Balanced Change‐over Designs for Autocorrelated Observations

Gill, Paramjit

doi:10.1111/j.1467-842x.1992.tb01057.x

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Because their information matrix C τ in (19.11) is completely symmetric it follows, of course, that comparisons between two direct effects are estimated with the same variance. Gill (1992) investigated this property for the case of autocorrelated errors (remember that C τ is now a function of ρ). He found that for p ≤ t and t a prime or prime power the method proposed by Williams (1949) can be used to construct a balanced design with t (t − 1) subjects.…”

Section: Autocorrelation Error Structurementioning

confidence: 99%

Computer Experiments

Morris

2012

Design and Analysis of Experiments

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Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Section: Autocorrelation Error Structurementioning

confidence: 99%

Computer Experiments

Morris

2012

Design and Analysis of Experiments

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“…Correlated observations models for CODs in the presence of carryover effects were discussed by Finney (1956), Taka and Armitage (1983), Laska and Meisner (1985), and Gill and Shukla (1987). Gill (1992) found that generalized least squares estimation of direct treatment effect and first-order carryover effect for CODs under autocorrelated observation model is very difficult. An easy way out is to consider a design that is robust to correlation structure similar to Ipinyomi (1986), who introduced a class of block designs called equineighbored block designs in which each treatment is paired with other treatments in every plot position.…”

Section: Introductionmentioning

confidence: 98%

Estimation of Treatment and Carryover Effects in Optimal Cross-Over Designs for Clinical Trials

Divecha

Gondaliya

2015

Statistics in Biopharmaceutical Research

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A particular multi-period cross-over design useful in trials where observations naturally are correlated and carryover effect may not die after one period-such as thorough QT, trials on diet, asthma, and others-is shown to be useful for estimation in three practical cases. The cases are: the variance balanced estimation of direct and carryover treatment effects in presence of higher-order carryover and correlated errors using ordinary least squares method; estimation of the said treatment effects under scattered missing observations; and the interim estimation of the same for trials with early stopping rules. We compare treatment effect estimates and their variances with those given in the literature for the higher-order carryover model. We also give a numerical example demonstrating estimation in all three cases.

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“…An inaccurate analyzed of a study can produce misleading results for that study. Repeated measures experimental designs require special attention, since in practice the observations within each subject are more likely to be correlated with different covariance structures that makes their analysis different from other factorial experiments (Bellavance et al, 1996;Gill, 1992;McCulloch, 2003). Considering the right covariance structure for the observations within each subject is an important aspect of the analysis of repeated measures experiment; this is where the dependency due to the repeated measures is taken into account.…”

Section: Introductionmentioning

confidence: 99%

Selecting the Covariance Structure in Mixed Model Using Statistical Methods Calibration

AL-Marshadi¹

2014

Journal of Mathematics and Statistics

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In this article the analysis of experiment of repeated measures design is considered which is used often in different fields of studies. In order to analyze the experiment of repeated measures design efficiently we need to select the suitable covariance structure which required a lot of efforts. In the current paper an approach is used to guide the selection of the covariance structure for the analysis of repeated measures design with high rate of success. Five well known model selection criteria are used in the approach. Simulation study is used to evaluate the approach in terms of its ability to select the right covariance structure. The evaluation of the approach was in terms of its percentage of times that it identifies the right covariance structure. Overall, the simulation study showed excellent performance for the approach in all the study cases. The main result of our article is that we recommend considering the approach as a standard way to select the right covariance structure.

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Balanced Change‐over Designs for Autocorrelated Observations

Cited by 8 publications

References 14 publications

Computer Experiments

Computer Experiments

Estimation of Treatment and Carryover Effects in Optimal Cross-Over Designs for Clinical Trials

Selecting the Covariance Structure in Mixed Model Using Statistical Methods Calibration

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