|Title||Additive mixed effect model for clustered failure time data.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Cai, Jianwen, and Donglin Zeng|
|Date Published||2011 Dec|
|Keywords||Cluster Analysis, Computer Simulation, Data Interpretation, Statistical, Disease-Free Survival, Humans, Models, Statistical, Outcome Assessment, Health Care, Risk Assessment, Risk Factors, Treatment Failure|
We propose an additive mixed effect model to analyze clustered failure time data. The proposed model assumes an additive structure and includes a random effect as an additional component. Our model imitates the commonly used mixed effect models in repeated measurement analysis but under the context of hazards regression; our model can also be considered as a parallel development of the gamma-frailty model in additive model structures. We develop estimating equations for parameter estimation and propose a way of assessing the distribution of the latent random effect in the presence of large clusters. We establish the asymptotic properties of the proposed estimator. The small sample performance of our method is demonstrated via a large number of simulation studies. Finally, we apply the proposed model to analyze data from a diabetic study and a treatment trial for congestive heart failure.
|Original Publication||Additive mixed effect model for clustered failure time data.|
|PubMed Central ID||PMC3139827|
|Grant List||P01 CA142538-01 / CA / NCI NIH HHS / United States |
R01-HL57444 / HL / NHLBI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 HL057444-13 / HL / NHLBI NIH HHS / United States
P01-CA142538 / CA / NCI NIH HHS / United States
R01 HL057444 / HL / NHLBI NIH HHS / United States
Additive mixed effect model for clustered failure time data.