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- Bivariate exponential (2)
- Survival analysis (2)
- Accelerated failure time model (1)
- Adaptive Dantzig variable selector; Censored linear regression; Buckley-James imputation; Model selection consistency; Asymptotic normality (1)
- Bayesian Additive Regression Trees (1)
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- Bayesian Inference (1)
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- Biomarkers; Disease prognosis; Predictive accuracy; Risk prediction; Survival analysis (1)
- Brain tumor (1)
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- Causal effect; causal inference; maximum likelihood estimation; super efficiency; cox-proportional hazards; G-computation (1)
- Dirac delta (1)
- Discrminant analysis; Nonparametric function estimation; Prediction; Receiver operating characteristics curve (1)
- Equivalence study; Event driven study; Kaplan-Meier curve; Non-inferiority trial; Post-market study; Proportional hazards estimate (1)
- Glioblastoma multiforme (1)
- Glioma (1)
- Measurement error (1)
- Methodology (1)
- Microarray (1)
- Predction (1)
- Principled sure independence screening; Multiple myeloma; Variable selection; Sure independence screening; Cox model; Ultra-high-dimensional covariates (1)
- Proportional hazards model (1)
- Prostate cancer (1)
- Random forest (1)
- Reliability models (1)
- Simulation and extrapolation method (1)
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Articles 1 - 13 of 13
Full-Text Articles in Survival Analysis
Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong
Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong
Electronic Thesis and Dissertation Repository
Microarray technology is essentially a measurement tool for measuring expressions of genes, and this measurement is subject to measurement error. Gene expressions could be employed as predictors for patient survival, and the measurement error involved in the gene expression is often ignored in the analysis of microarray data in the literature. Efforts are needed to establish statistical method for analyzing microarray data without ignoring the error in gene expression. A typical microarray data set has a large number of genes far exceeding the sample size. Proper selection of survival relevant genes contributes to an accurate prediction model. We study the …
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Improving Statistical Analysis Of Prospective Clinical Trials In Stem Cell Transplantation. An Inventory Of New Approaches In Survival Analysis, Aurelien Latouche
Improving Statistical Analysis Of Prospective Clinical Trials In Stem Cell Transplantation. An Inventory Of New Approaches In Survival Analysis, Aurelien Latouche
COBRA Preprint Series
The CLINT project is an European Union funded project, run as a specific support action, under the sixth framework programme. It is a 2 year project aimed at supporting the European Group for Blood and Marrow Transplantation (EBMT) to develop its infrastructure for the conduct of trans-European clinical trials in accordance with the EU Clinical Trials Directive, and to facilitate International prospective clinical trials in stem cell transplantation. The initial task is to create an inventory of the existing biostatistical literature on new approaches to survival analyses that are not currently widely utilised. The estimation of survival endpoints is introduced, …
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Dissertations & Theses (Open Access)
Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. …
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study, Lihui Zhao, Lu Tian, Hajime Uno, Scott D. Solomon, Marc A. Pfeffer, J. S. Schindler, L. J. Wei
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study, Lihui Zhao, Lu Tian, Hajime Uno, Scott D. Solomon, Marc A. Pfeffer, J. S. Schindler, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Recovery Of The Baseline Incidence Density In Censored Time-To-Event Analysis, Mikel Aickin
Recovery Of The Baseline Incidence Density In Censored Time-To-Event Analysis, Mikel Aickin
COBRA Preprint Series
Abstract Time-to-event analyses are often concerned with the effects of explanatory factors on the underlying incidence density, but since there is no intrinsic interest in the form of the incidence density itself, a proportional hazards model is used. When part of the purpose of the analysis is to use actual cumulative incidence for simulation, or for providing informative visual displays of the results, an estimate of the baseline incidence density is required. The usual method for estimating the baseline hazards in Cox’s proportional hazards analysis yields values that are of little use, and furthermore no standard deviations of the estimates …
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data, Hajime Uno, Tianxi Cai, Lu Tian, L. J. Wei
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data, Hajime Uno, Tianxi Cai, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Collaborative Targeted Maximum Likelihood For Time To Event Data, Ori M. Stitelman, Mark J. Van Der Laan
Collaborative Targeted Maximum Likelihood For Time To Event Data, Ori M. Stitelman, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Current methods used to analyze time to event data either, rely on highly parametric assumptions which result in biased estimates of parameters which are purely chosen out of convenience, or are highly unstable because they ignore the global constraints of the true model. By using Targeted Maximum Likelihood Estimation one may consistently estimate parameters which directly answer the statistical question of interest. Targeted Maximum Likelihood Estimators are substitution estimators, which rely on estimating the underlying distribution. However, unlike other substitution estimators, the underlying distribution is estimated specifically to reduce bias in the estimate of the parameter of interest. We will …
A New Class Of Dantzig Selectors For Censored Linear Regression Models, Yi Li, Lee Dicker, Sihai Dave Zhao
A New Class Of Dantzig Selectors For Censored Linear Regression Models, Yi Li, Lee Dicker, Sihai Dave Zhao
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Joint Distribution Of Bivariate Exponential Under Linearly Related Model, Norou Diawara, Kumer Pial Das
The Joint Distribution Of Bivariate Exponential Under Linearly Related Model, Norou Diawara, Kumer Pial Das
Mathematics & Statistics Faculty Publications
In this paper, fundamental results of the joint distribution of the bivariate exponential distributions are established. The positive support multivariate distribution theory is important in reliability and survival analysis, and we applied it to the case where more than one failure or survival is observed in a given study. Usually, the multivariate distribution is restricted to those with marginal distributions of a specified and familiar lifetime family. The family of exponential distribution contains the absolutely continuous and discrete case models with a nonzero probability on a set of measure zero. Examples are given, and estimators are developed and applied to …
Linear Dependency For The Difference In Exponential Regression, Indika Sathish, Norou Diawara
Linear Dependency For The Difference In Exponential Regression, Indika Sathish, Norou Diawara
Mathematics & Statistics Faculty Publications
In the field of reliability, a lot has been written on the analysis of phenomena that are related. Estimation of the difference of two population means have been mostly formulated under the no-correlation assumption. However, in many situations, there is a correlation involved. This paper addresses this issue. A sequential estimation method for linearly related lifetime distributions is presented. Estimations for the scale parameters of the exponential distribution are given under square error loss using a sequential prediction method. Optimal stopping rules are discussed using concepts of mean criteria, and numerical results are presented.
The Effect Of Salvage Therapy On Survival In A Longitudinal Study With Treatment By Indication, Edward Kennedy, Jeremy Taylor, Douglas Schaubel, Scott Williams
The Effect Of Salvage Therapy On Survival In A Longitudinal Study With Treatment By Indication, Edward Kennedy, Jeremy Taylor, Douglas Schaubel, Scott Williams
Edward H. Kennedy
We consider using observational data to estimate the effect of a treatment on disease recurrence, when the decision to initiate treatment is based on longitudinal factors associated with the risk of recurrence. The effect of salvage androgen deprivation therapy (SADT) on the risk of recurrence of prostate cancer is inadequately described by the existing literature. Furthermore, standard Cox regression yields biased estimates of the effect of SADT, since it is necessary to adjust for prostate-specific antigen (PSA), which is a time-dependent confounder and an intermediate variable. In this paper, we describe and compare two methods which appropriately adjust for PSA …