Results 1 
6 of
6
Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance
 In Bayesian Statistics 5
, 1995
"... Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significanc ..."
Abstract

Cited by 39 (12 self)
 Add to MetaCart
Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. 1 Introduction From 1974 to 1984 the Mayo Clinic conducted a doubleblinded randomized clinical trial involving 312 patients to compare the drug DPCA with a placebo in the treatment of primary biliary cirrhosis...
A generalized F mixture model for cure rate estimation
 Statistics in Medicine
, 1998
"... Cure rate estimation is an important issue in clinical trials for diseases such as lymphoma and breast cancer and mixture models are the main statistical methods. In the last decade, mixture models under different distributions, such as exponential, Weibull, lognormal and Gompertz, have been discus ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Cure rate estimation is an important issue in clinical trials for diseases such as lymphoma and breast cancer and mixture models are the main statistical methods. In the last decade, mixture models under different distributions, such as exponential, Weibull, lognormal and Gompertz, have been discussed and used. However, these models involve stronger distributional assumptions than is desirable and inferences may not be robust to departures from these assumptions. In this paper, a mixture model is proposed using the generalized F distribution family. Although this family is seldom used because of computational difficulties, it has the advantage of being very flexible and including many commonly used distributions as special cases. The generalised F mixture model can relax the usual stronger distributional assumptions and allow the analyst to uncover structure in the data that might otherwise have been missed. This is illustrated by fitting the model to data from largescale clinical trials with long followup of lymphoma patients. Computational problems with the model and model selection methods are discussed. Comparison of maximum likelihood estimates with those obtained from mixture models under other distributions are included. � 1998 John Wiley & Sons, Ltd. 1.
N The Logistic and the Log F Distribution
"... As in Chapter 2, consider the version of the logistic distribution with pdf f ∗ (z) =e −z /(1 + e −z) 2 and cdf F ∗ (z) =1/(1 + e −z)on− ∞
Abstract

Cited by 1 (1 self)
 Add to MetaCart
As in Chapter 2, consider the version of the logistic distribution with pdf f ∗ (z) =e −z /(1 + e −z) 2 and cdf F ∗ (z) =1/(1 + e −z)on− ∞ <z<∞. Inserting these formulae into (2.1.1) yields f ∗ i:n(zi) =
Approved by: Advisor
, 1984
"... One step in the regulation of a potential carcinogen is the extrapolation of the dose response relationship obtained in test animals to levels approximating human exposures. Many of the existing statistical procedures for this low dose extrapolation do not utilize frequently available time to tumor ..."
Abstract
 Add to MetaCart
One step in the regulation of a potential carcinogen is the extrapolation of the dose response relationship obtained in test animals to levels approximating human exposures. Many of the existing statistical procedures for this low dose extrapolation do not utilize frequently available time to tumor and time to death information. Product hazard survival models are presented as one method for incorporating this information into risk estimation. All product hazard models used to date for carcinogenic risk assessment have chosen a polynomial for the dose factor in the hazard. Other parsimonious functional dose component forms, which generalize the pracedures ignoring time, are described. This generalization technique also allows the pharmacokinetics of the administered and delivered dose
DISCUSSION PAPER SERIES IZA DP No. 222 Betit: A Family That Nests Probit and Logit
, 2000
"... This Discussion Paper is issued within the framework of IZA’s research area *HQHUDO /DERU (FRQRPLFV Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy ..."
Abstract
 Add to MetaCart
This Discussion Paper is issued within the framework of IZA’s research area *HQHUDO /DERU (FRQRPLFV Any opinions expressed here are those of the author(s) and not those of the institute. Research disseminated by IZA may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent, nonprofit limited liability company (Gesellschaft mit beschränkter Haftung) supported by the Deutsche Post AG. The center is associated with the University of Bonn and offers a stimulating research environment through its research networks, research support, and visitors and doctoral programs. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. The current research program deals with (1) mobility and flexibility of labor markets, (2) internationalization of labor markets and European integration, (3) the welfare state and labor markets, (4) labor markets in transition, (5) the future of work, (6) project evaluation and (7) general labor economics. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. IZA Discussion Paper No. 222
oro.open.ac.uk On a Class of Distributions with Simple Exponential Tails
"... Journal Article How to cite: Jones, M. C. (2008). On a class of distributions with simple exponential tails. Statistica Sinica, 18(3), pp. 1101–1110. For guidance on citations see FAQs. ..."
Abstract
 Add to MetaCart
Journal Article How to cite: Jones, M. C. (2008). On a class of distributions with simple exponential tails. Statistica Sinica, 18(3), pp. 1101–1110. For guidance on citations see FAQs.