On estimating average effects for multiple treatment groups

2012 ◽  
Vol 32 (11) ◽  
pp. 1829-1841 ◽  
Author(s):  
V. Landsman ◽  
R.M. Pfeiffer
Keyword(s):  
Treatment Groups ◽  
Multiple Treatment

scholarly journals Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Keyi Mou ◽  
Zhiming Li
Keyword(s):  
Binary Data ◽  
Estimation Error ◽  
Control Group ◽  
Homogeneity Test ◽  
Optimal Algorithms ◽  
Score Statistic ◽  
Correlated Binary Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Rate Score

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

scholarly journals Heat map visualization of high-density clinical chemistry data

2007 ◽  
Vol 31 (2) ◽  
pp. 352-356 ◽  
Author(s):  
J. Todd Auman ◽  
Gary A. Boorman ◽  
Ralph E. Wilson ◽  
Gregory S. Travlos ◽  
Richard S. Paules
Keyword(s):  
Large Scale ◽  
Clinical Chemistry ◽  
High Density ◽  
Male Rats ◽  
Z Score ◽  
Heat Map ◽  
Density Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Chemistry Data

Clinical chemistry data are routinely generated as part of preclinical animal toxicity studies and human clinical studies. With large-scale studies involving hundreds or even thousands of samples in multiple treatment groups, it is currently difficult to interpret the resulting complex, high-density clinical chemistry data. Accordingly, we conducted this study to investigate methods for easy visualization of complex, high-density data. Clinical chemistry data were obtained from male rats each treated with one of eight different acute hepatotoxicants from a large-scale toxicogenomics study. The raw data underwent a Z-score transformation comparing each individual animal's clinical chemistry values to that of reference controls from all eight studies and then were visualized in a single graphic using a heat map. The utility of using a heat map to visualize high-density clinical chemistry data was explored by clustering changes in clinical chemistry values for >400 animals. A clear distinction was observed in animals displaying hepatotoxicity from those that did not. Additionally, while animals experiencing hepatotoxicity showed many similarities in the observed clinical chemistry alterations, distinct differences were noted in the heat map profile for the different compounds. Using a heat map to visualize complex, high-density clinical chemistry data in a single graphic facilitates the identification of previously unrecognized trends. This method is simple to implement and maintains the biological integrity of the data. The value of this clinical chemistry data transformation and visualization will manifest itself through integration with other high-density data, such as genomics data, to study physiology at the systems level.

On Optimal Allocation of Experimental Units with Known Covariates into Multiple Treatment Groups

2016 ◽  
Vol 68 (1-2) ◽  
pp. 69-81 ◽  
Author(s):  
Samrat Hore ◽  
Anup Dewanji ◽  
Aditya Chatterjee
Keyword(s):  
Optimal Allocation ◽  
Treatment Groups ◽  
Multiple Treatment

Estimation of average treatment effects among multiple treatment groups by using an ensemble approach

2019 ◽  
Vol 38 (15) ◽  
pp. 2828-2846 ◽  
Author(s):  
Xiaofang Yan ◽  
Younathan Abdia ◽  
Somnath Datta ◽  
K. B. Kulasekera ◽  
Beatrice Ugiliweneza ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Ensemble Approach ◽  
Average Treatment ◽  
Average Treatment Effects
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