Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal Survival-Cure Model

Patients treated for prostate cancer are monitored by periodically measuring prostate-specific antigen (PSA) after treatment. Increases in PSA are suggestive of cancer recurrence and are used in making decisions about possible new treatments. The data from studies of such patients typically consist...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 103; H. 481; S. 178 - 187
Hauptverfasser:	Yu, Menggang, Taylor, Jeremy M. G, Sandler, Howard M
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria, VA Taylor & Francis 01.03.2008 American Statistical Association Taylor & Francis Ltd
Schlagworte:	Applications Applications and Case Studies Cancer Cancer therapies Censorship Cure Cure models Data Datasets Decision making Disease progression Diseases Exact sciences and technology General topics Inference from stochastic processes; time series analysis Joint model Longitudinal model Longitudinal studies Markov analysis Markov chain Monte Carlo Mathematics Measurement Measurement errors Medical treatment Modeling Monte Carlo simulation Parametric models Patients Predictions Probability and statistics Probability theory and stochastic processes Prostate Prostate cancer Prostate cancer data Radiation Radiation therapy Radiotherapy Recurrence Sciences and techniques of general use Statistical analysis Statistical data Statistical methods Statistical models Statistical variance Statistics Stochastic processes Survival analysis Survival model T distribution Therapy Treatment methods Tumors Validity Statistical distribution Prediction theory Probability distribution Multivariate analysis Stochastic method Stochastic process Markov chain Outlier Correspondence analysis Survival function Markov chain Monte Carlo Distribution function Approximation theory Survival model Joint model Censored data Mixed distribution Monte Carlo method Discriminant analysis Factor analysis Decision making Recurrence relation Statistical estimation Cure models Logistic distribution Statistical method Numerical analysis Non linear model Filtering theory Prostate cancer data Longitudinal model Principal component analysis
ISSN:	0162-1459, 1537-274X
Online-Zugang:	Volltext
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