A generalized linear mixed model (GLMM) is an extension to the generalized linear mixed (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. They also inherit from GLMs the idea of extending linear mixed models to non-normal data. There are several applications of various types of generalized linear mixed models (GLMMs) to various fields, especially in the areas of health and biological sciences. In this our study Poisson logistic mixed regression model (a class of GLMM) was adopted to investigate the performance of the above mentioned method on some psychiatric patients’ data. A clinical trial of ninety (90) mentally disordered patients was examined in this work. Patients suffering from some level of psychiatric disorder were randomized to receive either Amitryphylline or Benzhexol in addition to other therapy. This work is motivated by Thall and Vail, which investigated the performance of the Poisson logistic mixed model on some epileptics’ data. The two types of therapy have little effect on the patients, but the interaction (between treatments and visits) has a substantial impact on the patients. The number of seizures is reduced by visits, and a combination of visits and medicines decreases the number of seizures. The fact that the treatments are insignificant suggests that mental disorders are mostly treatable with currently available medications. These drugs only ‘manage' them for a short period of time.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 10, Issue 6) |
| DOI | 10.11648/j.ajtas.20211006.13 |
| Page(s) | 243-248 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
GLMMs, Poisson, Regression, Psychiatric, Amitryphylline and Benzhexol
| [1] | Agresti, A. (2002) Categorical Data Analysis. Wiley-Interscience. |
| [2] | Antonio, K., Beirlant, J., Hoedemakers, T., and Verlaak, R. (2006). Lognormal mixed models for reported claims reserves. North American Actuarial Journal, 10: 30–48. |
| [3] | Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Henry, M., Stevens, H., and White, J. S. (2008). Trends in Ecology and Evolution Vol. 24 No. 3, 127-135. |
| [4] | Breslow, N. E. and Clayton, D. G. (1993), Approximate Inference in Generalized Linear Mixed Models, Journal of American Statistical Association 88, 9-25. |
| [5] | Clayton, R. R. (1992). Transitions in drug use: Risk and protective factors. In M. D. Glantz & R. W. Pickens (Eds.), Vulnerability to drug abuse (pp. 15–51). American Psychological Association. https://doi.org/10.1037/10107-001. |
| [6] | Fahrmeir L., Tutz G. (1994) Introduction. In: Multivariate Statistical Modelling Based on Generalized Linear Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-0010-4_1. |
| [7] | Garrido and Zhou (2013). Full Credibility with Generalized Linear and Mixed Models. Published online by Cambridge University Press: pp. 61-80. |
| [8] | Halid O. Y. and Adeleke R. A. (2015). A new estimation method for Generalized Linear Mixed Models, International Journal of Statistics and Applications. |
| [9] | Karim M. R. and Zeger (1992), Generalized Linear models with random effects: Salamander mating revisited, Biometrics 48, 631-644. |
| [10] | Klinker, F. (2011) Generalized Linear Mixed Models for Ratemaking: A Means of Introducing Credibility into a Generalized Linear Model Setting. Casualty Actuarial Society E-Forum, Winter, Vol. 2. |
| [11] | Laird, N. M. and Waire, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38 (4), 513-524. |
| [12] | McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, (2nd edition), Chapman and Hall, London, U.K. |
| [13] | McCulloch, C. E. and Searle, S. R. (2001), Generalized linear and Mixed Models, John Wiley and Sons Inc., New York. |
| [14] | Shun, Z. (1997). Another Look at the salamander mating data: A modified Laplace Approximation Approach. Journal of American Statistical Association 92, 341-349. |
| [15] | Thall, P. F. and Vail, S. C. (1990), Some Covariance Models for Longitudinal Count Data with Over dispersion, Biometrics 46, 657-671. |
| [16] | Thiele, J. and Markussen, B. (2012), Potential of GLMM in Modelling Invasive Spread, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 7, 1-10. |
| [17] | Venables, W. N., Dichmont, C. M., (2004). A generalized linear model for catch allocation: an example of Australia’s Northern Prawn Fishery. Fish. Res. 70, 405–422 |
| [18] | Wang, Z and Louis, T. A (2003). Matching conditional and marginal shape in binary random intercept model using a bridge distribution function. Biometrika: 90 (4): 765-775. |
APA Style
