Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms
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 Do...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6685951 |
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| _version_ | 1850174220802195456 |
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| author | Keyi Mou Zhiming Li |
| author_facet | Keyi Mou Zhiming Li |
| author_sort | Keyi Mou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c97f241d25c94dfaacfb2ca3f78d8ae8 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c97f241d25c94dfaacfb2ca3f78d8ae82025-08-20T02:19:42ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66859516685951Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal AlgorithmsKeyi Mou0Zhiming Li1College of Mathematics and System Sciences, Xinjiang University, Urumqi 834800, ChinaCollege of Mathematics and System Sciences, Xinjiang University, Urumqi 834800, ChinaIn 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.http://dx.doi.org/10.1155/2021/6685951 |
| spellingShingle | Keyi Mou Zhiming Li Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms Complexity |
| title | Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms |
| title_full | Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms |
| title_fullStr | Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms |
| title_full_unstemmed | Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms |
| title_short | Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms |
| title_sort | homogeneity test of many to one risk differences for correlated binary data under optimal algorithms |
| url | http://dx.doi.org/10.1155/2021/6685951 |
| work_keys_str_mv | AT keyimou homogeneitytestofmanytooneriskdifferencesforcorrelatedbinarydataunderoptimalalgorithms AT zhimingli homogeneitytestofmanytooneriskdifferencesforcorrelatedbinarydataunderoptimalalgorithms |