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Articles 1 - 4 of 4
Full-Text Articles in Survival Analysis
Flexible Modelling Of Time-Dependent Covariate Effects With Correlated Competing Risks: Application To Hereditary Breast And Ovarian Cancer Families, Seungwoo Lee
Electronic Thesis and Dissertation Repository
This thesis aims to develop a flexible approach for modelling time-dependent covariate effects on event risk using B-splines in the presence of correlated competing risks. The performance of the proposed model was evaluated via simulation in terms of the bias and precision of the estimation of the parameters and penetrance functions. In addition, we extended the concordance index to account for time-dependent effects and competing events simultaneously and demonstrated its inference procedures. We applied our proposed methods to data rising from the BRCA1 mutation families from the breast cancer family registry to evaluate the time-dependent effects of mammographic screening and …
Nonparametric Estimation Of The Case Fatality Ratio With Competing Risks Data: An Application To Severe Acute Respiratory Syndome (Sars) , Nicholas P. Jewell, Xiudong Lei, A. C. Ghani, C. A. Donnelly, G. M. Leung, L. M. Ho, B. Cowling, A. J. Hedley
Nonparametric Estimation Of The Case Fatality Ratio With Competing Risks Data: An Application To Severe Acute Respiratory Syndome (Sars) , Nicholas P. Jewell, Xiudong Lei, A. C. Ghani, C. A. Donnelly, G. M. Leung, L. M. Ho, B. Cowling, A. J. Hedley
U.C. Berkeley Division of Biostatistics Working Paper Series
For diseases with some level of associated mortality, the case fatality ratio measures the proportion of diseased individuals who die from the disease. In principle, it is straightforward to estimate this quantity from individual follow-up data that provides times from onset to death or recovery. In particular, in a competing risks context, the case fatality ratio is defined by the limiting value of the sub-distribution function, associated with death, at infinity. When censoring is present, however, estimation of this quantity is complicated by the possibility of little information in the right tail of of the sub-distribution function, requiring use of …
Current Status Data: Review, Recent Developments And Open Problems, Nicholas P. Jewell, Mark J. Van Der Laan
Current Status Data: Review, Recent Developments And Open Problems, Nicholas P. Jewell, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Researchers working with survival data are by now adept at handling issues associated with incomplete data, particular those associated with various forms of censoring. An extreme form of interval censoring, known as current status observation, refers to situations where the only available information on a survival random variable T is whether or not T exceeds a random independent monitoring time C. This article contains a brief review of the extensive literature on the analysis of current status data, discussing the implications of response-based sampling on these methods. The majority of the paper introduces some recent extensions of these ideas to …
Maximum Likelihood Estimation Of Ordered Multinomial Parameters, Nicholas P. Jewell, John D. Kalbfleisch
Maximum Likelihood Estimation Of Ordered Multinomial Parameters, Nicholas P. Jewell, John D. Kalbfleisch
U.C. Berkeley Division of Biostatistics Working Paper Series
The pool-adjacent violator-algorithm (Ayer, et al., 1955) has long been known to give the maximum likelihood estimator of a series of ordered binomial parameters, based on an independent observation from each distribution (see Barlow et al., 1972). This result has immediate application to estimation of a survival distribution based on current survival status at a set of monitoring times. This paper considers an extended problem of maximum likelihood estimation of a series of ‘ordered’ multinomial parameters. By making use of variants of the pool adjacent violator algorithm, we obtain a simple algorithm to compute the maximum likelihood estimator and demonstrate …