Omobolaji Yusuf Halid, Samuel Oluwaseun Adejuwon, Vincent Gbenga Jemilohun. (2021). On the Performance of a Class of Generalized Linear Mixed Model on Some Psychiatric Patients' Data. American Journal of Theoretical and Applied Statistics, 10(6), 243-248. https://doi.org/10.11648/j.ajtas.20211006.13
ACS Style
Omobolaji Yusuf Halid; Samuel Oluwaseun Adejuwon; Vincent Gbenga Jemilohun. On the Performance of a Class of Generalized Linear Mixed Model on Some Psychiatric Patients' Data. Am. J. Theor. Appl. Stat. 2021, 10(6), 243-248. doi: 10.11648/j.ajtas.20211006.13
AMA Style
Omobolaji Yusuf Halid, Samuel Oluwaseun Adejuwon, Vincent Gbenga Jemilohun. On the Performance of a Class of Generalized Linear Mixed Model on Some Psychiatric Patients' Data. Am J Theor Appl Stat. 2021;10(6):243-248. doi: 10.11648/j.ajtas.20211006.13
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Download@article{10.11648/j.ajtas.20211006.13,
author = {Omobolaji Yusuf Halid and Samuel Oluwaseun Adejuwon and Vincent Gbenga Jemilohun},
title = {On the Performance of a Class of Generalized Linear Mixed Model on Some Psychiatric Patients' Data},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {10},
number = {6},
pages = {243-248},
doi = {10.11648/j.ajtas.20211006.13},
url = {https://doi.org/10.11648/j.ajtas.20211006.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211006.13},
abstract = {A generalized linear mixed model (GLMM) is an extension to the generalized linear mixed (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. They also inherit from GLMs the idea of extending linear mixed models to non-normal data. There are several applications of various types of generalized linear mixed models (GLMMs) to various fields, especially in the areas of health and biological sciences. In this our study Poisson logistic mixed regression model (a class of GLMM) was adopted to investigate the performance of the above mentioned method on some psychiatric patients’ data. A clinical trial of ninety (90) mentally disordered patients was examined in this work. Patients suffering from some level of psychiatric disorder were randomized to receive either Amitryphylline or Benzhexol in addition to other therapy. This work is motivated by Thall and Vail, which investigated the performance of the Poisson logistic mixed model on some epileptics’ data. The two types of therapy have little effect on the patients, but the interaction (between treatments and visits) has a substantial impact on the patients. The number of seizures is reduced by visits, and a combination of visits and medicines decreases the number of seizures. The fact that the treatments are insignificant suggests that mental disorders are mostly treatable with currently available medications. These drugs only ‘manage' them for a short period of time.},
year = {2021}
}
TY - JOUR T1 - On the Performance of a Class of Generalized Linear Mixed Model on Some Psychiatric Patients' Data AU - Omobolaji Yusuf Halid AU - Samuel Oluwaseun Adejuwon AU - Vincent Gbenga Jemilohun Y1 - 2021/11/27 PY - 2021 N1 - https://doi.org/10.11648/j.ajtas.20211006.13 DO - 10.11648/j.ajtas.20211006.13 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 243 EP - 248 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20211006.13 AB - A generalized linear mixed model (GLMM) is an extension to the generalized linear mixed (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. They also inherit from GLMs the idea of extending linear mixed models to non-normal data. There are several applications of various types of generalized linear mixed models (GLMMs) to various fields, especially in the areas of health and biological sciences. In this our study Poisson logistic mixed regression model (a class of GLMM) was adopted to investigate the performance of the above mentioned method on some psychiatric patients’ data. A clinical trial of ninety (90) mentally disordered patients was examined in this work. Patients suffering from some level of psychiatric disorder were randomized to receive either Amitryphylline or Benzhexol in addition to other therapy. This work is motivated by Thall and Vail, which investigated the performance of the Poisson logistic mixed model on some epileptics’ data. The two types of therapy have little effect on the patients, but the interaction (between treatments and visits) has a substantial impact on the patients. The number of seizures is reduced by visits, and a combination of visits and medicines decreases the number of seizures. The fact that the treatments are insignificant suggests that mental disorders are mostly treatable with currently available medications. These drugs only ‘manage' them for a short period of time. VL - 10 IS - 6 ER -
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