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Browsing by Subject "survival analysis"
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Item An Analysis of Survival Data when Hazards are not Proportional: Application to a Cancer Treatment Study(2021-12) White, John Benjamin; Yiannoutsos, Constantin; Bakoyannis, Giorgos; Fadel, WilliamThe crossing of Kaplan-Meier survival curves presents a challenge when conducting survival analysis studies, making it unclear whether any of the study groups involved present any significant difference in survival. An approach involving the determination of maximum vertical distance between the curves is considered here as a method to assess whether a survival advantage exists between different groups of patients. The method is illustrated on a dataset containing survival times of patients treated with two cancer treatment regimes, one involving treatment by chemotherapy alone, and the other by treatment with both chemotherapy and radiotherapy.Item Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes(Springer, 2016-01) Li, Shanshan; Department of Epidemiology, Richard M. Fairbanks School of Public HealthWhen conducting recurrent event data analysis, it is common to assume that the covariate processes are observed throughout the follow-up period. In most applications, however, the values of time-varying covariates are only observed periodically rather than continuously. A popular ad-hoc approach is to carry forward the last observed covariate value until it is measured again. This simple approach, however, usually leads to biased estimation. To tackle this problem, we propose to model the covariate effect on the risk of the recurrent events through jointly modeling the recurrent event process and the longitudinal measures. Despite its popularity, estimation of the joint model with binary longitudinal measurements remains a challenge, because the standard linear mixed effects model approach is not appropriate for binary measures. In this paper, we postulate a Markov model for the binary covariate process and a random-effect proportional intensity model for the recurrent event process. We use a Markov chain Monte Carlo algorithm to estimate all the unknown parameters. The performance of the proposed estimator is evaluated via simulations. The methodology is applied to an observational study designed to evaluate the effect of Group A streptococcus on pharyngitis among school children in India.Item Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer(Springer, 2018) Shao, Wei; Cheng, Jun; Sun, Liang; Han, Zhi; Feng, Qianjin; Zhang, Daoqiang; Huang, Kun; Medicine, School of MedicineExisting studies have demonstrated that combining genomic data and histopathological images can better stratify cancer patients with distinct prognosis than using single biomarker, for different biomarkers may provide complementary information. However, these multi-modal data, most high-dimensional, may contain redundant features that will deteriorate the performance of the prognosis model, and therefore it has become a challenging problem to select the informative features for survival analysis from the redundant and heterogeneous feature groups. Existing feature selection methods assume that the survival information of one patient is independent to another, and thus miss the ordinal relationship among the survival time of different patients. To solve this issue, we make use of the important ordinal survival information among different patients and propose an ordinal sparse canonical correlation analysis (i.e., OSCCA) framework to simultaneously identify important image features and eigengenes for survival analysis. Specifically, we formulate our framework basing on sparse canonical correlation analysis model, which aims at finding the best linear projections so that the highest correlation between the selected image features and eigengenes can be achieved. In addition, we also add constrains to ensure that the ordinal survival information of different patients is preserved after projection. We evaluate the effectiveness of our method on an early-stage renal cell carcinoma dataset. Experimental results demonstrate that the selected features correlated strongly with survival, by which we can achieve better patient stratification than the comparing methods.