two mixtures of neutral protamine Hagedorn (NPH) insulin, the standard (A) and one containing 5% less protamine (B) were tested on rabbits in a CToss-over design (Ciminera and Wolfe, 1953).…”

Section: Insulin Cross-over Trialmentioning

confidence: 99%

Multivariate distributions with correlation matrices for nonlinear repeated measurements

Lindsey

Lindsey²

2006

Computational Statistics & Data Analysis

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The polynomial growth curve model based on the multivariate normal distribution has dominated the analysis of continuous longitudinal repeated measurements for the last 50 years. The main reasons include the ease of modelling dependence because of the availability of the correlation matrix and the linearity of the regression coefficients. However, a variety of othPI useful distributions also involve a correlation matrix: the multivariate Student t, multivariate powPI-pxponential, and multivariate skew Laplace distributions, as well as Gaussian copulas with arbitrarily chosen marginal distributions. As well, with modern computing POWPI and software, nonlinear regression functions can be fitted as easily as linPM ones. By a number of pxamples, we show that these distributions, combined with nonlinear regression functions, generally yield an improved fit, as compared to the standard polynomial growth curve model, and can provide diffpxent conclusions.

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An Example of the Use of Extended Cross-Over Designs in the Comparison of NPH Insulin Mixtures

Cited by 13 publications

References 1 publication

Analysis of cross-over designs via growth curve models

Analysis of cross-over designs via growth curve models

Multivariate Elliptically Contoured Distributions for Repeated Measurements

Multivariate distributions with correlation matrices for nonlinear repeated measurements

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