August  2004, 4(3): 813-821. doi: 10.3934/dcdsb.2004.4.813

Testing increasing hazard rate for the progression time of dementia

1. 

Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, United States, United States, United States

2. 

Department of Surgery, Washington University in St. Louis, St. Louis, MO 63110, United States

3. 

Department of Neurology and Pathology and Immunology, Washington University in St. Louis, St. Louis, MO 63110, United States

Received  January 2003 Revised  January 2004 Published  May 2004

In the longitudinal studies of certain diseases, subjects are assessed periodically. In fact, many Alzheimer's Disease Research Centers (ADRC) in the United States typically assess their subjects annually, resulting in grouped or interval censored data for the progression time from one stage of dementia to a more severe stage of dementia. This paper studies the likelihood ratio test for increasing hazard rate associated with the progression time of dementia based on grouped progression time data. We first give the maximum likelihood estimators (MLEs) for model parameters under the assumption that the hazard rate of the progression time is nondecreasing. We then present the likelihood ratio test for testing the null hypothesis that the hazard rate is constant against the alternative that it is increasing. Finally, the methodology is applied to the dementia progression time from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD). The statistical methodology developed here, although specifically referred to the study of dementia in the paper, can be easily applied to other longitudinal medical studies in which the disease status is categorized according to the severity and the hazard rate associated with the transition time among disease stages is to be tested.
Citation: C. Xiong, J.P. Miller, F. Gao, Y. Yan, J.C. Morris. Testing increasing hazard rate for the progression time of dementia. Discrete & Continuous Dynamical Systems - B, 2004, 4 (3) : 813-821. doi: 10.3934/dcdsb.2004.4.813
[1]

Jie Huang, Xiaoping Yang, Yunmei Chen. A fast algorithm for global minimization of maximum likelihood based on ultrasound image segmentation. Inverse Problems & Imaging, 2011, 5 (3) : 645-657. doi: 10.3934/ipi.2011.5.645

[2]

R.L. Sheu, M.J. Ting, I.L. Wang. Maximum flow problem in the distribution network. Journal of Industrial & Management Optimization, 2006, 2 (3) : 237-254. doi: 10.3934/jimo.2006.2.237

[3]

Mikko Kaasalainen. Multimodal inverse problems: Maximum compatibility estimate and shape reconstruction. Inverse Problems & Imaging, 2011, 5 (1) : 37-57. doi: 10.3934/ipi.2011.5.37

[4]

Tianfa Xie, Zhong-Zhan Zhang. Identifiability of models for clinical trials with noncompliance. Discrete & Continuous Dynamical Systems - B, 2004, 4 (3) : 805-811. doi: 10.3934/dcdsb.2004.4.805

[5]

Evans K. Afenya. Using Mathematical Modeling as a Resource in Clinical Trials. Mathematical Biosciences & Engineering, 2005, 2 (3) : 421-436. doi: 10.3934/mbe.2005.2.421

[6]

Johnathan M. Bardsley. A theoretical framework for the regularization of Poisson likelihood estimation problems. Inverse Problems & Imaging, 2010, 4 (1) : 11-17. doi: 10.3934/ipi.2010.4.11

[7]

Johnathan M. Bardsley. An efficient computational method for total variation-penalized Poisson likelihood estimation. Inverse Problems & Imaging, 2008, 2 (2) : 167-185. doi: 10.3934/ipi.2008.2.167

[8]

Kevin Ford. The distribution of totients. Electronic Research Announcements, 1998, 4: 27-34.

[9]

Andrew Raich. Heat equations and the Weighted $\bar\partial$-problem. Communications on Pure & Applied Analysis, 2012, 11 (3) : 885-909. doi: 10.3934/cpaa.2012.11.885

[10]

Selim Esedoḡlu, Fadil Santosa. Error estimates for a bar code reconstruction method. Discrete & Continuous Dynamical Systems - B, 2012, 17 (6) : 1889-1902. doi: 10.3934/dcdsb.2012.17.1889

[11]

Shaoyong Lai, Qichang Xie. A selection problem for a constrained linear regression model. Journal of Industrial & Management Optimization, 2008, 4 (4) : 757-766. doi: 10.3934/jimo.2008.4.757

[12]

Adil Bagirov, Sona Taheri, Soodabeh Asadi. A difference of convex optimization algorithm for piecewise linear regression. Journal of Industrial & Management Optimization, 2019, 15 (2) : 909-932. doi: 10.3934/jimo.2018077

[13]

Wenxiong Chen, Congming Li. A priori estimate for the Nirenberg problem. Discrete & Continuous Dynamical Systems - S, 2008, 1 (2) : 225-233. doi: 10.3934/dcdss.2008.1.225

[14]

H. O. Fattorini. The maximum principle in infinite dimension. Discrete & Continuous Dynamical Systems - A, 2000, 6 (3) : 557-574. doi: 10.3934/dcds.2000.6.557

[15]

Tim Alderson, Alessandro Neri. Maximum weight spectrum codes. Advances in Mathematics of Communications, 2019, 13 (1) : 101-119. doi: 10.3934/amc.2019006

[16]

John Sylvester. An estimate for the free Helmholtz equation that scales. Inverse Problems & Imaging, 2009, 3 (2) : 333-351. doi: 10.3934/ipi.2009.3.333

[17]

Kim Knudsen, Matti Lassas, Jennifer L. Mueller, Samuli Siltanen. Regularized D-bar method for the inverse conductivity problem. Inverse Problems & Imaging, 2009, 3 (4) : 599-624. doi: 10.3934/ipi.2009.3.599

[18]

Nuutti Hyvönen, Lassi Päivärinta, Janne P. Tamminen. Enhancing D-bar reconstructions for electrical impedance tomography with conformal maps. Inverse Problems & Imaging, 2018, 12 (2) : 373-400. doi: 10.3934/ipi.2018017

[19]

Jiang Xie, Junfu Xu, Celine Nie, Qing Nie. Machine learning of swimming data via wisdom of crowd and regression analysis. Mathematical Biosciences & Engineering, 2017, 14 (2) : 511-527. doi: 10.3934/mbe.2017031

[20]

Song Wang, Quanxi Shao, Xian Zhou. Knot-optimizing spline networks (KOSNETS) for nonparametric regression. Journal of Industrial & Management Optimization, 2008, 4 (1) : 33-52. doi: 10.3934/jimo.2008.4.33

2018 Impact Factor: 1.008

Metrics

  • PDF downloads (3)
  • HTML views (0)
  • Cited by (2)

Other articles
by authors

[Back to Top]